{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"sqlite-mcp-server","slug":"sqlite-mcp-server","name":"SQLite MCP Server","type":"mcp","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/sqlite","page_url":"https://unfragile.ai/sqlite-mcp-server","categories":["mcp-servers"],"tags":["sqlite","database","sql","official","reference"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"sqlite-mcp-server__cap_0","uri":"capability://tool.use.integration.mcp.compliant.sqlite.tool.exposure.via.json.rpc.protocol","name":"mcp-compliant sqlite tool exposure via json-rpc protocol","description":"Exposes SQLite database operations as MCP tools through a standardized JSON-RPC 2.0 transport layer, enabling LLM clients to discover and invoke database capabilities via the MCP protocol primitives. The server implements the MCP Tools interface, registering discrete database operations (query execution, schema inspection, table creation) as callable tools with JSON Schema definitions for input validation and type safety.","intents":["I want to give Claude or another LLM direct access to query my SQLite database without writing custom API endpoints","I need an LLM agent to explore database schemas and execute analytical queries autonomously","I want to standardize how my LLM application communicates with SQLite using the MCP protocol"],"best_for":["LLM application developers integrating local SQLite databases with Claude or other MCP-compatible clients","Teams building agentic workflows that require structured database access","Developers prototyping data analysis agents without building custom REST APIs"],"limitations":["Requires MCP-compatible client implementation — not compatible with standard REST or GraphQL clients","No built-in authentication or multi-user access control — assumes single-user or trusted environment","Tool discovery happens at server startup; dynamic tool registration not supported","JSON-RPC transport adds ~50-100ms latency per tool invocation compared to direct library calls"],"requires":["Node.js 18+ or Python 3.9+ (depending on SDK implementation)","MCP client library (e.g., @modelcontextprotocol/sdk for TypeScript)","Valid SQLite database file path accessible to the server process"],"input_types":["JSON-RPC method calls with tool parameters","SQL query strings","JSON Schema-validated tool arguments"],"output_types":["JSON-RPC responses with query results","Structured result sets (rows as JSON objects)","Schema metadata as JSON"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sqlite-mcp-server__cap_1","uri":"capability://data.processing.analysis.sql.query.execution.with.result.streaming.and.error.handling","name":"sql query execution with result streaming and error handling","description":"Executes arbitrary SQL queries against a SQLite database file and returns results as structured JSON objects, with built-in error handling for malformed SQL, constraint violations, and database locking. The implementation parses query results into row-based JSON format, enabling LLMs to reason over tabular data natively. Supports both SELECT queries (returning result sets) and DML operations (INSERT, UPDATE, DELETE) with transaction semantics.","intents":["I want to execute SELECT queries and get results back as JSON for LLM analysis","I need to perform data mutations (INSERT/UPDATE/DELETE) through the LLM interface with proper error reporting","I want the LLM to handle SQL errors gracefully and retry or reformulate queries"],"best_for":["Data analysis agents that need to query structured databases","LLM applications performing CRUD operations on SQLite tables","Developers building chatbots that answer questions by querying databases"],"limitations":["No query result pagination — large result sets (>10k rows) may exceed context window limits","No query optimization or cost estimation — LLM can execute expensive full-table scans","Synchronous execution only — long-running queries block the MCP server","No prepared statement support — vulnerable to SQL injection if LLM constructs queries unsafely"],"requires":["SQLite database file with read/write permissions","Valid SQL syntax (SQLite dialect)","Sufficient memory to hold entire result set in memory"],"input_types":["SQL query string (SELECT, INSERT, UPDATE, DELETE, CREATE TABLE, etc.)","Query parameters (if parameterized queries supported)"],"output_types":["JSON array of row objects for SELECT queries","Affected row count for DML operations","Error message string with SQLite error code"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sqlite-mcp-server__cap_10","uri":"capability://data.processing.analysis.join.query.execution.with.multi.table.result.sets","name":"join query execution with multi-table result sets","description":"Executes SELECT queries with JOIN clauses (INNER, LEFT, RIGHT, FULL OUTER) across multiple tables, returning flattened result sets with columns from all joined tables. The server handles SQLite's join semantics, including NULL propagation in outer joins and duplicate row handling. This enables LLM agents to correlate data across tables without understanding join syntax, by specifying tables and join conditions as parameters.","intents":["I want an LLM agent to correlate data