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rag-mcp-server

Name: rag-mcp-server
Author: q6066697

by q6066697

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rag-mcp-server

프로덕션 RAG 파이프라인(rag-eval-service)을 에이전트용 표준 프로토콜(Model Context Protocol)로 감싸는 MCP 서버입니다. 하이브리드 검색(dense 임베딩 + BM25 + RRF)이 모든 MCP 클라이언트(Claude Desktop, Claude Code 또는 자체 에이전트)가 호출할 수 있는 도구가 됩니다.

아키텍처

Client (Claude Desktop / Claude Code / любой MCP-клиент)
    |
    v  MCP over stdio (JSON-RPC)
FastMCP server  (rag_mcp_server/server.py)
    |
    +--> search_documents(query, top_k)
    +--> get_document(doc_id)
    +--> rerank_results(query, doc_ids)
    |
    v
Hybrid retrieval  (rag_mcp_server/core/retrieval.py)
    |
    +---> Dense retriever  --- OpenAI text-embedding-3-small ---> Qdrant (cosine, 1536d)
    |                                                                 |
    +---> Sparse retriever -- BM25 (rank-bm25 / BM25Okapi) ----------+
    |                                                                 |
    |                                 +-------------------------------+
    |                                 v
    |                        Reciprocal Rank Fusion (k=60)
    |                                 |
    +---------------------------------v
                              Top-k документов --> клиент (LLM формирует ответ)

Qdrant는 기본적으로 임베디드 모드(디스크의 파일, 별도 프로세스 없음)로 동작하므로 서버는 self-contained이며 데모에 외부 인프라가 필요하지 않습니다. 원한다면 docker-compose.yml을 통해 완전한 Qdrant 서버로 전환할 수 있습니다(아래 참조).

Related MCP server: RAG MCP Server

이것이 무엇이고 왜 필요한가

이것은 retrieval 로직을 재발명하는 것이 아니라 MCP 래퍼입니다. 하이브리드 검색(dense + BM25 + RRF) 자체는 rag-eval-service에서 거의 변경 없이 포팅되었습니다. 이 래퍼가 새로 추가하는 것은 다음과 같습니다:

HTTP API 대신 프로토콜 — 커스텀 HTTP 클라이언트를 작성하거나 REST용으로 서비스를 계속 실행할 필요 없이, 도구가 모든 MCP 클라이언트(Claude Desktop, Claude Code)에 표시됩니다.
Self-contained 데모 모드 — 도커 컨테이너 대신 임베디드 Qdrant를 사용하여 git clone → 동작하는 도구까지 최소한의 단계로 구성됩니다.
doc_id/title 스키마 및 문서 수준(청크가 아닌) 결과 집계 — search_documents → get_document를 호출하는 LLM 클라이언트의 요구에 맞춰 원시 청크를 직접 다루지 않습니다.
API 문서를 읽는 사람이 아닌, 도구의 LLM 소비자를 위해 작성된 Docstring 계약(rag_mcp_server/server.py 참조).

로컬에서 실행하는 방법

요구 사항: Python 3.10+, OpenAI API 키(임베딩용).

git clone https://github.com/q6066697/rag-mcp-server.git
cd rag-mcp-server

python3 -m venv .venv
source .venv/bin/activate        # Windows: .venv\Scripts\activate
pip install -r requirements.txt  # или: pip install -e ".[dev]"

cp .env.example .env
# впишите свой OPENAI_API_KEY в .env

인덱스 구축(1회성; data/를 읽고 OpenAI를 통해 청크를 임베딩한 후 임베디드 Qdrant에 업로드):

python -m rag_mcp_server.core.indexing

(선택 사항) 임베디드 모드 대신 실제 Qdrant:

docker-compose up -d
# затем в .env: QDRANT_MODE=server

서버 실행(stdio 전송):

python -m rag_mcp_server.server

MCP Inspector로 확인(mcp[cli]에 내장된 인스펙터, 도구를 수동으로 호출할 수 있는 웹 UI를 엽니다):

mcp dev rag_mcp_server/server.py

테스트(실제 Qdrant/OpenAI가 필요 없음 — 벡터 검색과 cross-encoder가 모킹됨):

pytest

Claude Desktop / Claude Code에 연결하는 방법

claude_desktop_config.json에 추가합니다(Claude Desktop: Settings → Developer → Edit Config; Claude Code: 프로젝트의 .mcp.json 또는 claude mcp add):

{
  "mcpServers": {
    "rag-mcp-server": {
      "command": "/absolute/path/to/rag-mcp-server/.venv/bin/python",
      "args": ["-m", "rag_mcp_server.server"],
      "cwd": "/absolute/path/to/rag-mcp-server",
      "env": {
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

클라이언트를 재시작하면 search_documents, get_document, rerank_results 도구가 사용 가능한 도구 목록에 나타납니다.

