{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-baserow","slug":"baserow","name":"Baserow","type":"mcp","url":"https://baserow.io/user-docs/mcp-server","page_url":"https://unfragile.ai/baserow","categories":["mcp-servers"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-baserow__cap_0","uri":"capability://tool.use.integration.table.row.crud.operations.via.mcp","name":"table-row-crud-operations-via-mcp","description":"Enables read and write operations on Baserow table rows through MCP protocol, exposing individual row creation, retrieval, update, and deletion as discrete tool calls. Implements row-level mutations with field-value validation against the table's 20+ typed field definitions (text, number, select, date, links, etc.), returning structured row objects with metadata. Works by translating MCP tool invocations into Baserow's internal row storage layer, respecting workspace and table-level permissions defined in the hosting tier.","intents":["I need to create new records in a Baserow table from an AI agent without manual API calls","I want to update existing rows based on LLM-generated decisions or data transformations","I need to fetch specific rows or filter results to feed into downstream AI reasoning","I want to delete obsolete records programmatically as part of a data cleanup workflow"],"best_for":["Teams building AI agents that need persistent data storage in a no-code database","Developers prototyping data-driven workflows without managing their own database infrastructure","Non-technical users automating Baserow operations through Claude or other MCP-compatible LLMs"],"limitations":["MCP Server feature only available in Premium ($10/user/month) and Advanced ($18/user/month) tiers — not accessible in Free tier","Row limits per workspace vary by tier: Free=3,000, Premium=50,000, Advanced=250,000 rows","No built-in transaction support for multi-row atomic operations — each row mutation is independent","Field-level permissions (Advanced tier only) may restrict write access to specific columns even if table-level access is granted","Row change history retention varies: Free=14 days, Premium=90 days, Advanced=180 days"],"requires":["Baserow instance (self-hosted or cloud at baserow.io)","Premium or Advanced pricing tier with MCP Server feature enabled","Database token or API authentication credentials (generation method not documented in provided material)","MCP-compatible client (Claude, other LLM with MCP support)","Valid workspace and table IDs to target"],"input_types":["structured JSON object with field names and values matching table schema","row ID (UUID or numeric identifier format unknown)","filter/query parameters (format unknown)"],"output_types":["row object with all field values, metadata (created_at, updated_at, row_id)","success/failure status with error details","paginated result sets for bulk read operations"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-baserow__cap_1","uri":"capability://data.processing.analysis.field.type.aware.data.validation.and.transformation","name":"field-type-aware-data-validation-and-transformation","description":"Automatically validates and transforms input data against Baserow's 20+ typed field definitions (single-line text, long text, number, rating, boolean, date/time, URL, email, file, select, link-to-table, lookup, rollup, collaborator, count, duration, autonumber, UUID, password, etc.) before persisting rows. Implements field-specific coercion rules (e.g., converting ISO date strings to date fields, validating email format, enforcing select options) and returns validation errors with field-level details. Enables LLMs to understand table schema constraints and generate valid mutations without trial-and-error.","intents":["I want the AI to understand what data types each column accepts so it generates valid mutations","I need validation errors to be field-specific so the AI can correct mistakes and retry","I want to ensure email, URL, and phone fields are properly formatted before insertion","I need the AI to respect single-select and multi-select constraints when populating choice fields"],"best_for":["AI agents building data pipelines that must maintain data integrity across heterogeneous field types","Teams using LLMs to populate complex forms with linked records, lookups, and rollups","Developers who want schema-driven validation without writing custom type guards"],"limitations":["Field type definitions and validation rules must be queried separately — not automatically inferred from table schema in MCP tool discovery","Custom field types or user-defined validations (if supported by Baserow) are not documented in provided material","Lookup and rollup fields are read-only and cannot be directly written — mutations must target source tables","Link-to-table fields require valid row IDs in the linked table; no foreign-key constraint enforcement details documented","File field uploads may require separate file handling (multipart/form-data) — MCP row mutation format unknown"],"requires":["Schema introspection capability to fetch field definitions (tool name/format unknown)","Knowledge of all 20+ field type names and their validation rules","For linked records: valid row IDs in the target table","For select fields: knowledge of available options (must be queried separately)"],"input_types":["raw field values (strings, numbers, booleans, dates, arrays for multi-select)","field type metadata (name, type, constraints)"],"output_types":["validated field values in Baserow's internal format","validation error objects with field name, error code, and human-readable