| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes the data.gouv.fr API v1 GET /1/datasets/ endpoint through an MCP tool that accepts free-text search queries and returns paginated dataset metadata (title, description, organization, tags, update frequency). Implements client-side pagination and result ranking to surface the most relevant datasets from France's national open data catalog without requiring users to manually navigate the web interface.
Unique: Directly wraps data.gouv.fr's native search API through MCP protocol, enabling conversational dataset discovery without web scraping or custom indexing — the server acts as a thin, read-only proxy that preserves the platform's native ranking and filtering logic.
vs alternatives: Unlike generic web search or manual catalog browsing, this provides structured, ranked results from the authoritative French government data platform with guaranteed freshness and official metadata.
Fetches complete metadata for a single dataset by ID from data.gouv.fr API v1 GET /1/datasets/{id}/, returning title, description, organization, tags, creation/update timestamps, license, and a complete inventory of all associated resources (files). Uses a single API call per dataset to avoid N+1 queries and provides structured output suitable for downstream resource selection or analysis planning.
Unique: Provides a single atomic call to retrieve complete dataset context including all resources, avoiding the need for separate API calls per resource and enabling AI agents to make informed decisions about which files to query or download.
vs alternatives: More efficient than iterating through individual resource endpoints; returns the full dataset graph in one call, reducing latency and simplifying agent planning logic compared to sequential resource lookups.
Provides a Dockerfile and Docker Compose configuration for containerized deployment, enabling the MCP server to run in Kubernetes, Docker Swarm, or any container orchestration platform. The container exposes port 8000 (HTTP) and includes health check configuration (GET /health endpoint) for orchestrator integration. Supports environment variable configuration for API endpoints, logging levels, and other runtime parameters, enabling deployment across development, staging, and production environments without code changes.
Unique: Provides production-ready Docker configuration with health check integration and environment variable support, enabling seamless deployment to any container orchestration platform without modification — the server is stateless and horizontally scalable.
vs alternatives: Ready-to-deploy container image reduces operational overhead compared to manual installation; stateless design enables horizontal scaling and zero-downtime updates.
Centralizes all runtime configuration (API endpoints, logging levels, server port, CORS settings, etc.) in environment variables, enabling the same Docker image or Python process to run in different environments without code changes. Configuration is loaded at startup via a dedicated configuration module that validates and provides defaults. Supports multi-instance deployments where each instance can be configured independently via environment variables, enabling load-balanced and highly-available setups.
Unique: Uses environment variables for all configuration, enabling the same codebase and Docker image to run in any environment without modification — this is a cloud-native best practice (12-factor app methodology).
vs alternatives: Simpler and more portable than configuration files or hardcoded settings; integrates seamlessly with container orchestration platforms (Kubernetes, Docker Swarm) that manage environment variables.
Queries data.gouv.fr API v2 GET /2/datasets/resources/{id}/ to retrieve detailed metadata for a single file/resource, including format (CSV, XLSX, JSON, etc.), file size, MIME type, and critically, whether the resource supports the Tabular API (a data.gouv.fr feature enabling row-level querying without full download). Returns structured metadata that allows agents to decide between streaming/parsing (for unsupported formats) or direct tabular queries (for supported formats).
Unique: Explicitly surfaces Tabular API availability as a first-class capability, enabling agents to make intelligent routing decisions between direct querying and download-then-parse workflows — this is unique to data.gouv.fr's architecture and not exposed by generic data APIs.
vs alternatives: Provides format-aware capability detection that generic file metadata APIs lack; allows agents to optimize for latency and bandwidth by choosing the most efficient access pattern per resource.
Executes structured queries against CSV and XLSX resources using data.gouv.fr's Tabular API, supporting row filtering, column selection, sorting, and pagination. Implements client-side parameter validation and result streaming to handle large datasets within practical limits (respects data.gouv.fr rate limits and payload size constraints). Queries are executed without downloading the entire file, enabling efficient exploration of large datasets within a single conversation turn.
Unique: Leverages data.gouv.fr's native Tabular API to enable server-side filtering and pagination without full file download, reducing bandwidth and latency compared to download-then-filter approaches — the MCP server translates natural query parameters into Tabular API calls.
vs alternatives: More efficient than downloading entire CSV files for exploration; supports server-side filtering and pagination that generic file download APIs do not provide, enabling interactive data exploration at scale.
Downloads and parses CSV, XLSX, JSON, and other resource formats that do not support the Tabular API, streaming the file to avoid memory exhaustion and applying format-specific parsers (csv.DictReader for CSV, openpyxl for XLSX, json.load for JSON). Implements chunked reading and result truncation to respect practical limits on response size within MCP protocol constraints. Enables agents to access data from any format without requiring external download tools.
Unique: Implements streaming and chunked parsing to handle large files without loading entire datasets into memory, with format-specific parsers (csv.DictReader, openpyxl, json.load) that preserve data types and structure — this is distinct from naive download-and-parse approaches that fail on large files.
vs alternatives: Supports format-agnostic parsing with streaming to handle files larger than available memory; more robust than generic HTTP download tools because it applies format-specific parsing logic and respects MCP payload constraints.
Queries data.gouv.fr's dataservice catalog (API endpoints, web services, and data APIs exposed by organizations) via dedicated MCP tools that search and retrieve dataservice metadata. Enables agents to discover and understand available APIs and services without manual catalog browsing, returning service descriptions, endpoints, and usage documentation. Complements dataset discovery by surfacing programmatic access methods.
Unique: Exposes data.gouv.fr's dataservice catalog as a first-class MCP tool, enabling agents to discover and reason about APIs and web services in addition to static datasets — most data discovery tools focus only on datasets and ignore programmatic access methods.
vs alternatives: Provides unified discovery of both datasets and dataservices through a single MCP interface, whereas typical data portals require separate browsing for static files vs. APIs.
+4 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs datagouv-mcp at 35/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities