{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-mindsdb--mindsdb","slug":"mindsdb--mindsdb","name":"mindsdb","type":"mcp","url":"https://mindsdb.com","page_url":"https://unfragile.ai/mindsdb--mindsdb","categories":["mcp-servers"],"tags":["agents","ai","analytics","artificial-inteligence","bigquery","business-intelligence","databases","hacktoberfest","llms","mcp","mssql","mysql","postgresql","rag"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-mindsdb--mindsdb__cap_0","uri":"capability://data.processing.analysis.sql.based.federated.query.execution.across.200.heterogeneous.data.sources","name":"sql-based federated query execution across 200+ heterogeneous data sources","description":"MindsDB exposes a MySQL-compatible protocol (port 47335) and HTTP REST API that translates standard SQL queries into source-specific API calls and database commands. The IntegrationController manages 200+ data source handlers (PostgreSQL, MySQL, BigQuery, MongoDB, Salesforce, GitLab, etc.), each implementing a unified interface that abstracts connection pooling, authentication, and query translation. Queries are parsed into an AST, optimized, and dispatched to appropriate handlers with result aggregation and schema inference happening transparently.","intents":["Query live data from multiple databases and APIs using a single SQL interface without ETL","Build agents that can access real-time data from Salesforce, MongoDB, PostgreSQL, and BigQuery in one query","Avoid writing custom API clients by querying REST APIs as if they were SQL tables"],"best_for":["Teams building AI agents that need access to live data across multiple systems","Data engineers replacing custom ETL with declarative SQL federation","Developers prototyping multi-source analytics without infrastructure overhead"],"limitations":["Query optimization across heterogeneous sources is limited — complex joins across different source types may require client-side filtering","API rate limits from source systems are not automatically managed — developers must implement backoff logic","Schema inference for unstructured APIs (e.g., REST endpoints) may require manual schema definition"],"requires":["Python 3.10+","Valid credentials for each data source (API keys, connection strings, OAuth tokens)","Network access to target data sources","MySQL client or HTTP client library for query submission"],"input_types":["SQL SELECT/INSERT/UPDATE/DELETE statements","Connection parameters (host, port, credentials, API keys)"],"output_types":["Structured result sets (rows/columns)","JSON (via HTTP API)","Native MySQL protocol response"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mindsdb--mindsdb__cap_1","uri":"capability://memory.knowledge.dynamic.knowledge.base.construction.with.semantic.search.over.heterogeneous.data","name":"dynamic knowledge base construction with semantic search over heterogeneous data","description":"MindsDB's KNOWLEDGE_BASE abstraction fuses structured tables (from databases) with vectorized unstructured data (PDFs, HTML, text) by storing embeddings in integrated vector databases (Pinecone, Weaviate, Milvus, etc.). The RAG pipeline implementation accepts documents, chunks them, generates embeddings via configured LLM providers, and stores them alongside metadata. Queries trigger semantic search to retrieve relevant context, which is then joined with structured data and passed to reasoning agents. The knowledge base is queryable via SQL, enabling agents to perform hybrid structured-semantic searches.","intents":["Build RAG systems that combine semantic search over documents with live database queries","Create agents that can retrieve relevant context from PDFs and databases in a single query","Index unstructured data (documentation, emails, PDFs) and make it queryable alongside structured data"],"best_for":["Teams building document-aware AI agents (customer support, research assistants)","Organizations needing to combine proprietary documents with live CRM/database data","Developers implementing RAG without managing separate vector database infrastructure"],"limitations":["Chunking strategy is fixed (no custom chunking logic exposed) — may not be optimal for domain-specific documents","Embedding model selection is limited to configured LLM providers — no fine-tuned embedding models","Vector database selection is static per knowledge base — cannot dynamically switch backends","No built-in deduplication or update semantics — re-indexing requires manual deletion and re-upload"],"requires":["Vector database integration (Pinecone, Weaviate, Milvus, or similar) with API credentials","LLM provider for embedding generation (OpenAI, Anthropic, HuggingFace, etc.)","Document source (file uploads, URLs, or database tables)","Python 3.10+"],"input_types":["PDF files","HTML/text documents","Database table rows","URLs pointing to web content"],"output_types":["Structured result sets with relevance scores","Joined results combining semantic matches with structured data","Metadata and source attribution"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mindsdb--mindsdb__cap_10","uri":"capability://automation.workflow.project.based.workspace.isolation.and.multi.tenancy","name":"project-based workspace isolation and multi-tenancy","description":"MindsDB organizes resources (models, agents, knowledge bases, data connections) into