message","coerced values (e.g., string '2024-01-15' → date object)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-baserow__cap_10","uri":"capability://data.processing.analysis.select.field.option.enumeration.and.constraint.enforcement","name":"select-field-option-enumeration-and-constraint-enforcement","description":"Exposes single-select and multi-select field options as queryable enumerations through MCP, enabling LLMs to understand available choices and enforce constraints when populating select fields. Implements option enumeration by fetching the list of valid options for a select field and returning them with metadata (option ID, label, color). Validates mutations against the option list, rejecting invalid selections and returning constraint violation errors.","intents":["I want the AI to know what options are available for a select field before populating it","I need the AI to respect single-select constraints (only one option) vs multi-select (multiple options)","I want the AI to understand select field colors or metadata for UI rendering","I need the AI to reject invalid selections and retry with valid options"],"best_for":["Teams using select fields for categorical data and wanting AI agents to populate them correctly","Developers building workflows where select field constraints must be enforced","Users automating data entry with AI agents that respect field constraints"],"limitations":["Select field option metadata (color, icon, description) is not documented — assumed to include at least label and ID","No documented support for dynamic option creation through MCP","Option ordering (alphabetical, custom) is not documented","No documented support for option grouping or hierarchies","Multi-select field maximum option count is not documented","No documented support for conditional select options (options that depend on other field values)"],"requires":["Select field option enumeration MCP tool (name and format unknown)","Valid field ID or name","Read permission on the table"],"input_types":["field ID or name"],"output_types":["list of select options with metadata (ID, label, color)","field type (single-select vs multi-select)","constraint information (max options for multi-select)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-baserow__cap_11","uri":"capability://data.processing.analysis.date.time.field.handling.with.timezone.support","name":"date-time-field-handling-with-timezone-support","description":"Supports reading and writing date and date/time fields through MCP with timezone awareness, enabling LLMs to work with temporal data correctly. Implements date/time handling by accepting ISO 8601 formatted strings or date objects and converting them to Baserow's internal format, with timezone information preserved. Returns dates in ISO 8601 format with timezone metadata, enabling agents to reason about temporal relationships and schedule-based workflows.","intents":["I want the AI to create records with dates and times in the correct timezone","I need the AI to understand date constraints (e.g., deadline must be in the future)","I want the AI to reason about temporal relationships (e.g., which tasks are overdue)","I need the AI to handle daylight saving time transitions correctly"],"best_for":["Teams using Baserow for scheduling, project management, or time-sensitive workflows","Developers building AI agents that reason about temporal data and deadlines","Organizations with distributed teams across multiple timezones"],"limitations":["Timezone handling (whether stored as UTC or user-local) is not documented","Date format validation rules are not documented — assumed to accept ISO 8601","No documented support for recurring dates or date ranges","Duration field semantics (how durations are stored and calculated) are not documented","No documented support for date arithmetic or interval calculations through MCP","Daylight saving time handling is not documented"],"requires":["Date or date/time field defined in the target table","ISO 8601 formatted date string or date object","Optional: timezone information (format unknown)"],"input_types":["ISO 8601 date string (e.g., '2024-01-15')","ISO 8601 datetime string with timezone (e.g., '2024-01-15T14:30:00Z')","optional: timezone identifier (e.g., 'America/New_York')"],"output_types":["ISO 8601 formatted date or datetime string","timezone metadata","date field value in row responses"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-baserow__cap_12","uri":"capability://data.processing.analysis.rating.and.boolean.field.operations","name":"rating-and-boolean-field-operations","description":"Supports reading and writing rating (1-5 star) and boolean (true/false) fields through MCP, enabling LLMs to populate simple categorical fields and understand binary states. Implements rating fields as integer values (1-5) with validation, and boolean fields as true/false values. Returns typed values in row responses, enabling agents to reason about ratings and boolean states.","intents":["I want the AI to set a rating or satisfaction score based on feedback","I need the AI to toggle boolean flags (active/inactive, approved/rejected) based on conditions","I want the AI to understand and reason about boolean states in workflows","I need the AI to validate rating values (must be 1-5)"],"best_for":["Teams using ratings and boolean fields for simple categorical data","Developers building workflows with binary states or satisfaction ratings","Users automating simple field updates based on AI decisions"],"limitations":["Rating field range (1-5) is fixed — no documented support for