MCP Servers

atlas-mcp-doc-search

Enables hybrid document search (BM25 + vector) with Reciprocal Rank Fusion over the Atlas corpus, returning ranked chunks from documents.

README

atlas-mcp-doc-search

MCP server that exposes hybrid document search over the Atlas ingested corpus. Built with the official Python mcp SDK (FastMCP server interface, Streamable HTTP transport), Python 3.12 asyncio, and deployed as a standalone K8s service on AKS.

Tool Contract

`doc_search`

doc_search(query: str, k: int = 8) -> {chunks: [{id, text, source_id, score}]}

Parameter	Type	Default	Description
`query`	`str`	required	Natural-language search query
`k`	`int`	`8`	Number of top-ranked chunks to return

Returns a list of up to k chunks, each with:

Field	Type	Description
`id`	`str`	Unique chunk identifier
`text`	`str`	Raw chunk text
`source_id`	`str`	Identifier of the source document
`score`	`float`	Reciprocal Rank Fusion (RRF) fused score

Hybrid Retrieval Approach

The server implements HYBRID retrieval — combining sparse (BM25) and dense (vector) signals and fusing them with Reciprocal Rank Fusion (RRF):

Query embedding — the raw query string is sent to the Atlas gateway's /v1/embeddings endpoint to produce a dense vector.
Parallel retrieval
- Elasticsearch BM25 keyword search over the doc_chunks index.
- Qdrant vector similarity search over collection doc_chunks (payload fields: source_id, doc_id, chunk_idx, text).
Fusion — both ranked lists are merged with RRF to produce a single ranked list.
Return — top k results are returned with their fused scores.

Dependencies

Dependency	Role
`mcp` (official Python SDK)	MCP server framework
Elasticsearch	BM25 keyword retrieval over `doc_chunks`
Qdrant	Dense vector retrieval over collection `doc_chunks`
Atlas gateway `/v1/embeddings`	Query embedding generation

Diagrams

atlas-docs — document ingestion pipeline that populates the corpus
atlas-mcp-citations — citation verification MCP server

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