Projects, providing logical isolation and multi-tenancy. Each project has its own namespace, allowing teams to organize work by domain, customer, or application. Projects are queryable via REST API and SQL, enabling programmatic project management. Access control is enforced at the project level, supporting role-based permissions for team collaboration.","intents":["Organize MindsDB resources (models, agents) by team, customer, or application domain","Enable multi-tenant deployments where each customer has isolated projects","Manage access control and permissions at the project level for team collaboration"],"best_for":["Teams organizing work across multiple domains or customers","SaaS platforms using MindsDB as a backend with per-customer isolation","Organizations needing fine-grained access control and resource organization"],"limitations":["Cross-project queries are not supported — data must be copied or federated manually","Project-level quotas (storage, compute) are not enforced","Role-based access control is basic — no fine-grained permissions per resource type","Project deletion cascades to all resources — no soft delete or archival"],"requires":["Project name and configuration","User authentication and authorization","Python 3.10+"],"input_types":["Project creation parameters (name, description)","Resource definitions (models, agents, knowledge bases)"],"output_types":["Project metadata","Resource listings","Access control configurations"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mindsdb--mindsdb__cap_11","uri":"capability://automation.workflow.scheduled.job.execution.and.automation.workflows","name":"scheduled job execution and automation workflows","description":"MindsDB supports scheduled job execution via cron-like syntax, enabling automated workflows such as periodic model retraining, data synchronization, and report generation. Jobs are defined via SQL (CREATE JOB) or REST API, specifying a query to execute and a schedule. The job scheduler manages execution, handles failures with retry logic, and logs execution history. Jobs can trigger agents, execute SQL queries, or invoke external webhooks, enabling complex automation without external orchestration tools.","intents":["Automatically retrain models on new data on a schedule","Periodically synchronize data between MindsDB and external systems","Generate and send reports on a schedule without manual intervention"],"best_for":["Teams automating routine data tasks (model retraining, data sync)","Organizations generating periodic reports or alerts","Developers building event-driven workflows without external schedulers"],"limitations":["Cron syntax is limited — complex scheduling logic requires multiple jobs","Job execution is not distributed — all jobs run on a single MindsDB instance","Failure handling is basic — no conditional logic or branching on job failure","Job history is not queryable — no built-in analytics on job execution patterns","No support for job dependencies — jobs cannot wait for other jobs to complete"],"requires":["Cron expression for scheduling","SQL query or agent invocation to execute","Python 3.10+","MindsDB running continuously for job execution"],"input_types":["SQL CREATE JOB statement with cron schedule","Query or agent invocation to execute"],"output_types":["Job execution logs","Query results from job execution","Success/failure status"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mindsdb--mindsdb__cap_12","uri":"capability://memory.knowledge.vector.database.integration.for.embeddings.and.semantic.search","name":"vector database integration for embeddings and semantic search","description":"MindsDB integrates with multiple vector databases (Pinecone, Weaviate, Milvus, Qdrant, Chroma) to store and query embeddings. The integration abstracts vector database APIs, enabling semantic search queries via SQL (e.g., 'SELECT * FROM vector_db WHERE semantic_search(query)'). Embeddings are generated via configured LLM providers and stored alongside metadata, enabling hybrid queries that combine semantic similarity with structured filters. The vector database handler manages connection pooling, index management, and result ranking.","intents":["Store embeddings of documents or data in a vector database for semantic search","Query embeddings via SQL without learning vector database-specific APIs","Build hybrid search systems combining semantic similarity with structured filters"],"best_for":["Teams building semantic search systems without learning vector database APIs","Organizations combining embeddings with structured data for hybrid search","Developers integrating vector databases into MindsDB workflows"],"limitations":["Vector database selection is static per integration — cannot dynamically switch backends","Embedding model is fixed per integration — cannot use different models for different data","Index management is manual — no automatic index optimization or tuning","Batch operations are not optimized — inserting large numbers of embeddings may be slow","No built-in deduplication or update semantics for embeddings"],"requires":["Vector database account and API credentials (Pinecone, Weaviate, etc.)","LLM provider for embedding generation","Python 3.10+"],"input_types":["Text or documents to embed","Semantic search queries","Metadata for filtering"],"output_types":["Embeddings (vector