custom ranges","No documented support for half-star ratings (e.g., 3.5)","Boolean field display format (checkbox, toggle, yes/no) is not documented","No documented support for nullable boolean fields (true/false/null)"],"requires":["Rating or boolean field defined in the target table","Valid value (1-5 for rating, true/false for boolean)"],"input_types":["integer 1-5 for rating fields","boolean true/false for boolean fields"],"output_types":["integer 1-5 for rating fields","boolean true/false for boolean fields","field value in row responses"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-baserow__cap_13","uri":"capability://data.processing.analysis.email.url.and.phone.field.format.validation","name":"email-url-and-phone-field-format-validation","description":"Validates and stores email, URL, and phone number fields through MCP with format-specific validation rules, enabling LLMs to populate contact fields correctly. Implements validation by checking email format (RFC 5322), URL format (valid protocol and domain), and phone number format (international or regional), rejecting invalid values with detailed error messages. Returns validated values in row responses, ensuring data quality for contact information.","intents":["I want the AI to validate email addresses before storing them","I need the AI to understand URL format constraints (must be valid HTTP/HTTPS URL)","I want the AI to validate phone numbers in different formats (international, regional)","I need the AI to reject invalid contact information and retry with corrected values"],"best_for":["Teams using Baserow for contact management and needing AI agents to populate contact fields","Developers building workflows that validate and store contact information","Organizations automating data entry with AI agents that enforce contact field constraints"],"limitations":["Email validation rules (RFC 5322 compliance, domain verification) are not documented","URL validation rules (allowed protocols, domain validation) are not documented","Phone number format validation (international vs regional, allowed formats) is not documented","No documented support for phone number normalization or formatting","No documented support for email verification or domain validation","No documented support for phone number country code detection"],"requires":["Email, URL, or phone number field defined in the target table","Valid email address, URL, or phone number string"],"input_types":["email string (e.g., 'user@example.com')","URL string (e.g., 'https://example.com')","phone number string (e.g., '+1-555-123-4567' or '555-123-4567')"],"output_types":["validated email, URL, or phone number string","validation error with specific reason (invalid format, etc.)","field value in row responses"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-baserow__cap_14","uri":"capability://safety.moderation.password.field.secure.storage","name":"password-field-secure-storage","description":"Supports writing to password fields through MCP with secure hashing and storage, enabling LLMs to set passwords or secrets in Baserow records. Implements password storage by accepting plaintext passwords and hashing them using Baserow's secure hashing algorithm before storage, with read access restricted to prevent plaintext exposure. Returns only a masked indicator on retrieval, preventing password leakage.","intents":["I want the AI to set a password or secret for a record securely","I need the AI to understand that password fields are write-only and cannot be read","I want to ensure passwords are hashed and not stored in plaintext","I need the AI to generate and store secure passwords"],"best_for":["Teams using Baserow for credential management and needing AI agents to set passwords securely","Developers building workflows that generate and store secrets","Organizations automating account creation with secure password assignment"],"limitations":["Password hashing algorithm is not documented — assumed to be bcrypt or similar","Password field read access is restricted — cannot retrieve plaintext or hash","No documented support for password reset or recovery workflows","No documented support for password strength validation or requirements","No documented support for password expiration or rotation policies","No documented support for password history or audit trail"],"requires":["Password field defined in the target table","Plaintext password string (will be hashed before storage)","Write permission on the password field"],"input_types":["plaintext password string"],"output_types":["success/failure status on write","masked indicator on read (e.g., '***' or 'password set')","no plaintext or hash returned"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-baserow__cap_15","uri":"capability://data.processing.analysis.long.text.field.rich.content.support","name":"long-text-field-rich-content-support","description":"Supports reading and writing long text fields through MCP with optional rich-text formatting (markdown, HTML), enabling LLMs to store and retrieve formatted content. Implements long text fields as plain text or rich-text content with optional formatting metadata, returning formatted content in row responses. Enables document-like content storage within Baserow records.","intents":["I want the AI to store long-form content (descriptions, notes) in a single field","I need the AI to understand and generate markdown or HTML-formatted content","I want to retrieve formatted content from long text fields for display or processing","I need the AI to handle line breaks and special characters correctly"],"best_for":["Teams