across multiple related tables","I need to retrieve denormalized result sets that combine data from parent and child tables","I want the agent to reason about relationships between entities without writing join syntax"],"best_for":["Data analysis agents working with normalized schemas","Teams building LLM-powered data exploration tools","Developers implementing natural language interfaces to relational databases"],"limitations":["Result sets are flattened — no support for nested objects or arrays representing related records","Large joins can produce Cartesian products if join conditions are incorrect, causing memory exhaustion","No automatic join optimization — clients must specify join order and conditions explicitly","NULL values in join columns may produce unexpected results if join semantics are not understood"],"requires":["SELECT query with JOIN clauses and join conditions"],"input_types":["SQL SELECT query with JOIN clauses"],"output_types":["JSON array of flattened result rows with columns from all joined tables"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sqlite-mcp-server__cap_11","uri":"capability://data.processing.analysis.index.creation.and.query.optimization.hints","name":"index creation and query optimization hints","description":"Provides MCP tools to create indexes on table columns and retrieve query execution plans (EXPLAIN QUERY PLAN output) to help optimize slow queries. The server accepts index definitions (table, columns, uniqueness) and generates CREATE INDEX statements, then validates that indexes are created successfully. For query optimization, the server executes EXPLAIN QUERY PLAN and returns the execution plan in a structured format, allowing LLM agents to understand query performance and suggest index creation.","intents":["I want to optimize slow queries by creating indexes on frequently filtered columns","I need to understand query execution plans to debug performance issues","I want an LLM agent to suggest indexes based on query patterns"],"best_for":["Performance optimization agents that analyze and improve query efficiency","Teams building self-tuning database systems with AI assistance","Developers implementing query optimization tools with LLM guidance"],"limitations":["Index creation is synchronous and blocks the database during index build — no background indexing","EXPLAIN QUERY PLAN output is SQLite-specific and may not be intuitive for non-experts","No support for partial indexes or expression-based indexes — only simple column indexes","Index suggestions are not automatic — clients must analyze EXPLAIN output and request index creation"],"requires":["Write permissions on the SQLite database file","Understanding of query performance and index design"],"input_types":["Index definition: table name, column names, uniqueness flag","Query string for EXPLAIN QUERY PLAN analysis"],"output_types":["Success response confirming index creation","EXPLAIN QUERY PLAN output as structured text or parsed format"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sqlite-mcp-server__cap_2","uri":"capability://data.processing.analysis.database.schema.introspection.and.metadata.exposure","name":"database schema introspection and metadata exposure","description":"Provides tools to inspect SQLite database schema including table definitions, column types, constraints, indexes, and relationships. The implementation queries SQLite's built-in metadata tables (sqlite_master, PRAGMA table_info, PRAGMA foreign_key_list) and returns structured schema information as JSON, enabling LLMs to understand database structure before constructing queries. Supports discovery of all tables, views, and their column definitions with type information.","intents":["I want the LLM to understand the database schema before writing queries","I need to expose table and column metadata so the LLM can discover what data is available","I want to generate schema documentation or help the LLM understand relationships between tables"],"best_for":["LLM agents that need to explore unfamiliar databases before querying","Interactive SQL assistants that help users write queries","Automated documentation generation from database schemas"],"limitations":["Does not expose query execution statistics or table sizes — LLM cannot estimate query cost","Foreign key relationships shown but not enforced in schema introspection output","No support for custom metadata or comments on tables/columns","Schema changes require server restart to refresh cached metadata (if caching implemented)"],"requires":["SQLite database file with read access","SQLite version 3.0+ (for PRAGMA support)"],"input_types":["Optional table name filter (to describe specific table)","Optional schema scope parameter"],"output_types":["JSON object with tables array containing column definitions","Column metadata: name, type, nullable, primary key, default value","Index definitions and foreign key constraints"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sqlite-mcp-server__cap_3","uri":"capability://data.processing.analysis.table.creation.and.schema.definition.through.sql.ddl","name":"table creation and schema definition through sql ddl","description":"Allows LLMs to create new tables in the SQLite