도구 호출 예시

search_documents

search_documents(query="что такое Reciprocal Rank Fusion", top_k=3)

[
  {
    "doc_id": "reciprocal-rank-fusion.md",
    "title": "Reciprocal Rank Fusion",
    "snippet": "RRF сливает несколько ранжированных списков без нормализации сырых score — документ на позиции r получает вклад 1/(k+r)…",
    "score": 0.0328
  },
  {
    "doc_id": "hybrid-search.md",
    "title": "Hybrid Search",
    "snippet": "Гибридный поиск комбинирует dense-эмбеддинги и BM25, чтобы ловить и семантическое сходство, и точные термины…",
    "score": 0.0301
  }
]

get_document

get_document(doc_id="reciprocal-rank-fusion.md")

"# Reciprocal Rank Fusion (RRF)\n\nRRF — метод слияния нескольких ранжированных списков результатов…"

rerank_results

rerank_results(
    query="как оценивать качество ретривера",
    doc_ids=["eval-retrieval-metrics.md", "reranking.md", "hybrid-search.md"]
)

[
  {
    "doc_id": "eval-retrieval-metrics.md",
    "title": "Evaluating Retrieval Quality",
    "snippet": "Метрики retrieval — hit@k, recall@k, MRR, nDCG@k — измеряют, находит ли поиск релевантные документы…",
    "score": 4.81
  },
  {
    "doc_id": "reranking.md",
    "title": "Reranking",
    "snippet": "Cross-encoder реранкинг переупорядочивает шортлист кандидатов, читая query и passage вместе…",
    "score": 1.02
  }
]

저장소 구조

rag-mcp-server/
├── rag_mcp_server/
│   ├── server.py          # точка входа, FastMCP инстанс, регистрация tools
│   ├── core/
│   │   ├── retrieval.py   # портированная гибридная логика поиска (dense + BM25 + RRF + rerank)
│   │   └── indexing.py    # загрузка корпуса, чанкинг, индексация в Qdrant
│   └── config.py          # конфигурация из .env
├── data/                  # bootstrap-корпус (15 markdown-доков, копия из rag-eval-service)
├── tests/
│   └── test_server.py     # unit-тесты на MCP tools (мокают поиск)
├── docker-compose.yml     # опциональный Qdrant-сервер
├── pyproject.toml / requirements.txt
├── .env.example
└── LICENSE (MIT)

retrieval 로직의 출처

하이브리드 검색(rag_mcp_server/core/retrieval.py, core/indexing.py)은 rag-eval-service에서 포팅되었습니다. eval-harness(NFCorpus/BEIR 벤치마크, 커스텀 golden set, hit@k/recall@k/MRR/nDCG 메트릭)도 그곳에 있으며, 이 저장소에는 의도적으로 포함하지 않았습니다. rag-mcp-server는 이미 검증된 retrieval 파이프라인 위의 얇은 프로토콜 래퍼이지, 그 파이프라인을 재평가하는 도구가 아니기 때문입니다.

향후 추가할 것

MCP 서버 자체를 위한 Docker — 현재는 Qdrant만 컨테이너로 감싸고 있습니다. 서버 자체를 배포하려면 자체 Dockerfile이 필요합니다.
SSE/HTTP 전송 — stdio는 클라이언트와 같은 머신의 로컬 프로세스를 전제로 합니다. 원격 액세스(여러 사용자, 클라우드 배포)에는 MCP SDK의 SSE 또는 Streamable HTTP 전송이 필요합니다.
인증 — stdio 전송은 설계상 인증이 없습니다(신뢰할 수 있는 프로세스가 로컬에서 실행됨). 네트워크 전송으로 전환하면 서버 수준의 API 키/OAuth가 필요합니다.
증분 인덱싱 — 현재 core.indexing은 컬렉션을 전체적으로 재생성합니다. 성장하는 코퍼스에는 변경된 문서만 upsert하는 방식이 필요합니다.
CI/프로덕션에서 기본적으로 실제 Qdrant 사용 — 임베디드 모드는 데모와 테스트에 적합하지만, 여러 프로세스의 동시 액세스에는 서버가 필요합니다.

Available Tools

3 tools

get_documentA

Возвращает полный текст документа из корпуса по его doc_id.