using Baserow for content management and needing AI agents to generate or store long-form content","Developers building workflows that work with rich-text or markdown content","Users automating content creation with AI agents"],"limitations":["Rich-text format support (markdown, HTML, plain text) is not documented","Maximum field length is not documented — assumed to be large but not unlimited","No documented support for embedded media (images, videos) in long text fields","No documented support for text formatting preservation on retrieval","No documented support for full-text search within long text fields"],"requires":["Long text field defined in the target table","Text content (plain text or formatted)"],"input_types":["plain text string","optional: markdown or HTML formatted content"],"output_types":["plain text or formatted content string","field value in row responses"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-baserow__cap_2","uri":"capability://safety.moderation.workspace.and.table.permission.enforcement.in.mcp","name":"workspace-and-table-permission-enforcement-in-mcp","description":"Enforces Baserow's hierarchical permission model (workspace → database → table → field level) at the MCP tool invocation layer, restricting read/write access based on the authenticated user's role and tier. Implements role-based access control (RBAC) for workspace members and team workspaces, with field-level and view-level permissions available in Advanced tier. Prevents unauthorized mutations by validating credentials against Baserow's permission store before executing row operations, returning permission-denied errors for disallowed operations.","intents":["I want to ensure the AI agent can only access tables and fields that the authenticated user has permission to modify","I need to restrict certain team members from writing to sensitive fields while allowing reads","I want field-level permissions to prevent the AI from exposing or modifying confidential data","I need audit trails of who (via which API token) made changes to rows"],"best_for":["Teams with multi-user Baserow workspaces who need to delegate AI automation to specific users with limited permissions","Organizations requiring field-level access control for sensitive data (PII, financial records)","Enterprises needing audit logs of AI-driven mutations (Advanced tier only)"],"limitations":["Role-based permissions (RBAC) only available in Advanced tier ($18/user/month) — Premium tier has no role support","Field-level permissions (Advanced tier only) — Premium tier cannot restrict access to specific columns","View-level permissions are 'coming soon' and not yet available","Permission model is tied to Baserow user accounts — no service-account or API-key-only authentication documented","Audit logs (Advanced tier) do not specify whether they capture MCP-specific mutations or only UI changes","No documented rate limiting or quota enforcement per user/token at the MCP layer"],"requires":["Valid database token or API credentials linked to a Baserow user account","User must have at least table-level read or write permissions in the target workspace","For field-level restrictions: Advanced tier subscription","For audit logging: Advanced tier subscription"],"input_types":["authentication token/credentials","workspace, database, table, and field IDs","user role and permission metadata (queried from Baserow)"],"output_types":["permission-granted or permission-denied status","filtered row data (only fields the user can read)","error responses with permission-denied reason"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-baserow__cap_3","uri":"capability://memory.knowledge.schema.introspection.and.table.discovery","name":"schema-introspection-and-table-discovery","description":"Exposes Baserow table schemas (field names, types, constraints, linked tables) as queryable MCP resources, enabling LLMs to discover available tables, understand field definitions, and generate valid mutations without hardcoding schema details. Implements schema caching or on-demand introspection to retrieve field metadata (type, required, unique, linked-table references, select options) and returns structured schema objects. Allows agents to dynamically adapt to schema changes without code updates.","intents":["I want the AI to automatically discover what tables and fields exist in my workspace","I need the AI to understand field constraints (required, unique, linked records) before generating mutations","I want to query available options for select fields so the AI can populate them correctly","I need the AI to adapt when I add new fields to a table without redeploying the agent"],"best_for":["Teams building dynamic AI agents that work across multiple Baserow workspaces without hardcoded schema","Developers prototyping multi-table workflows where schema changes frequently","Non-technical users who want AI to adapt to their Baserow structure without code changes"],"limitations":["Schema introspection tool name, input/output format, and performance characteristics are unknown from provided documentation","No documented caching strategy — unclear if schema is fetched on every agent invocation or cached","Linked-table references may require recursive schema queries to fully understand relationships","Select field options must be queried separately — not included in field metadata by default (assumed)","View definitions and view-level filters are not documented as part of schema introspection","No documented support for schema versioning or change notifications"],"requires":["MCP tool for schema/table