database by executing CREATE TABLE statements with full DDL support including column definitions, constraints (PRIMARY KEY, UNIQUE, NOT NULL, CHECK), and indexes. The implementation validates DDL syntax and enforces schema constraints before execution, preventing invalid table definitions. Supports both simple table creation and complex schemas with foreign keys and composite keys.","intents":["I want the LLM to create new tables dynamically based on data it needs to store","I need to set up database schemas programmatically without manual SQL","I want the LLM to define tables with proper constraints and indexes for data integrity"],"best_for":["Data pipeline agents that need to create staging tables or temporary storage","LLM applications that dynamically adapt their schema based on incoming data","Prototyping tools where schema evolution is frequent"],"limitations":["No schema migration or versioning — creating duplicate tables will fail","No rollback support — failed CREATE TABLE operations may leave partial state","Limited validation of constraint definitions — complex CHECK constraints may not be validated before execution","No support for ALTER TABLE — schema modifications require dropping and recreating tables"],"requires":["SQLite database file with write permissions","Valid DDL syntax (SQLite CREATE TABLE dialect)","Sufficient disk space for new table"],"input_types":["CREATE TABLE SQL statement with column definitions and constraints","Column names, types (INTEGER, TEXT, REAL, BLOB, etc.), and modifiers"],"output_types":["Success confirmation with table name","Error message if table already exists or DDL is invalid"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sqlite-mcp-server__cap_4","uri":"capability://data.processing.analysis.transactional.data.operations.with.acid.guarantees","name":"transactional data operations with acid guarantees","description":"Supports SQLite transactions (BEGIN, COMMIT, ROLLBACK) to ensure ACID properties for multi-step data operations. The implementation manages transaction state and allows LLMs to group multiple SQL operations into atomic units, rolling back all changes if any operation fails. Enables data consistency guarantees for complex workflows like data imports or multi-table updates.","intents":["I want the LLM to perform multiple database operations atomically so either all succeed or all fail","I need to ensure data consistency when the LLM is performing complex multi-step data transformations","I want to prevent partial updates if an error occurs mid-operation"],"best_for":["Data pipeline agents performing ETL with consistency requirements","Financial or accounting applications requiring ACID guarantees","LLM workflows that need to coordinate multiple database changes"],"limitations":["No nested transaction support (SQLite savepoints not exposed as MCP tools)","Long-running transactions may lock tables and block other operations","No transaction timeout — LLM could hold locks indefinitely","Implicit transaction handling may cause unexpected behavior if LLM doesn't explicitly commit"],"requires":["SQLite database file with write permissions","MCP client that maintains session state across multiple tool calls"],"input_types":["Transaction control commands: BEGIN, COMMIT, ROLLBACK","SQL statements to execute within transaction"],"output_types":["Transaction state confirmation","Rollback confirmation with affected row count"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sqlite-mcp-server__cap_5","uri":"capability://data.processing.analysis.data.analysis.and.aggregation.query.support","name":"data analysis and aggregation query support","description":"Enables LLMs to execute analytical SQL queries including GROUP BY, aggregation functions (COUNT, SUM, AVG, MIN, MAX), JOINs, and window functions to perform data analysis directly on SQLite. The implementation returns aggregated results as JSON, allowing LLMs to derive insights from structured data without exporting to external tools. Supports complex queries with subqueries and CTEs (Common Table Expressions).","intents":["I want the LLM to analyze data by running aggregation queries and interpreting results","I need to compute statistics (averages, totals, counts) across large datasets","I want the LLM to identify patterns or anomalies by grouping and filtering data"],"best_for":["Business intelligence agents that analyze operational data","Data exploration tools where LLMs help users understand datasets","Automated reporting systems that generate insights from databases"],"limitations":["No query optimization hints — LLM may construct inefficient queries with expensive JOINs","Large aggregation results may exceed context window limits","No support for window functions with PARTITION BY in some SQLite versions","Floating-point aggregations (AVG, STDDEV) may have precision issues"],"requires":["SQLite database with data to analyze","Valid SQL with GROUP BY, aggregate functions, or window functions"],"input_types":["SQL SELECT query with aggregation functions and GROUP BY clauses","JOIN conditions and WHERE filters"],"output_types":["JSON array of aggregated result rows","Grouped data with aggregate values (COUNT, SUM, AVG, etc.)