arrays)","Search results with relevance scores","Metadata and source attribution"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mindsdb--mindsdb__cap_13","uri":"capability://tool.use.integration.llm.provider.abstraction.with.multi.provider.support","name":"llm provider abstraction with multi-provider support","description":"MindsDB abstracts LLM provider APIs (OpenAI, Anthropic, Ollama, HuggingFace, Vertex AI, etc.) behind a unified interface, enabling agents and models to use any provider without code changes. Providers are configured via REST API or SQL, with credentials stored securely. The LLM handler manages API calls, token counting, rate limiting, and fallback logic. Agents can specify a preferred provider, and MindsDB automatically routes requests to the appropriate API.","intents":["Use multiple LLM providers (OpenAI, Anthropic, Ollama) interchangeably in agents","Switch LLM providers without changing agent code","Implement fallback logic to use alternative providers if primary provider fails"],"best_for":["Teams wanting to avoid vendor lock-in with a single LLM provider","Organizations using multiple LLM providers for cost optimization or redundancy","Developers building LLM applications that need provider flexibility"],"limitations":["Provider-specific features (vision, function calling) are not uniformly supported — some providers may not support all features","Token counting is approximate — actual token usage may differ from estimates","Rate limiting is not coordinated across providers — each provider's limits are enforced independently","Cost tracking is manual — no built-in cost attribution per provider or agent","Model selection is manual — no automatic model selection based on task or cost"],"requires":["API keys for configured LLM providers","Provider configuration (model name, API endpoint, etc.)","Python 3.10+"],"input_types":["LLM provider configuration","Prompts or function-calling requests","Model parameters (temperature, max_tokens, etc.)"],"output_types":["LLM responses (text, function calls)","Token usage statistics","Cost estimates"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mindsdb--mindsdb__cap_2","uri":"capability://planning.reasoning.autonomous.agent.execution.with.skill.based.tool.orchestration","name":"autonomous agent execution with skill-based tool orchestration","description":"MindsDB's AI Agents System implements a skill-based architecture where agents are composed of reusable tools (skills) that map to database queries, API calls, or LLM invocations. The agent execution engine uses a planning-reasoning loop: the LLM decomposes user intent into subtasks, selects appropriate skills, executes them via the unified SQL/API layer, and iterates until the goal is achieved. Skills are registered in a schema-based function registry, enabling multi-provider LLM support (OpenAI, Anthropic, Ollama) with native function-calling APIs. Agent state is managed via a session context that persists across reasoning steps.","intents":["Deploy autonomous agents that can query databases, call APIs, and reason about results without human intervention","Build multi-step workflows where agents decompose complex tasks into database queries and API calls","Create agents that learn from execution history and adapt their skill selection over time"],"best_for":["Teams building autonomous data analysis agents (business intelligence, analytics)","Developers creating customer-facing chatbots that need to access live data","Organizations automating multi-step business processes (data validation, reporting, alerting)"],"limitations":["Agent reasoning is stateless across sessions — no persistent learning or fine-tuning of skill selection","Skill composition is manual — no automatic skill discovery or dynamic skill generation","Error recovery is limited — failed skill executions may not trigger fallback strategies","Reasoning latency scales with number of skills and reasoning steps — no built-in caching of intermediate results","Multi-agent coordination is not supported — agents cannot delegate to other agents"],"requires":["LLM provider API key (OpenAI, Anthropic, Ollama, or compatible)","Skill definitions (SQL queries, API endpoints, or custom Python functions)","Access to underlying data sources (databases, APIs)","Python 3.10+","HTTP client or MySQL client for agent communication"],"input_types":["Natural language user intent","Skill definitions (schema-based function signatures)","Context data (previous query results, session state)"],"output_types":["Structured results from skill execution","Natural language responses","Execution logs and reasoning traces"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mindsdb--mindsdb__cap_3","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.for.ai.agent.integration","name":"model context protocol (mcp) server for ai agent integration","description":"MindsDB exposes an MCP server interface that allows external AI agents (Claude, other LLM-based systems) to interact with MindsDB capabilities as MCP resources and tools. The MCP implementation translates agent requests into MindsDB operations: querying data sources, executing agents, managing knowledge bases, and retrieving results. This enables Claude and other MCP-compatible agents to treat MindsDB as a data and reasoning backend without direct API calls, using the standardized