discovery (name and format unknown)","Valid workspace and database IDs","Read permission on tables to introspect their schemas"],"input_types":["workspace ID or database ID","optional: table name or ID filter"],"output_types":["list of tables with metadata (name, ID, row count, created_at)","field definitions (name, type, required, unique, linked-table reference, select options)","relationship metadata (which tables link to which)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-baserow__cap_4","uri":"capability://data.processing.analysis.linked.record.and.lookup.field.traversal","name":"linked-record-and-lookup-field-traversal","description":"Enables querying and traversing linked-record relationships (link-to-table fields) and computed fields (lookups, rollups) through MCP, allowing LLMs to fetch related rows and aggregated data without separate API calls. Implements relationship traversal by following link-to-table field references to fetch rows from linked tables, and read-only access to lookup and rollup fields that compute values from related records. Returns nested or flattened data structures depending on the query pattern.","intents":["I want the AI to fetch related records (e.g., all orders for a customer) by following link-to-table fields","I need the AI to read lookup fields that pull data from linked tables without making separate queries","I want the AI to understand rollup aggregations (sum, count, average) across related records","I need the AI to create links between records in different tables as part of a workflow"],"best_for":["Teams modeling relational data in Baserow and needing AI agents to navigate relationships","Developers building multi-table workflows where lookups and rollups provide computed context","Users who want AI to understand denormalized data (lookups) without manual joins"],"limitations":["Lookup and rollup fields are read-only — cannot be directly written; mutations must target source tables","Link-to-table field mutations require valid row IDs in the target table; no cascading delete or referential integrity constraints documented","Recursive relationship traversal (e.g., following links multiple levels deep) may have performance implications — no documented limits","Rollup aggregation functions supported are unknown — assumed to include sum, count, average, min, max but not documented","No documented support for filtering linked records (e.g., 'fetch only active orders') — must retrieve all and filter client-side","Circular references in linked tables may cause infinite traversal — no documented safeguards"],"requires":["Tables with link-to-table fields defined","Valid row IDs in both source and target tables","Read permission on both linked tables","For creating links: write permission on the link-to-table field"],"input_types":["source row ID and link-to-table field name","optional: filter/sort parameters (format unknown)"],"output_types":["list of linked row objects with all fields","lookup field values (computed from linked records)","rollup aggregation results (sum, count, average, etc.)","nested or flattened data structures"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-baserow__cap_5","uri":"capability://data.processing.analysis.bulk.row.operations.and.batch.mutations","name":"bulk-row-operations-and-batch-mutations","description":"Supports batch creation, update, or deletion of multiple rows in a single MCP invocation, reducing round-trip latency and enabling efficient bulk data operations. Implements batch processing by accepting arrays of row objects or mutations and executing them as a group, with optional transaction semantics or partial-failure handling. Returns success/failure status per row and aggregated results, enabling LLMs to perform data migrations or bulk corrections efficiently.","intents":["I want the AI to create 100+ records in one operation instead of making individual calls","I need to update multiple rows based on a batch transformation rule","I want to delete obsolete records in bulk as part of a cleanup workflow","I need efficient data migration from one table to another"],"best_for":["Teams performing bulk data operations (migrations, corrections, imports) via AI agents","Developers building ETL workflows that need to minimize API round-trips","Users importing large datasets through LLM-driven transformations"],"limitations":["Batch operation size limits are unknown — no documented maximum rows per batch","Transaction semantics (all-or-nothing vs. partial success) are not documented","Failure handling strategy unknown — unclear if failed rows are reported individually or as aggregate","No documented support for conditional batch operations (e.g., 'update only if field X matches Y')","Batch operations may be subject to rate limiting or timeout constraints not documented in provided material","Row change history may not capture batch operations as atomic — each row mutation may be logged separately"],"requires":["Batch operation MCP tool (name and format unknown)","Array of row objects or mutations","Write permission on target table","Valid field values for all rows in the batch"],"input_types":["array of row objects (create) or row ID + field updates (update) or row IDs (delete)","optional: transaction flag or failure handling strategy"],"output_types":["array of results with per-row success/failure status","created/updated row IDs","error details for failed rows","aggregated statistics (rows created, updated, deleted, failed)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-baserow__cap_6","uri":"capability://search.retrieval.view.based.filtering.and.query.composition","name":"view-based-filtering-and-query-composition","description":"Enables