Skip to main content
Glama
thebrownproject

RagLit MCP Server

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
OPENAI_API_KEYYesYour OpenAI API key for embedding generation.
EMBEDDING_MODELNoThe OpenAI embedding model to use.text-embedding-3-small
EXTERNAL_API_KEYYesThe API key for PostgREST (if required).
EXTERNAL_API_URLYesThe URL of the PostgREST or Supabase instance.

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}

Tools

Functions exposed to the LLM to take actions

NameDescription
chunk_documentD
search_chunksD
filter_metadataD

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

C2.1/5.0

Scored across 3 tools

Disambiguation5/5

Each tool targets a distinct step in the RAG pipeline: chunking documents, searching chunks, and filtering by metadata. The purposes are clearly separable based on names alone, with no overlapping actions.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case: chunk_document, search_chunks, filter_metadata. This creates a predictable and readable naming convention.

Tool Count4/5

Three tools is on the lower end of the well-scoped range (3-15), but it covers the core RAG operations without being sparse. The count is reasonable for a focused server, though slightly minimal.

Completeness4/5

The tool set covers the essential lifecycle for a simple RAG server: ingest (chunk), retrieve (search), and refine (filter). Minor gaps exist, such as lack of explicit document management or chunk retrieval, but the core workflow is functional.

Maintenance

ActivityInactive
ResponsivenessNo issues