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metamind-vault-rag

by Nikitzu

metamind-vault-rag

마크다운 디렉터리를 위한 검색 엔진입니다. 파일을 감시하고 증분적으로 인덱싱하며, 해당 파일들에 대한 하이브리드 검색 쿼리에 응답합니다.

벡터는 sqlite-vec에, 키워드는 SQLite FTS5에 저장되며, 두 결과는 reciprocal rank fusion으로 결합됩니다. 임베딩은 fastembed의 ONNX 모델을 통해 프로세스 내에서 실행되므로, 서버를 세울 필요도 API 키를 보관할 필요도 없습니다. rerank 추가 기능을 통해 선택적 cross-encoder 재점수 계층을 사용할 수 있습니다.

설치

uv tool install metamind-vault-rag

Related MCP server: mnemonic

진입점

명령어

용도

metamind-vault-rag-watcher

디렉터리를 감시하고 변경 사항을 인덱싱

metamind-vault-rag-indexer

일회성 전체 재인덱싱

metamind-vault-rag-http

루프백 HTTP 검색 API

metamind-vault-rag-server

stdio MCP 서버

metamind-vault-rag-doctor

환경 및 인덱스 진단

구성

변수

의미

VAULT_PATH

인덱싱할 디렉터리

VAULT_COLLECTION

컬렉션 이름. 인덱스 파일의 범위를 지정함

VAULT_HTTP_PORT

루프백 검색 API용 포트

VAULT_STATE_DIR

인덱스, 캐시, 로그가 기록되는 위치. 기본값은 ~/.vault-rag

인덱스는 상태 디렉터리에 컬렉션 이름으로 기록되며, 절대 말뭉치 내부에 배치되지 않습니다. 서로 다른 컬렉션이나 서로 다른 상태 디렉터리를 가리키는 두 클라이언트는 서로를 알지 못한 채 한 머신에서 공존할 수 있습니다.

소비자

서비스를 실행하지 않고 검색이 필요한 모든 클라이언트가 설치합니다. 엔진은 누가 요청하는지에 대해 어떤 의견도 갖지 않습니다. 출력에서 클라이언트를 지칭하지 않으며, 모든 환경 변수는 VAULT_ 접두사를 사용하고, 상태 디렉터리 밖에는 아무것도 쓰지 않습니다.

개발

uv run --extra dev pytest

클라이언트는 uv tool install --from /path/to/this/repo metamind-vault-rag를 사용하여 릴리스 대신 작업 복사본을 가리킬 수 있습니다.

라이선스

MIT

Available Tools

3 tools
search_vaultC

Semantic search over the Obsidian Knowledge vault.

ParametersJSON Schema
NameRequiredDescriptionDefault
kNo
queryYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.1/5.0
Behavior2/5

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

Annotations are absent, so the description should fully disclose behavioral aspects. It merely states 'semantic search' – which implies a read operation but does not explicitly state side effects, resource constraints, or result handling. No details on output stability, rate limits, or side effects are given. Minimal transparency.

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

Conciseness2/5

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

The description is a single sentence, which is concise. However, it is under-specified: it lacks necessary detail for an agent to use the tool effectively. The brevity is not a virtue because it omits critical information, making it more of an under-specification than a well-structured concise entry.

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

Completeness2/5

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

The tool has two parameters and an output schema, but the description gives no information about return format, pagination, or semantics. Since annotations are missing, the description must bear the burden of explaining expected behavior. It fails to provide enough context to use the tool safely, especially for a semantic search that could have variable behavior.

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

Parameters1/5

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

Schema description coverage is 0%, so the description is the only source of parameter meaning. The description does not mention 'query' or 'k' at all. The schema lists 'k' with a default but no description, and 'query' without context. This is a complete failure to provide any parameter semantics in the description.

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

Purpose3/5

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

The description states it performs 'semantic search' over the vault, which clearly identifies the action and resource. However, it does not differentiate from sibling tools like 'related_notes' or 'expand_search' – the term 'semantic' hints at a method but not why this one is distinct. Purpose is clear but not enriched with scope or contrast.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus the siblings. The description only states what it does, not when it is the right choice. There is no mention of use cases, exclusions, or alternative recommendations.

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. 3 tool updatesv0.10.1
    • First observedexpand_search
    • First observedrelated_notes
    • First observedsearch_vault

TDQS

C2.7/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: semantic search, finding related notes, and expanding search via wikilinks. There is no overlap or ambiguity.

Naming Consistency4/5

All tools use snake_case and follow a verb-like pattern, but 'related_notes' uses an adjective rather than a verb, creating minor inconsistency with the verb_noun style of the other two.

Tool Count5/5

With only 3 tools, the set is minimal but well-scoped for the server's focus on vault search and navigation. It avoids superfluous tools while covering essential actions.

Completeness5/5

The server covers the core needs of a RAG vault assistant: searching semantically, exploring relationships, and expanding through linked notes. This is a complete set for its intended purpose.

Maintenance

ActivitySlowing
ResponsivenessNo issues

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