Используй этот инструмент после search_documents(), когда сниппета недостаточно и нужен полный текст найденного документа — например, чтобы процитировать точную формулировку или прочитать раздел, который не попал в сниппет.

Args: doc_id: Идентификатор документа, как он возвращается в поле "doc_id" у search_documents() (совпадает с именем файла в корпусе, например "hybrid-search.md").

Returns: Полный текст документа (markdown). Если doc_id не найден в корпусе, вместо ошибки возвращается строка со списком доступных doc_id, чтобы клиент мог сразу повторить вызов с корректным значением.

ParametersJSON Schema

Name	Required	Description	Default
`doc_id`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.6/5.0

Behavior4/5

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

Since no annotations are provided, the description carries the full burden for behavioral disclosure. It explicitly mentions the graceful error handling: if doc_id is not found, it returns a list of available doc_ids instead of an error, which is key behavioral context. However, it does not mention potential performance implications (e.g., fetching a large document) or any authentication requirements, so it is not perfect.

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

Conciseness4/5

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

The description is reasonably concise, with a clear first sentence stating the main purpose, followed by usage guidance and parameter/returns details. It is structured in sections (Args, Returns) for readability. However, the 'Args:' section is somewhat redundant with the schema, and the text could be slightly more streamlined without losing value.

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 simplicity (one parameter, no annotations) and the presence of an output schema, the description is complete. It covers purpose, usage context, parameter semantics, and return behavior. The graceful error handling is a nice touch that prepares the agent for a non-standard response, making it comprehensive.

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

Parameters4/5

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

The input schema defines doc_id as a string but provides no documentation. The description adds significant meaning by explaining that doc_id matches the field returned by search_documents() and corresponds to the filename in the corpus (e.g., 'hybrid-search.md'), which is not evident from the schema. With 0% schema description coverage, this compensation is crucial and well-executed.

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 the tool's purpose: retrieving the full text of a document from a corpus by its doc_id. It uses a specific verb ('Возвращает') and resource ('документ из корпуса'), and it distinguishes itself from siblings by focusing on fetching full text, while search_documents is for searching snippets.

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?

The description explicitly states when to use this tool: after search_documents() when the snippet is insufficient, and provides concrete examples (quoting exact phrasing or reading sections not in the snippet). It also names the sibling tool search_documents as the prior step, making the workflow clear.

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

rerank_resultsA

Переранжирует переданный список документов-кандидатов относительно query с помощью cross-encoder модели, которая читает пару (query, документ) вместе — точнее, чем независимое векторное сходство, но дороже по compute, поэтому применяется к уже отфильтрованному шортлисту, а не ко всему корпусу.

Используй этот инструмент, когда уже есть список кандидатов (например, из search_documents() или собранный вручную из нескольких запросов) и нужно уточнить их порядок релевантности перед тем, как процитировать top-N пользователю. Для поиска по всему корпусу используй search_documents() — этот инструмент только переупорядочивает уже известные doc_id и не находит новые документы.

Args: query: Запрос, относительно которого переранжируются документы. doc_ids: Список doc_id кандидатов (см. search_documents/ get_document). Неизвестные doc_id молча пропускаются.

Returns: Список [{"doc_id": str, "title": str, "snippet": str, "score": float}, ...], отсортированный по убыванию cross-encoder score. score здесь — необязательно вероятность и несравним по шкале со score из search_documents() (RRF-скор).

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes
`doc_ids`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.5/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses that unknown doc_ids are silently skipped and that scores are not comparable to search_documents scores, which is helpful. However, it doesn't detail side effects (none expected) or performance implications beyond general compute cost, which is mentioned.

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 concise but thorough, with front-loaded purpose, clear usage guidance, parameter details, and return format. Every sentence adds value without redundancy, despite being longer than ideal, it remains structured and readable.

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?

The description is complete for a tool with two simple params, no annotations, and a clear output schema. It explains the return format and score semantics, covers edge cases (unknown ids), and contextualizes the tool's role in the workflow, making it self-sufficient.

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

Parameters4/5

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

Schema description coverage is 0%, but the description provides meaningful semantics for both parameters: query is the reference for re-ranking, doc_ids is the candidate list and indicates unknown ids are skipped. This adds value beyond the minimal 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 specifies the tool re-ranks a provided candidate list relative to a query using a cross-encoder, distinguishing it from direct similarity search. It explicitly states it does not find new documents, differentiating it from sibling tools like search_documents and get_document.

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 when-to-use (when a candidate list already exists) and when-not-to-use (for corpus-wide search, use search_documents). Mentions alternatives and gives a specific scenario (before quoting top-N to the user), making usage guidance clear.