MCP protocol for resource discovery and tool invocation.","intents":["Integrate MindsDB as a data backend for Claude or other MCP-compatible AI agents","Allow external agents to query databases and execute reasoning workflows via MCP protocol","Build multi-agent systems where Claude can delegate data access to MindsDB agents"],"best_for":["Teams using Claude or other MCP-compatible LLMs who need data access","Developers building agent ecosystems where MindsDB serves as a shared data layer","Organizations standardizing on MCP for agent-to-system integration"],"limitations":["MCP protocol overhead adds latency compared to direct HTTP/MySQL API calls","Resource discovery is static — agents cannot dynamically discover new data sources or skills","Authentication is delegated to MCP client — MindsDB cannot enforce fine-grained access control per agent","Streaming results are not supported — large result sets must be buffered and returned atomically"],"requires":["MCP-compatible AI agent (Claude, etc.)","MindsDB MCP server running (Python 3.10+)","Network connectivity between agent and MCP server","MCP client library in agent framework"],"input_types":["MCP resource requests (list, read)","MCP tool invocations (function calls with parameters)"],"output_types":["MCP resource representations (JSON)","Tool execution results (structured data, text)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mindsdb--mindsdb__cap_4","uri":"capability://planning.reasoning.agent.to.agent.a2a.protocol.for.multi.agent.coordination","name":"agent-to-agent (a2a) protocol for multi-agent coordination","description":"MindsDB implements an Agent-to-Agent protocol that enables autonomous agents to communicate, delegate tasks, and share context. Agents can invoke other agents via the A2A protocol, passing structured parameters and receiving results, enabling hierarchical task decomposition and specialization. The protocol manages agent discovery, request routing, and result aggregation, allowing complex workflows where agents collaborate to solve problems that require multiple specialized skills or data sources.","intents":["Build multi-agent systems where specialized agents collaborate on complex tasks","Enable agents to delegate subtasks to other agents with different expertise or data access","Create hierarchical agent architectures with coordinator and worker agents"],"best_for":["Teams building complex autonomous systems with multiple specialized agents","Organizations needing agent-based task distribution and load balancing","Developers implementing hierarchical reasoning where agents delegate to sub-agents"],"limitations":["Agent discovery is manual — no automatic service registration or health checking","Deadlock detection is not implemented — circular agent dependencies can cause hangs","Result caching across agent calls is not supported — redundant agent invocations are not deduplicated","Timeout and retry logic must be implemented by calling agents","No built-in audit trail for multi-agent interactions"],"requires":["Multiple MindsDB agents deployed and accessible","A2A protocol endpoint configuration","Network connectivity between agents","Python 3.10+"],"input_types":["Agent invocation requests (agent name, parameters)","Context data (previous results, session state)"],"output_types":["Agent execution results","Structured responses from delegated agents"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mindsdb--mindsdb__cap_5","uri":"capability://data.processing.analysis.sql.based.model.training.and.prediction.with.automl","name":"sql-based model training and prediction with automl","description":"MindsDB exposes ML model training and inference through SQL commands (CREATE MODEL, SELECT ... FROM model_name), abstracting the complexity of feature engineering, hyperparameter tuning, and model selection. The system integrates with Lightwood (MindsDB's AutoML engine) and external ML frameworks (scikit-learn, TensorFlow, XGBoost via handlers). Training queries automatically handle data preprocessing, feature engineering, train/test splitting, and model evaluation. Predictions are executed via SELECT queries that join input data with the trained model, returning predictions with confidence scores.","intents":["Train predictive models on database tables using SQL without writing Python ML code","Deploy models that automatically retrain on new data without manual intervention","Make predictions on new data by querying trained models as if they were database tables"],"best_for":["Data analysts building predictive models without ML expertise","Teams automating model training pipelines using SQL","Organizations deploying models that need to adapt to new data automatically"],"limitations":["Feature engineering is automatic but not customizable — domain-specific features must be pre-computed in source tables","Model interpretability is limited — no built-in SHAP or LIME explanations","Hyperparameter tuning is automatic but not exposed for manual control","Large datasets (>1GB) may require external ML handlers — Lightwood is optimized for smaller datasets","Time series models have limited support for complex seasonality patterns"],"requires":["Training data in a queryable source (database table, CSV, API)","Target column (label) for supervised learning","Python 3.10+","Sufficient memory for model training (depends