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bdc-doc-mcp

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Hybrid

BDC Doc RAG

Das Dokumentations-RAG-MCP von bdc-assist

bdc_doc_mcp/config.py      env-driven embeddings/LLM/Chroma (replaces utils/__init__.set_emb_llm)
bdc_doc_mcp/ingest.py      .pkl/.md/.mdx/.txt/.pdf → embeddings → Chroma (replaces utils/chroma/utils.py)
bdc_doc_mcp/api.py         FastAPI: /health /search
bdc_doc_mcp/mcp_server.py  search_docs MCP tool for AI agents — self-contained, same search as the API
bdc_doc_mcp/preproc/       source-specific preprocessing pipeline
tests/                     self-checks + API / agent notebooks
data/                      preproc output (*.pkl), ingest input

Einrichtung

uv sync
cp .env.example .env    # then fill in keys/URLs

Quell-Repos

Nur für die Vorverarbeitung (--sources all) benötigt; der API/MCP-Server und das Aufnehmen vorhandener .pkl-Dateien funktionieren auch ohne sie. Klonen Sie neben dieses Repo (oder setzen Sie die Umgebungsvariablen darauf):

git clone https://github.com/stagecc/interim-bdc-website ../interim-bdc-website   # BDC_WEBSITE_DIR
git clone https://github.com/stagecc/bdc-gitbook ../bdc-gitbook                   # BDC_GITBOOK_DIR

Modelle

Für die Vervollständigung verwenden Sie die OpenAI-API auf Azure (standardmäßig gpt-4o-mini)

Für Einbettungen verwenden Sie Ollama auf Sterling (Verbindung über RENCI-VPN)

kubectl -n ner port-forward svc/ollama 11434:11434

Oder mit lokalem Ollama und dem Modell groonga/bge-m3-Q4_K_M-GGUF.

Related MCP server: okfy

Aufnahme

Vollständiger Neuaufbau aus jeder Quelle (benötigt die beiden geklonten Quell-Repos – siehe Einrichtung; schreibt data/*.pkl und lädt sie dann):

uv run python -m bdc_doc_mcp.preproc.pipeline --sources all --ingest --reset

Einzelne Dateien oder Verzeichnisse:

uv run python -m bdc_doc_mcp.ingest ./data/docs.pkl --doc-type docs   # BDC_Chatbot preproc .pkl
uv run python -m bdc_doc_mcp.ingest ../interim-bdc-website/src/pages --doc-type page --reset

Einbettungsmodelle sind innerhalb einer Sammlung nicht austauschbar – bge-m3 hat 1024 Dimensionen, text-embedding-3-small 1536. Ein Modellwechsel bedeutet --reset und eine vollständige Neuaufnahme.

API

uv run uvicorn bdc_doc_mcp.api:app --port 8000     # docs at /docs

Endpunkt

Anfragekörper

Rückgabe

GET /health

—

{status, documents}

POST /search

{query, k, mode?, doc_type?, date_from?, date_to?}

bewertete Chunks + Metadaten + Punktzahl

mode ist embedding (Standard; semantische Ähnlichkeit, Punktzahl = Distanz, niedriger ist besser) oder keyword (unscharfer wörtlicher Wortabgleich – ignoriert Groß-/Kleinschreibung und Satzzeichen und toleriert kleine Tippfehler, sodass picsure "PIC-SURE" findet; Punktzahl = Anzahl der Vorkommen, höher ist besser – für exakte Namen/Akronyme verwenden). doc_type ist eine CSV der zu durchsuchenden Typen (z. B. page,faq). Wenn nicht angegeben, werden nur docs, page, faq und video durchsucht – nennen Sie fellow, update oder event explizit, um sie zu durchsuchen. date_from/date_to (YYYY-MM-DD, inklusive) filtern nach Datum; nur Ereignis- und Update-Dokumente haben ein Datum, daher schränkt ein Datumsfilter implizit auf diese Typen ein.