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

search_documentsA

Гибридный поиск (dense-эмбеддинги + BM25, слитые через Reciprocal Rank Fusion) по проиндексированному корпусу markdown-документов о RAG, поиске и LLM-инфраструктуре.

Используй этот инструмент, когда нужно найти релевантные документы или факты по теме или вопросу пользователя, прежде чем отвечать — вместо того, чтобы полагаться на собственные знания. Одинаково хорошо работает и для семантических запросов на естественном языке ("как оценивать качество ретривера"), и для точных терминов/аббревиатур ("RRF", "BM25"), поскольку сочетает векторный и лексический поиск.

Args: query: Запрос на естественном языке или ключевые слова. top_k: Сколько документов вернуть (по умолчанию 5, максимум 20).

Returns: Список словарей, отсортированный по убыванию релевантности — [{"doc_id": str, "title": str, "snippet": str, "score": float}, ...]. doc_id — идентификатор документа, который нужно передать в get_document(), чтобы получить его полный текст, или в rerank_results(), чтобы уточнить порядок кандидатов.

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes
`top_k`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations provisioned, the description carries the full explanation burden. It does well by disclosing the dense+BM25 RRF behavior, a relevance-sorted return structure, and downstream integration via doc_id to get_document or rerank_results. It maybe does not explicitly state side-effect safety or permissions, but such considerations are minimal for a search operation.

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: a front-loaded explanation of the retrieval mechanism, followed by concrete usage guidance and a neat Args/Returns block. Every sentence completes the main explanation of what the tool does, when to use it, how it behaves, and what the caller receives.

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 output schema, sibling tools, and search-oriented complexity, the description provides a practically complete flow: search for candidates, receive relevant snippets and scores, then optionally pass doc_id to get_document for full content or rerank_results for reranking. This is sufficient contextual guidance for agentic use.

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?

While the schema description coverage is 0%, the description fully explains both parameters: query is a natural-language request or keywords, and top_k is the number of documents to return with a default of 5 and maximum of 20. This adds strong semantic value beyond the bare input 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 a specific verb and resource: hybrid search over an indexed corpus of markdown documents about RAG, retrieval, and LLM infrastructure. It distinguishes this tool from siblings like get_document and rerank_results by making search and retrieval its primary role.

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

Usage Guidelines4/5

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

The description gives explicit direction: use this tool to find relevant documents or facts before answering instead of relying on internal knowledge. It also explains suitability for semantic queries and exact terms; however, it does not explicitly describe when not to use this tool versus its siblings.

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.

3 tool updatesv0.1.0Aug 19, 2026
- First observedget_document
- First observedrerank_results
- First observedsearch_documents

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

A4.7/5.0

Scored 2026-08-19 22:05 across 3 tools

Disambiguation5/5

Tools have clear boundaries: search_documents retrieves candidates, get_document fetches full text by ID, rerank_results reorders given IDs. No overlap in purpose, and each description explicitly states when to use it.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (search_documents, get_document, rerank_results) in snake_case. Naming convention is uniform and predictable.

Tool Count5/5

Three tools is well-scoped for a focused retrieval server: search, fetch full content, and re-rank. Each tool serves a distinct step in the pipeline without redundancy, fitting the typical 3-15 range.

Completeness5/5

The tool surface covers the full read-only retrieval workflow: hybrid search with snippets, full document access, and optional cross-encoder re-ranking. No missing operations are needed for the stated purpose.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

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RAG Systems Search
DevNexsler
F
license
Not graded
quality
B
maintenance
Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.
Updated 4 days ago (2026-10-08 18:40 UTC)
4
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RAG MCP Server
RAG Systems Vector Databases Search
devsinsight
F
license
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quality
D
maintenance
Indexes PDF documents into Qdrant and exposes semantic search as MCP tools, enabling RAG-based interactions with your documents.
Updated 3 months ago (2026-06-29 05:23 UTC)
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mcp-rag-assistant
RAG Systems Search
EdwinIN01
F
license
Not graded
quality
C
maintenance
Provides RAG-based knowledge retrieval and document management as MCP tools, supporting hybrid search, reranking, and retrieval process visualization.
Updated a month ago (2026-09-14 06:12 UTC)
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mcp-rag-search
RAG Systems Search
guo-yb-123
F
license
Not graded
quality
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maintenance
Enables agent tools like Claude Code and GitHub Copilot to perform knowledge retrieval using hybrid search (BM25 + dense) with reranking, via MCP protocol.
Updated a month ago (2026-09-02 03:00 UTC)
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