on dataset size)"],"input_types":["SQL CREATE MODEL statement with training data source","SQL SELECT statement for predictions with input features"],"output_types":["Trained model metadata (accuracy, feature importance)","Predictions with confidence scores","Model evaluation metrics"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mindsdb--mindsdb__cap_6","uri":"capability://data.processing.analysis.bring.your.own.model.byom.integration.for.custom.ml.frameworks","name":"bring your own model (byom) integration for custom ml frameworks","description":"MindsDB's BYOM system allows developers to register custom ML models (trained in scikit-learn, TensorFlow, PyTorch, etc.) as queryable MindsDB models. Models are packaged as Python classes implementing a standard interface (fit, predict methods), uploaded to MindsDB, and then invoked via SQL SELECT queries. The system handles model versioning, serialization, and execution in isolated Python environments, enabling teams to deploy proprietary or domain-specific models without rewriting them for MindsDB's AutoML engine.","intents":["Deploy custom ML models trained in scikit-learn or TensorFlow as queryable MindsDB models","Integrate proprietary or domain-specific models without rewriting for MindsDB's AutoML","Version and manage multiple model variants in a single MindsDB instance"],"best_for":["Teams with existing ML models who want to integrate them into MindsDB","Data scientists deploying custom models without rewriting for AutoML","Organizations needing to manage multiple model versions and variants"],"limitations":["Model interface is rigid — custom models must implement fit/predict methods","Serialization is pickle-based — not suitable for models with external dependencies or non-serializable state","Execution environment is isolated — models cannot access external services or files at inference time","No automatic model monitoring or drift detection","Model size is limited by available memory — large models (>1GB) may not be practical"],"requires":["Custom ML model as Python class with fit/predict methods","Model dependencies specified (scikit-learn, TensorFlow, etc.)","Python 3.10+","Model serialization via pickle or joblib"],"input_types":["Python class implementing model interface","Training data (for fit method)","Inference data (for predict method)"],"output_types":["Model predictions","Confidence scores or probabilities"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mindsdb--mindsdb__cap_7","uri":"capability://data.processing.analysis.huggingface.model.integration.for.nlp.and.vision.tasks","name":"huggingface model integration for nlp and vision tasks","description":"MindsDB integrates with HuggingFace's model hub, allowing developers to deploy pre-trained NLP and vision models (text classification, summarization, object detection, etc.) as queryable MindsDB models. The integration handles model downloading, caching, tokenization, and inference through a unified SQL interface. Models are invoked via SELECT queries, with input text or images passed as query parameters and results returned as structured data (classifications, summaries, detections).","intents":["Deploy HuggingFace NLP models (sentiment analysis, NER, summarization) without custom code","Run vision models (object detection, image classification) on data in MindsDB","Build text processing pipelines that combine HuggingFace models with database queries"],"best_for":["Teams using HuggingFace models who want to integrate them into MindsDB workflows","Data engineers building NLP pipelines without Python ML expertise","Organizations processing text or images stored in databases"],"limitations":["Model selection is limited to HuggingFace hub — custom or fine-tuned models must be uploaded to HuggingFace first","Batch inference is not optimized — processing large datasets may be slow","GPU support depends on MindsDB deployment — CPU-only deployments will be slow for vision models","Model caching is local — distributed MindsDB instances may duplicate model downloads","No built-in model quantization or optimization for inference speed"],"requires":["HuggingFace model identifier (e.g., 'bert-base-uncased')","HuggingFace API token for private models","Sufficient disk space for model caching","Python 3.10+","GPU recommended for vision models"],"input_types":["Text (for NLP models)","Images (for vision models)","Model parameters (temperature, max_length, etc.)"],"output_types":["Classification labels with confidence scores","Generated text (summaries, translations)","Structured detections (bounding boxes, class labels)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mindsdb--mindsdb__cap_8","uri":"capability://tool.use.integration.http.rest.api.for.programmatic.mindsdb.access","name":"http rest api for programmatic mindsdb access","description":"MindsDB exposes a comprehensive HTTP REST API (port 47334) that enables programmatic access to all core capabilities: executing SQL queries, managing projects, creating/training models, deploying agents, and managing knowledge bases. The API follows RESTful conventions with JSON request/response bodies, supports authentication via API keys, and provides OpenAPI documentation for client generation. The API is the primary integration point for external applications, dashboards, and automation tools.","intents":["Build