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"MCP server for codebase discovery and structure generation" matching MCP servers:

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    Enables AI agents to search and discover data across SQLite and CSV sources through an MCP interface, with metadata indexing and fuzzy search capabilities.
    MIT
  • F
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    quality
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    Enables AI agents to index and search across SQLite databases and CSV files to discover table schemas and column metadata. It provides a unified MCP API for data source management and structural exploration through natural language.
  • F
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    Enables semantic search and retrieval of code files using embeddings stored in PostgreSQL. Supports intelligent codebase exploration through natural language queries, file listing, and content retrieval.
    16
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    Enables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.
    1
    GPL 3.0
  • A
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    MCP server that connects AI assistants to Redash, enabling listing of assets, read-only query execution, dashboard inspection, and alert management through the Redash API.
    MIT
  • A
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    A safety-gated Python MCP server for Metabase that lets MCP clients and agents inspect Metabase, run governed queries, and create or maintain dashboards, cards/questions, collections, snippets, permissions, and other Metabase assets through the Metabase REST API.
    GPL 3.0
  • F
    license
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    quality
    C
    maintenance
    An MCP server for managing OpenText Knowledge Discovery content engines, enabling AI assistants to create, delete, list databases, delete documents, query engine status, and manage engine configuration through natural language.
  • F
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    quality
    A
    maintenance
    Enables local data discovery by indexing metadata from SQLite, CSV, and Markdown sources, providing hybrid keyword and TF-IDF semantic search via MCP tools (listSources, indexSource, search, getSchema) and a REST API.
  • F
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    An MCP server that indexes local Python projects into a SQLite database to enable efficient symbol searching and dependency tracking. It allows users to find function or class definitions, trace module imports, and read file contents through natural language interfaces.
  • A
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    Enables AI interaction with Microsoft Access and SQLite databases to perform SQL queries, updates, and data management. It supports importing and exporting data via CSV and Excel files and includes tools for managing human-readable notes about database files.
    7
    MIT
  • A
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    Enables secure access to Oracle databases with fine-grained access control, supporting multiple databases simultaneously with configurable access modes (readonly/readwrite/full) and table-level permissions for safe query execution and data management.
    1
    MIT
  • A
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    Provides access to MySQL databases with fine-grained access control, supporting multiple databases simultaneously with configurable access modes (readonly, readwrite, full) and table-level permissions using whitelists, blacklists, wildcards, and regex patterns.
    1
    1
    MIT
  • A
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    quality
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    maintenance
    A TypeScript-based server to interact with ArangoDB using the Model Context Protocol, enabling database operations and integration with tools like Claude and VSCode extensions for streamlined data management.
    7
    39
    47
    MIT
  • A
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    quality
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    A FastMCP integration server that provides access to Apache Gravitino metadata management APIs, allowing users to manage catalog/schema/table metadata, tags, and user-role information through a structured interface.
    23
    Apache 2.0
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    A Model Context Protocol server that connects AI agents to Apache Jena, enabling them to execute SPARQL queries and updates against RDF data stored in Jena Fuseki.
    8
    MIT