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sqlite-mcp-server__cap_6","uri":"capability://tool.use.integration.mcp.resource.exposure.for.database.file.access","name":"mcp resource exposure for database file access","description":"Exposes SQLite database files as MCP resources, allowing LLM clients to read database file contents or metadata through the MCP Resources interface. The implementation registers database files with MIME types and optional descriptions, enabling clients to discover available databases and access them via the standardized resource protocol. Supports both direct file access and metadata-only exposure depending on security configuration.","intents":["I want LLM clients to discover which SQLite databases are available through MCP","I need to expose database file metadata (size, modification time) to clients","I want to provide read-only access to database files through the MCP resource interface"],"best_for":["Multi-database environments where LLM clients need to discover available databases","Systems exposing multiple SQLite files with different purposes (analytics, transactional, etc.)","Scenarios requiring resource-level access control separate from tool-level access"],"limitations":["Resource interface is read-only — cannot modify databases through resource protocol","No streaming support for large database files — full file must be loaded into memory","Resource discovery happens at server startup — new databases require server restart","No fine-grained access control per resource — all-or-nothing exposure"],"requires":["SQLite database files with read permissions","MCP client supporting the Resources interface"],"input_types":["Resource URI identifying the database file"],"output_types":["Database file contents (binary SQLite format) or metadata JSON","Resource list with database names and descriptions"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sqlite-mcp-server__cap_7","uri":"capability://safety.moderation.error.handling.and.validation.with.detailed.diagnostics","name":"error handling and validation with detailed diagnostics","description":"Implements comprehensive error handling for SQL syntax errors, constraint violations, type mismatches, and database locking conditions, returning detailed error messages and SQLite error codes to LLM clients. The implementation validates SQL before execution where possible and provides actionable error information enabling LLMs to correct queries or handle failures gracefully. Supports error recovery suggestions and constraint violation details.","intents":["I want the LLM to understand why a query failed and retry with corrections","I need detailed error messages that help the LLM debug SQL syntax or logic errors","I want the LLM to handle constraint violations gracefully (e.g., duplicate keys) and suggest alternatives"],"best_for":["Interactive SQL assistants where error feedback drives query refinement","Agentic systems that need to recover from database errors autonomously","Educational tools teaching SQL where error messages guide learning"],"limitations":["SQLite error messages are generic — may not pinpoint exact issue in complex queries","No query validation before execution — syntax errors only caught at runtime","Error recovery suggestions not provided — LLM must infer corrections from error codes","No logging of failed queries — difficult to debug patterns of errors"],"requires":["SQLite database with error reporting enabled","MCP client capable of parsing structured error responses"],"input_types":["SQL query that may contain errors","Invalid parameters or constraint violations"],"output_types":["Error message string with SQLite error code","Constraint violation details (column name, constraint type)","Syntax error location information if available"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sqlite-mcp-server__cap_8","uri":"capability://data.processing.analysis.multi.database.support.with.database.selection.and.switching","name":"multi-database support with database selection and switching","description":"Supports operations across multiple SQLite database files, allowing LLM clients to select or switch between databases for queries and operations. The implementation maintains database connections for multiple files and routes tool calls to the appropriate database based on context or explicit selection. Enables workflows that require data coordination across separate SQLite databases.","intents":["I want the LLM to query data from multiple SQLite databases in a single workflow","I need to perform operations on different databases based on data requirements","I want to coordinate data between separate SQLite files (e.g., staging and production)"],"best_for":["Multi-tenant systems where each tenant has a separate SQLite database","Data pipeline systems that stage data in one database and load to another","Analytical systems that combine data from multiple operational databases"],"limitations":["No cross-database JOINs — cannot query across multiple databases in a single SQL statement","Database selection must be explicit in each tool call — no implicit context switching","No transaction support across databases — each database has independent transactions","Connection pooling