querying rows through Baserow's view definitions (Grid, Gallery, Form, Kanban, Calendar, Timeline), which encapsulate filters, sorts, and grouping rules. Implements view-based queries by applying the view's filter and sort configuration to row retrieval, allowing LLMs to fetch pre-filtered datasets without specifying filter logic manually. Returns rows in the order and grouping defined by the view, enabling context-aware data retrieval.","intents":["I want the AI to fetch rows using a pre-defined view filter (e.g., 'active customers') without hardcoding filter logic","I need the AI to respect view-level sorting and grouping when presenting data","I want to create views for different AI workflows (e.g., 'high-priority tasks') and query them by name","I need the AI to understand view-level permissions (coming soon) to restrict access to certain views"],"best_for":["Teams using Baserow views to organize data for different workflows and wanting AI agents to respect those views","Developers building context-aware AI agents that fetch data through pre-defined filters","Non-technical users who want AI to use their existing view structure without custom filter definitions"],"limitations":["View-level permissions are 'coming soon' and not yet available — no documented timeline","Filter composition syntax and supported operators are not documented in provided material","View-based queries may not support additional filtering beyond the view's definition — unclear if LLMs can add filters on top of view filters","Kanban, Calendar, and Timeline views may have special grouping/ordering semantics not documented","No documented support for dynamic view creation or modification through MCP","View change notifications or subscriptions are not documented"],"requires":["View-based query MCP tool (name and format unknown)","Valid view ID or name","Read permission on the view and its underlying table"],"input_types":["view ID or name","optional: additional filter parameters (format unknown)","optional: pagination parameters (limit, offset)"],"output_types":["rows filtered and sorted according to view definition","view metadata (name, type, filter rules, sort rules, grouping)","paginated results with total count"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-baserow__cap_7","uri":"capability://data.processing.analysis.file.field.attachment.handling","name":"file-field-attachment-handling","description":"Supports reading and writing file attachments in Baserow file fields through MCP, enabling LLMs to reference, upload, or retrieve files associated with rows. Implements file handling by accepting file references (URLs, file IDs, or base64-encoded content) and storing them in file fields, with metadata (filename, size, MIME type) returned on retrieval. Enables document processing workflows where LLMs can attach files to records or fetch file metadata for downstream processing.","intents":["I want the AI to attach files (documents, images) to rows as part of a workflow","I need the AI to fetch file metadata and URLs from file fields to process them downstream","I want to upload documents through the AI agent and link them to records","I need the AI to understand file field constraints (max file size, allowed MIME types)"],"best_for":["Teams building document-processing workflows where AI agents attach files to records","Developers creating knowledge management systems where files are linked to structured data","Users automating file uploads and organization through AI agents"],"limitations":["File upload mechanism (multipart/form-data, base64, URL reference) is not documented in provided material","Maximum file size per field is not documented — assumed to be constrained by workspace storage tier (Free=2GB, Premium=20GB, Advanced=100GB)","Allowed MIME types or file extensions are not documented","File versioning or change history is not documented","No documented support for file preview generation or thumbnail creation","File access control (whether file-level permissions exist) is not documented","Concurrent file uploads or streaming are not documented"],"requires":["File field defined in the target table","File content (URL, base64, or multipart upload)","Write permission on the file field","Sufficient workspace storage quota"],"input_types":["file content (URL, base64-encoded bytes, or multipart form data)","optional: filename, MIME type metadata"],"output_types":["file ID and metadata (filename, size, MIME type, upload timestamp)","file URL for retrieval","file field value (array of file objects if multi-file field)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-baserow__cap_8","uri":"capability://automation.workflow.collaborator.field.user.assignment","name":"collaborator-field-user-assignment","description":"Enables assigning Baserow workspace users to collaborator fields through MCP, allowing LLMs to assign tasks, responsibilities, or ownership to team members. Implements user assignment by accepting user IDs or email addresses and validating them against the workspace's user roster, returning user metadata (name, email, avatar) on retrieval. Enables workflow automation where LLMs assign work items to appropriate team members based on rules or context.","intents":["I want the AI to assign tasks to team members based on their expertise or availability","I need the AI to understand who is assigned to a record and route work accordingly","I want to automate task assignment based on rules (e.g., assign to the least-busy team member)","I