GraphQL Schema Embedder MCP Server

GraphQL schema embedder MCP server

Python MCP server for LLMs that indexes a GraphQL schema, stores embeddings per type->field via an embeddings endpoint, and enables fast lookup plus run_query execution once relevant types are identified to fetch data from your GraphQL endpoint.

Architecture

• GraphQL schema: provide a schema file (SDL) to exercise parsing and indexing.
• Indexer: schema_indexer.py flattens the schema into type.field signatures (with arguments and return types), embeds each summary via the configured embeddings endpoint, and persists to data/metadata.json + data/vectors.npz (normalized embeddings for cosine search).
• Server: server.py exposes MCP tools list_types and run_query. The server ensures the schema index exists on startup; it only calls the embeddings endpoint when reindexing or embedding a new query.
• Persistence: data/ is .gitignore'd so you can regenerate locally without polluting the repo.

Setup

Set env vars. You can start from .env.example.

Environment configuration:

• GRAPHQL_EMBED_API_KEY (or OPENAI_API_KEY)
• GRAPHQL_EMBEDDINGS_URL (full embeddings URL)
• GRAPHQL_EMBED_MODEL
• GRAPHQL_EMBED_API_KEY_HEADER / GRAPHQL_EMBED_API_KEY_PREFIX
• GRAPHQL_EMBED_HEADERS (JSON object string for extra headers) Endpoint auth (when using GRAPHQL_ENDPOINT_URL):
• GRAPHQL_ENDPOINT_HEADERS (JSON object string, merged with any --header flags)

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python3 src/server.py

Run the MCP server

python3 src/server.py                # SSE on 127.0.0.1:8000/sse by default
python3 src/server.py --transport sse     # explicit SSE
python3 src/server.py --transport streamable-http  # Streamable HTTP on 127.0.0.1:8000/mcp
# Or: point at a live GraphQL endpoint (requires introspection enabled)
python3 src/server.py --endpoint https://api.example.com/graphql
# Endpoint auth headers (repeat --header)
python3 src/src/server.py --endpoint https://api.example.com/graphql --header "Authorization: Bearer $TOKEN"
# Options: --host 0.0.0.0 --port 9000 --log-level DEBUG --mount-path /myapp

Tools:

• list_types(query, limit=5) – fuzzy search over type.field signatures (embeddings; auto-build index if missing). Results are ordered by combined score (with a Query boost) and include a query for Query fields plus a select hint for object fields. Output is compacted to reduce tokens.
• run_query(query) – if --endpoint is set, proxies the query to the endpoint; otherwise validates/runs against the local schema (no resolvers; primarily for validation/shape checking, data resolves to null). Both indexing and querying use the same embedding model (text-embedding-3-small by default, override via config/env or --model).

Ranking + cutoff (list_types):

• Scoring formula (non-aggregate):

score =
  embedding_score
  + 0.30 * I[is_query]
  + 0.20 * I[token_match]
  + 0.15 * I[list_query & connection]
  + 0.05 * I[list_query & list]
  - 0.20 * I[list_query & count]

• Scoring formula (aggregate):

score =
  embedding_score
  + 0.30 * I[is_query]
  + 0.25 * I[is_count]
  + 0.10 * I[is_connection]

• Dynamic cutoff: keep items where score >= 0.75 * max_score or token_match; always keep at least 3 and at most limit.
• Diversity guard: when limit >= 5, keep up to 3 non-Query items if available, with a softer cutoff to avoid Query-only starvation.

Example list_types output:

[
  {
    "type": "Query",
    "field": "users",
    "summary": "Query.users(limit: Int = 10, offset: Int = 0) -> [User!]!",
    "query": "query { users(limit: <Int = 10>, offset: <Int = 0>) { id name email profile { joinedAt preferences { newsletter } } orders { id status total } } }"
  },
  {
    "type": "User",
    "field": "orders",
    "summary": "User.orders -> [Order!]!",
    "select": "orders { id status total items { quantity subtotal } }"
  },
  {
    "type": "Product",
    "field": "reviews",
    "summary": "Product.reviews -> [Review!]!",
    "select": "reviews { id rating title author { id name } }"
  }
]

Notes:

• python3 src/server.py defaults to the sse transport; pass --transport streamable-http if you want HTTP instead.
• You can also set env vars prefixed with FASTMCP_ (e.g., FASTMCP_HOST, FASTMCP_PORT, FASTMCP_LOG_LEVEL) to override defaults.
• The server ensures the schema index is built on startup; if embeddings are computed, a simple progress bar is printed. Set GRAPHQL_EMBED_BATCH_SIZE to tune the batch size.
• The server exposes MCP instructions (override with MCP_INSTRUCTIONS) that describe the server as an abstraction layer and tell the LLM to use list_types then run_query with minimal tool calls.

Quick test with the MCP Inspector

Requires npm/npx on PATH.

Connect to an already-running SSE server

In one terminal (start the server): ``