not exposed — each database connection consumes resources"],"requires":["Multiple SQLite database files with appropriate permissions","MCP client that can track and specify database context"],"input_types":["Database identifier or file path","SQL query to execute on selected database"],"output_types":["Query results from specified database","List of available databases"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sqlite-mcp-server__cap_9","uri":"capability://data.processing.analysis.aggregate.function.support.with.group.by.and.having.clause.execution","name":"aggregate function support with group by and having clause execution","description":"Executes SELECT queries with aggregate functions (COUNT, SUM, AVG, MIN, MAX) and GROUP BY/HAVING clauses, returning grouped results with aggregate values. The server handles SQLite's aggregate semantics, including NULL handling, type coercion in aggregates, and HAVING clause filtering. This enables LLM agents to perform analytical queries without understanding SQL aggregate syntax, by specifying grouping columns and aggregate functions as parameters.","intents":["I want an LLM agent to compute aggregates (sum, count, average) over grouped data","I need to filter groups based on aggregate values (e.g., groups with count > 10)","I want the agent to analyze data distributions and patterns without writing complex SQL"],"best_for":["Data analysis agents that need to compute summaries and distributions","Teams building LLM-powered business intelligence dashboards","Developers implementing natural language analytics interfaces"],"limitations":["Aggregate functions are limited to SQLite's built-in set (COUNT, SUM, AVG, MIN, MAX, GROUP_CONCAT); no custom aggregates","GROUP BY results are not sorted by default — clients must specify ORDER BY to control result order","HAVING clause filtering is applied after aggregation, which may be inefficient for large datasets","NULL values in GROUP BY columns are treated as a single group, which may not match application semantics"],"requires":["SELECT query with aggregate functions and GROUP BY clause"],"input_types":["SQL SELECT query with GROUP BY and aggregate functions"],"output_types":["JSON array of grouped results with aggregate values"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"sqlite-mcp-server__headline","uri":"capability://data.processing.analysis.mcp.server.for.sqlite.database.operations","name":"mcp server for sqlite database operations","description":"An official server designed for managing SQLite databases, offering tools for creating tables, executing queries, and performing data analysis, making it ideal for developers working with local SQLite files.","intents":["best SQLite MCP server","MCP server for SQLite database management","SQLite server for data analysis","SQLite database operations server","how to use MCP with SQLite"],"best_for":["developers needing SQLite database management","users looking for educational reference servers"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":75,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+ or Python 3.9+ (depending on SDK implementation)","MCP client library (e.g., @modelcontextprotocol/sdk for TypeScript)","Valid SQLite database file path accessible to the server process","SQLite database file with read/write permissions","Valid SQL syntax (SQLite dialect)","Sufficient memory to hold entire result set in memory","SELECT query with JOIN clauses and join conditions","Write permissions on the SQLite database file","Understanding of query performance and index design","SQLite database file with read access"],"failure_modes":["Requires MCP-compatible client implementation — not compatible with standard REST or GraphQL clients","No built-in authentication or multi-user access control — assumes single-user or trusted environment","Tool discovery happens at server startup; dynamic tool registration not supported","JSON-RPC transport adds ~50-100ms latency per tool invocation compared to direct library calls","No query result pagination — large result sets (>10k rows) may exceed context window limits","No query optimization or cost estimation — LLM can execute expensive full-table scans","Synchronous execution only — long-running queries block the MCP server","No prepared statement support — vulnerable to SQL injection if LLM constructs queries unsafely","Result sets are flattened — no support for nested objects or arrays representing related records","Large joins can produce Cartesian products if join conditions are incorrect, causing memory exhaustion","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.55,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:05.296Z","last_scraped_at":null,"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=sqlite-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=sqlite-mcp-server"}},"signature":"9CdMC5xjHGWcpbc/V+NYLuMI/rD9HSnNNJuCIVhP9AU4zjUBJ3U+H68Hs30j6vuDvVOJYVrH/D5VqZIY0pHpDQ==","signedAt":"2026-06-23T20:35:21.679Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sqlite-mcp-server","artifact":"https://unfragile.ai/sqlite-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=sqlite-mcp-server","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"}}