Der Dienst ist von Natur aus nur für die Suche gedacht; die Aufnahme erfolgt offline über die CLI (siehe Aufnahme) und das Beantworten ist Aufgabe des Aufrufers – ein Agent bringt sein eigenes LLM mit.

MCP

uv run python -m bdc_doc_mcp.mcp_server           # stdio
uv run python -m bdc_doc_mcp.mcp_server --http    # streamable HTTP, port MCP_PORT (default 8001)

Stellt ein Tool bereit, search_docs – dieselbe Suche wie die API, fragt aber Chroma direkt ab, sodass der API-Dienst nicht laufen muss. Benötigt eine aufgenommene .chroma_db + Einbettungen.

Stdio-Clients (Claude Desktop/Code, Cursor) starten den Server selbst – registrieren Sie ihn:

{"mcpServers": {"bdc-doc-mcp": {
  "command": "uv",
  "args": ["--directory", "/path/to/bdc-doc-mcp", "run", "python", "-m", "bdc_doc_mcp.mcp_server"]
}}}

Netzwerk-Clients: Führen Sie --http aus und richten Sie sie stattdessen auf http://host:8001/mcp.

Smoke-Test: uv run python tests/test_mcp.py

Vorverarbeitung

bdc_doc_mcp/preproc/ ist die portierte BDC_Chatbot-Pipeline:

Modul

Quelle

Portiert von (BDC_Chatbot)

Anmerkungen

bdc_repo.py

interim-bdc-website MDX

utils/preproc/proc_BDC_repo.py (nahezu wörtlich)

fellows, events, latest-updates, pages

bdc_docs.py

bdc-gitbook markdown

utils/preproc/proc_BDC_docs.py (LLM-Initialisierung auf Modulebene entfernt)

nach Header-Hierarchie aufgeteilt; benötigt das geklonte Repo

freshdesk.py

bdcatalyst.freshdesk.com

utils/preproc/proc_freshdesk.py

Live-Scrape

vids.py

Google Sheet + Drive SRT

utils/preproc/proc_BDC_vids.py (GoogleSheetsReader-Klasse abgeflacht)

Video-Transkripte mit Zeitstempel-URLs

utils.py

—

—

LLM-Chunk-Kontextualisierer + Zusammenfasser

pipeline.py

—

utils/preproc_doc.py

Orchestrator

--no-contextualize überspringt den LLM-Aufruf pro Chunk (viel schneller, schwächere Abfrage). Quellpfade stammen aus BDC_WEBSITE_DIR / BDC_GITBOOK_DIR.

Tests

uv run python tests/test_ingest.py                             # batching + chunk-id logic, no network
uv run python tests/test_keyword.py                            # keyword ranking, pure function, no DB or API
uv run python tests/test_mcp.py                                # starts the server over stdio and exercises its tools; needs .chroma_db + embeddings

Notebooks (jedes startet die API auf einem freien Port und fährt sie am Ende herunter; beide benötigen eine aufgenommene .chroma_db):

  • tests/api_test.ipynb — einfacher API-Durchlauf: /health, /search, doc_type-Filter. Benötigt nur die lokalen Einbettungen.

  • tests/agent_test.ipynb — ein Tool-aufrufender Agent (deepagents): das konfigurierte LLM erhält search_docs als LangChain-Tool und entscheidet, wann es aufgerufen wird. Benötigt außerdem den erreichbaren Vervollständigungsanbieter.

Available Tools

1 tool
search_docsA

Search the BDC (NHLBI BioData Catalyst) documentation database.

Returns the top-k matching chunks with content, metadata (source, doc_type, datetime when available), and a score.

query is the search text. In embedding mode phrase it as a question or topic (e.g. "how do I bring my own data"); in keyword mode give the literal terms to match.

k is the number of chunks to return (default 5). Raise it (10-20) for broad or multi-part questions; each chunk is a small section of a document.

mode toggles the search engine:

  • "embedding" (default): semantic similarity — best for questions, topics, and paraphrased wording. score is a distance (lower = more similar).

