Schema | rag-mcp

rag-mcp

Describes the environment variables required to run the server.

Name	Required	Description
`EMBED_DIM`	No	Dimension of the embedding vectors. Must match the model (default: 1536).
`CHUNK_SIZE`	No	Number of characters per chunk (default: 1000).
`DATABASE_URL`	Yes	PostgreSQL connection string (e.g., postgresql://postgres:postgres@localhost:5432/rag). Required.
`CHUNK_OVERLAP`	No	Number of overlapping characters between chunks (default: 200).
`MCP_TRANSPORT`	No	Transport protocol: 'stdio' or 'http' (default: stdio).
`EMBEDDINGS_MODEL`	No	Embedding model name (default: text-embedding-3-small).
`EMBEDDINGS_API_KEY`	Yes	API key for the embeddings endpoint (OpenAI-compatible). Required.
`EMBEDDINGS_API_BASE`	No	Base URL for the embeddings API (default: https://api.openai.com/v1).

Features and capabilities supported by this server

Capability	Details
`tools`	{ "listChanged": true }
`logging`	{}
`prompts`	{ "listChanged": false }
`resources`	{ "subscribe": false, "listChanged": false }
`extensions`	{ "io.modelcontextprotocol/ui": {} }
`experimental`	{}

Functions exposed to the LLM to take actions

Name	Description
ingest_textA	Chunk, embed, and store text in a collection for later semantic search. collection: logical namespace (e.g. a project or document set). metadata: optional JSON attached to every chunk (source, title, url, ...).
searchA	Semantic search a collection. Returns the top-k chunks with a 0-1 cosine score.
list_collectionsA	List all collections and their chunk counts.
delete_collectionA	Delete a collection and all of its chunks. Irreversible.

Interactive templates invoked by user choice

Name	Description
No prompts

Contextual data attached and managed by the client

Name	Description
No resources

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/NextLevelManagementAdvisors/rag-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server