need the AI to fetch user information (name, email) from collaborator fields"],"best_for":["Teams using Baserow for task/project management and wanting AI agents to automate assignments","Developers building workflow automation where work items are routed to team members","Organizations automating responsibility assignment based on AI-driven decisions"],"limitations":["User roster and availability information are not documented — unclear if LLMs can query user availability or load","User assignment validation (whether users must be workspace members) is not documented","No documented support for assigning external users or guest collaborators","Notification behavior when users are assigned (email, in-app) is not documented","No documented support for assignment history or audit trail","Multi-user assignment (if supported) semantics are not documented"],"requires":["Collaborator field defined in the target table","Valid user ID or email address of a workspace member","User must be a member of the workspace"],"input_types":["user ID or email address","optional: assignment metadata (role, responsibility type)"],"output_types":["user object with metadata (ID, name, email, avatar URL)","assignment confirmation with timestamp","list of users assigned to a collaborator field"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-baserow__cap_9","uri":"capability://data.processing.analysis.autonumber.and.uuid.field.generation","name":"autonumber-and-uuid-field-generation","description":"Supports reading auto-generated autonumber and UUID fields through MCP, enabling LLMs to understand and reference system-generated identifiers. Implements auto-generation by allowing the database to populate these fields on row creation, with LLMs reading the generated values after insertion. Returns generated IDs in row responses, enabling agents to reference newly created records in subsequent operations.","intents":["I want the AI to create a record and automatically get its generated ID for use in linked records","I need the AI to understand autonumber sequences for reporting or reference purposes","I want to use UUID fields for distributed record creation without ID collisions","I need the AI to reference generated IDs when creating related records"],"best_for":["Teams using autonumber or UUID fields for record identification and needing AI agents to reference them","Developers building workflows where generated IDs are used in downstream operations","Users automating record creation with automatic ID assignment"],"limitations":["Autonumber field configuration (starting value, increment) is not documented","UUID version (v4, v5, etc.) is not documented","No documented support for custom ID generation logic or sequences","Autonumber field values cannot be manually set — read-only after generation","No documented support for resetting or modifying autonumber sequences"],"requires":["Autonumber or UUID field defined in the target table","Row creation operation (auto-generation happens on insert)"],"input_types":["row object (autonumber/UUID fields omitted, auto-generated on insert)"],"output_types":["generated autonumber value (integer)","generated UUID value (string)","full row object with generated IDs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Baserow instance (self-hosted or cloud at baserow.io)","Premium or Advanced pricing tier with MCP Server feature enabled","Database token or API authentication credentials (generation method not documented in provided material)","MCP-compatible client (Claude, other LLM with MCP support)","Valid workspace and table IDs to target","Schema introspection capability to fetch field definitions (tool name/format unknown)","Knowledge of all 20+ field type names and their validation rules","For linked records: valid row IDs in the target table","For select fields: knowledge of available options (must be queried separately)","Select field option enumeration MCP tool (name and format unknown)"],"failure_modes":["MCP Server feature only available in Premium ($10/user/month) and Advanced ($18/user/month) tiers — not accessible in Free tier","Row limits per workspace vary by tier: Free=3,000, Premium=50,000, Advanced=250,000 rows","No built-in transaction support for multi-row atomic operations — each row mutation is independent","Field-level permissions (Advanced tier only) may restrict write access to specific columns even if table-level access is granted","Row change history retention varies: Free=14 days, Premium=90 days, Advanced=180 days","Field type definitions and validation rules must be queried separately — not automatically inferred from table schema in MCP tool discovery","Custom field types or user-defined validations (if supported by Baserow) are not documented in provided material","Lookup and rollup fields are read-only and cannot be directly written — mutations must target source tables","Link-to-table fields require valid row IDs in the linked table; no foreign-key constraint enforcement details documented","File field uploads may require separate file handling (multipart/form-data) — MCP row mutation format unknown","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=baserow","compare_url":"https://unfragile.ai/compare?artifact=baserow"}},"signature":"3GI8+7lw2YuQYVSW7SNuiEbUdUg65AMDbpOaITRB3AaQTQyT+XsdXSrFPNKxu8HTKVGZtmvNjuChwoCxcibEAA==","signedAt":"2026-06-21T07:20:16.672Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/baserow","artifact":"https://unfragile.ai/baserow","verify":"https://unfragile.ai/api/v1/verify?slug=baserow","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}