  • "keyword": fuzzy literal word matching — ignores case and punctuation ("picsure" finds "PIC-SURE") and tolerates small typos — best for exact names, acronyms, tool names, or error messages the embedding may blur. Chunks matching more of the query terms rank first; score is the total number of occurrences (higher = better).

doc_type is a CSV string of types to search (e.g. "page,faq" or "video"). Available types:

  • docs: BDC GitBook platform documentation — user guides, how-tos, and technical reference (bdcatalyst.gitbook.io)

  • page: key pages of the BDC website — about/overview, joining BDC, analyzing & sharing data, usage costs and terms

  • faq: Freshdesk help-desk FAQ articles (support questions & answers)

  • video: transcripts of BDC YouTube tutorials/webinars, with timestamped links into the video

  • fellow: BDC Fellows profiles — fellowship recipients and their research projects

  • update: dated news posts ("latest updates") from the BDC website

  • event: dated BDC events — webinars, workshops, deadlines When doc_type is omitted, only docs, page, faq, and video are searched — name fellow, update, or event explicitly to search them.

date_from / date_to ("YYYY-MM-DD", inclusive) filter by date. Only event and update docs carry a date, so a date filter implicitly narrows to those types. Results are ranked by relevance, NOT date — for "recent"/"latest" questions, always set date_from to bound the range, then compare the dates returned.

ParametersJSON Schema
NameRequiredDescriptionDefault
kNo
modeNoembedding
queryYes
date_toNo
doc_typeNo
date_fromNo

TDQS

A5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully discloses behavioral traits: default doc types when omitted, score interpretation (distance vs occurrences), the effect of date filters, and ranking by relevance not date. It also notes that only event and update docs carry dates, further clarifying behavior. No annotation contradiction exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with bullet points for modes and types, and clear paragraphs for date and ranking behavior. It is lengthy but every sentence carries essential information, and it is front-loaded with the purpose and return content. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 parameters, no output schema, no annotations), the description is complete. It explains return format, scoring meaning, type-specific behavior, and parameter interactions. It fully equips an agent to invoke the tool correctly for a variety of use cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it does thoroughly. It explains query phrasing for each mode, k's range and purpose, mode options with detailed semantics, doc_type as a CSV list with each type's meaning, and date_from/date_to format and inclusive behavior. This adds far more meaning than the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it searches the BDC (NHLBI BioData Catalyst) documentation database and returns top-k matching chunks with content, metadata, and a score. It names the specific resource and what is returned, making the tool's purpose unambiguous even without sibling tools for differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance on when to use embedding vs keyword mode, how to adjust k for broad questions, when to explicitly name doc_type values, and how to use date filters for recency queries. It also warns that date filters implicitly narrow to types with dates and explains ranking behavior, giving clear when-to-use and when-not-to-use instruction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev0.1.0
    • First observedsearch_docs
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.6/5.0

Scored across 1 tool

Disambiguation5/5

With only a single tool, there is no possibility of ambiguity. The tool has a clearly defined purpose for searching documentation.

Naming Consistency5/5

The tool name 'search_docs' follows a consistent verb_noun pattern and is descriptive. Since it is the only tool, naming is inherently consistent.

Tool Count1/5

The server exposes only one tool, which is extremely thin. Even though the tool is multi-functional, a single tool does not constitute a well-scoped set; most servers with this purpose would benefit from at least a couple of complementary tools (e.g., retrieving a document by ID or listing available types).

Completeness3/5

The search tool covers multiple documentation sources and provides filtering and multiple modes, which addresses the core purpose. However, it lacks any other operation such as fetching a specific document, listing available doc types, or managing content, leaving notable gaps for a documentation server.

Maintenance

ActivityMaintained
ResponsivenessUnresponsive

Resources

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