applications that query MindsDB without using SQL or MySQL protocol","Integrate MindsDB into existing REST-based architectures and microservices","Automate MindsDB operations (model training, agent deployment) via CI/CD pipelines"],"best_for":["Web application developers integrating MindsDB as a backend service","DevOps teams automating MindsDB operations via REST","Teams building custom dashboards or analytics UIs on top of MindsDB"],"limitations":["API rate limiting is not enforced — high-frequency requests may impact MindsDB performance","Streaming responses are not supported — large result sets must be paginated","Authentication is API-key based — no fine-grained role-based access control","API versioning is not explicit — breaking changes may occur in minor releases"],"requires":["HTTP client library (curl, requests, axios, etc.)","MindsDB API key for authentication","Network access to MindsDB HTTP endpoint (port 47334)","JSON serialization/deserialization capability"],"input_types":["JSON request bodies","URL path parameters","Query string parameters"],"output_types":["JSON response bodies","HTTP status codes","Error messages with details"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mindsdb--mindsdb__cap_9","uri":"capability://tool.use.integration.mysql.protocol.compatibility.for.legacy.tool.integration","name":"mysql protocol compatibility for legacy tool integration","description":"MindsDB implements the MySQL wire protocol (port 47335), allowing any MySQL-compatible client (MySQL CLI, Tableau, Metabase, DBeaver, etc.) to connect and query MindsDB as if it were a MySQL server. The protocol adapter translates MySQL commands into MindsDB operations, enabling seamless integration with existing BI tools, SQL IDEs, and reporting systems without custom drivers or adapters. Connection pooling, prepared statements, and transaction semantics are supported.","intents":["Connect existing BI tools (Tableau, Metabase) to MindsDB without custom connectors","Use MySQL CLI and SQL IDEs (DBeaver, DataGrip) to query MindsDB","Migrate applications from MySQL to MindsDB with minimal code changes"],"best_for":["Organizations with existing MySQL-based tooling who want to adopt MindsDB","Teams using BI tools that support MySQL but not custom protocols","Developers migrating from MySQL to MindsDB incrementally"],"limitations":["Not all MySQL features are supported — some edge cases in transaction handling or prepared statements may fail","Performance may be lower than native MySQL due to protocol translation overhead","Some MySQL-specific functions (e.g., LOAD DATA INFILE) are not supported","Connection pooling is limited — high concurrency may degrade performance"],"requires":["MySQL-compatible client (mysql CLI, Tableau, Metabase, etc.)","Network access to MindsDB MySQL port (47335)","Valid MindsDB credentials"],"input_types":["MySQL protocol commands","SQL queries (SELECT, INSERT, UPDATE, DELETE, CREATE MODEL, etc.)"],"output_types":["MySQL protocol result sets","Column metadata","Error messages"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","Valid credentials for each data source (API keys, connection strings, OAuth tokens)","Network access to target data sources","MySQL client or HTTP client library for query submission","Vector database integration (Pinecone, Weaviate, Milvus, or similar) with API credentials","LLM provider for embedding generation (OpenAI, Anthropic, HuggingFace, etc.)","Document source (file uploads, URLs, or database tables)","Project name and configuration","User authentication and authorization","Cron expression for scheduling"],"failure_modes":["Query optimization across heterogeneous sources is limited — complex joins across different source types may require client-side filtering","API rate limits from source systems are not automatically managed — developers must implement backoff logic","Schema inference for unstructured APIs (e.g., REST endpoints) may require manual schema definition","Chunking strategy is fixed (no custom chunking logic exposed) — may not be optimal for domain-specific documents","Embedding model selection is limited to configured LLM providers — no fine-tuned embedding models","Vector database selection is static per knowledge base — cannot dynamically switch backends","No built-in deduplication or update semantics — re-indexing requires manual deletion and re-upload","Cross-project queries are not supported — data must be copied or federated manually","Project-level quotas (storage, compute) are not enforced","Role-based access control is basic — no fine-grained permissions per resource type","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8165848366375135,"quality":0.35,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:57:01.479Z","last_commit":"2026-04-30T11:59:01Z"},"community":{"stars":39093,"forks":6192,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mindsdb--mindsdb","compare_url":"https://unfragile.ai/compare?artifact=mindsdb--mindsdb"}},"signature":"LgyXwXKkZwVSK6RE8y8XHpiambPB3JpM6q13zwQS9YP556Uy138a0iwhOIRjbGGEkyVoZ6aNWkMsA1oCxlImBw==","signedAt":"2026-06-22T01:08:51.546Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mindsdb--mindsdb","artifact":"https://unfragile.ai/mindsdb--mindsdb","verify":"https://unfragile.ai/api/v1/verify?slug=mindsdb--mindsdb","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"}}