Gengomcp

An MCP server (Python, stdio transport) that lets an agent retrieve ACL conference papers about NLP from a Qdrant vector database. It combines semantic search (Sentence‑Transformers embeddings) with structured filtering by bibliographic fields like publication year and venue.

Qdrant access is currently limited. This server queries a shared Qdrant collection of ACL NLP papers. If you'd like credentials to use it, please reach out to the project maintainer — access may be granted at a limited scale. You'll receive a QDRANT_URL, QDRANT_KEY, and QDRANT_COLLECTION_NAME to set in your MCP client's env field.

Quick start

1. Install gengomcp from PyPI:

   pip install gengomcp
   

2. Configure credentials in your MCP client's env field. You'll need QDRANT_URL, QDRANT_KEY, and QDRANT_COLLECTION_NAME — see Wiring it into an MCP client for full config examples.

3. Use it. Your agent can now call search_papers, get_paper, list_papers, and get_collection_info to find ACL NLP conference papers.

Wiring it into an MCP client

Any MCP client over stdio works. When installed from PyPI (pip install gengomcp or uv tool install gengomcp), the gengomcp command is on your PATH and runs independently of your working directory, so it's safe to launch from anywhere.

Example for Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "gengomcp": {
      "command": "gengomcp",
      "args": []
    }
  }
}

Configuring credentials via the MCP client

Credentials (QDRANT_URL, QDRANT_KEY, QDRANT_COLLECTION_NAME) are read from the process environment. Inject them directly through your MCP client's env field — this is the recommended way to configure per-agent credentials:

{
  "mcpServers": {
    "gengomcp": {
      "command": "gengomcp",
      "args": [],
      "env": {
        "QDRANT_URL": "https://<cluster>.cloud.qdrant.io",
        "QDRANT_KEY": "<your-api-key>",
        "QDRANT_COLLECTION_NAME": "papers_test"
      }
    }
  }
}

Credentials come from the MCP client's env field and are never logged or hard-coded. They live only on your machine — they are not sent to any third-party service.

Required variables (no defaults):

Variable	Description
QDRANT_URL	Qdrant cluster URL
QDRANT_KEY	Qdrant API key
QDRANT_COLLECTION_NAME	Collection to search (e.g. papers_test)

Optional variables (have defaults; not needed for basic use): EMBEDDING_MODEL, AUTO_CREATE_INDEXES, LOG_LEVEL.

If a required variable is missing at startup, the server exits with a clear error explaining how to set it.

Poolside (pool)

The server is registered to use the PyPI-installed gengomcp command with credentials injected via the env field. Verify with:

pool mcp list          # shows: gengomcp
pool mcp get gengomcp  # shows the stored command + args + env vars

The config is stored under mcp_servers in ~/.config/poolside/settings.yaml (personal config). Credentials are passed via the env field and live only on your machine — they are never sent to Poolside's servers. To remove the server later:

pool mcp remove gengomcp

Tools

Tool	Purpose
search_papers	Semantic search for ACL NLP papers. USE when the user has a topic/question. Embeds query and returns the most similar papers, optionally narrowed by structured filters.
get_paper	USE to inspect a single ACL NLP paper in full detail (abstract, summaries, entities) when you already have its paper_uuid from a search result.
list_papers	USE to browse/filter ACL NLP papers with no query text — pure structured filtering + pagination (e.g. "all ACL 2024 papers").
get_collection_info	USE first to discover available venues, years, fields of study, and vector names before building filters.

search_papers parameters

query                 str   (required) search text
limit                 int   = 10   (clamped 1..100)
vector_name           str   = "overview"   one of overview/approach/challenge/outcome
year                  int            exact publication year (e.g. 2026)
year_min / year_max   int            year range (inclusive)
year_gt  / year_lt    int            year range (exclusive)
venue                 str            substring match on the booktitle (e.g. "Annual Meeting")
collection_acronym    str            exact venue acronym, e.g. "ACL" / "EMNLP" / "NAACL"
collection_id         str            e.g. "2026.acl"
field_of_study        list[str]      membership on `field_of_studies` (e.g. ["Reasoning"])
author                str            name contained in `author_names`
min_score             float          only return results with similarity >= this value

All filters are AND‑combined, so you can layer them, e.g. search_papers(query="...", year_min=2020, collection_acronym="ACL").

Example tool calls

search_papers(query="stress testing large language models",
              vector_name="overview", year_min=2024, year_max=2026,
              collection_acronym="ACL", limit=5)

get_paper(paper_id="000036a6-e2be-523e-8b8d-0f2cbe2b39e7")

list_papers(collection_acronym="EMNLP", year=2024, limit=20)

list_papers(field_of_study=["Reasoning"], author="Pan", limit=20, offset=<prev_uuid>)

How it works

• Secrets & config — credentials are set via your MCP client's env field (QDRANT_URL, QDRANT_KEY, QDRANT_COLLECTION_NAME). QDRANT_KEY is passed directly to the Qdrant client and is never printed or hard-coded.
• Payload indexes — Qdrant requires a payload index to filter on a field. This collection ships with no indexes, so the server creates the needed ones idempotently at startup (non-destructive — it only adds indexes). Disable with AUTO_CREATE_INDEXES=0 if you manage indexes yourself.
• Embeddings — queries are embedded with Sentence‑Transformers using Snowflake/snowflake-arctic-embed-s, the only model that matches this collection's 384-dimensional index. The server can truncate+renormalise other model outputs to the index dimensionality (matryoshka‑style) as a safety net, but models in a different embedding space (e.g. the 768-dim m-v1.5) will still fail to retrieve — see The embedding model.
• Named vectors — the overview/approach/challenge/outcome named vectors in the collection are all 384-dimensional.

The embedding model

The collection's vectors are 384-dimensional and were built with the Snowflake arctic-embed "s" model (Snowflake/snowflake-arctic-embed-s). This is the only model that produces embeddings in the correct space for this index — it is the default and should not be changed.

Other models in the Snowflake family (e.g. m-v1.5 at 768-dim or l-v1.5 at 1024-dim) live in different embedding spaces. Even though the server can truncate embeddings to the index dimensionality (matryoshka-style) as a safety net, those models will not retrieve against this collection — keep EMBEDDING_MODEL at its default unless you re-index with a different model.

Project layout

gengomcp/
├── server.py        # the MCP server (tools + Qdrant/Embeddings glue)
├── main.py          # thin launcher
├── pyproject.toml   # deps + `gengomcp` console script
├── uv.lock          # pinned dependency versions
├── LICENSE          # MIT
├── .env.example     # template for all config vars (committed)
└── README.md

Development / testing

uv run python -c "import server; print('ok')"