Mcp Server Collector

Mcp Server Collector is a specialized MCP (Model Context Protocol) server designed to bridge the gap between raw MCP specifications and the structured data that AI assistants require for efficient, context‑aware interaction. By automatically crawling, parsing, and ingesting MCP definitions from both server and client sources, it produces a ready‑to‑query database that can be leveraged by Claude or other AI assistants to discover available tools, resources, and prompt templates without manual intervention.

The core problem this server addresses is the fragmentation of MCP metadata. In many organizations, MCP files are scattered across repositories, documentation sites, or internal wikis, and they often evolve independently of the AI assistants that consume them. Developers must otherwise manually sync these files, resolve naming collisions, and maintain consistency across environments—a tedious process that is prone to error. The Mcp Server Collector automates this workflow by crawling designated directories, extracting relevant MCP elements (such as resources, tools, and prompts), and populating a Supabase database. This guarantees that every assistant instance has instant access to the latest, authoritative MCP definitions.

Key features include:

• Selective crawling: Options like or let users target specific parts of the MCP hierarchy, reducing processing time and bandwidth.
• Database integration: The crawler writes directly to a Supabase instance, providing a scalable, queryable store that can be queried via standard SQL or MCP client APIs.
• SQL export: With , developers can generate a portable SQL dump for backup, migration, or version control purposes.
• Import flexibility: The flag enables a “dry run” that validates the crawl without touching the database, useful for CI pipelines.

Real‑world scenarios where this server shines include:

• Continuous delivery of AI tooling: In a microservices architecture, new MCP definitions can be automatically ingested and made available to all assistants in minutes.
• Compliance auditing: By maintaining a single source of truth, organizations can audit which tools and prompts are active, ensuring adherence to policy.
• Rapid prototyping: New developers can spin up a fresh MCP database with minimal setup, allowing them to experiment with AI assistants against the latest tooling.

Integration into an AI workflow is straightforward. Once the database is populated, any MCP‑compatible client (such as a Claude connector) can query it to discover available resources or invoke tools. Because the server exposes the MCP catalog as a standard SQL interface, existing data‑access patterns (ORMs, raw queries) can be reused without learning new APIs. This seamless fit into existing development pipelines makes the Mcp Server Collector a practical choice for teams that need reliable, up‑to‑date MCP metadata without the overhead of manual maintenance.