Local RAG
MCP Local RAG
MCP 클라이언트나 터미널에서 개인 문서를 검색하되, 문서를 임베딩 API로 전송하지 않습니다.
mcp-local-rag는 사용자 컴퓨터의 PDF, DOCX, Markdown, 텍스트 파일을 인덱싱합니다. 검색은 의미적 유사성과 키워드 매칭을 결합하므로, 쿼리가 의도와 API 이름, 클래스 이름, 오류 코드 같은 정확한 기술 용어를 모두 매칭할 수 있습니다.
기능
로컬에서 실행: 문서 파싱, 임베딩, 저장, 검색이 모두 사용자 컴퓨터에서 실행됩니다. 초기 모델 다운로드 이후에는 텍스트 수집과 검색이 오프라인으로 동작합니다.
하이브리드 검색: 의미적 검색이 관련 개념을 찾고, 키워드 매칭이 정확한 기술 용어를 강조합니다.
구성 가능한 임베딩: 문서의 언어와 도메인에 맞는 Hugging Face 임베딩 모델을 선택할 수 있습니다.
의미 기반 청킹: 문서가 고정된 문자 수가 아닌 주제 경계에서 분할됩니다. Markdown 코드 블록은 그대로 유지됩니다.
MCP 및 CLI: AI 코딩 도구나 터미널에서 직접 동일한 인덱스를 사용할 수 있습니다.
API 키, Docker, Python, 외부 데이터베이스가 필요 없습니다.
Related MCP server: cowork-semantic-search
빠른 시작
요구 사항
Node.js 22 이상
npm 패키지와 임베딩 모델 다운로드를 위한 최초 사용 시 인터넷 연결
검색하려는 문서가 들어 있는 디렉터리
BASE_DIR을 해당 디렉터리로 설정하세요. 이 디렉터리는 파일 작업의 보안 경계이기도 합니다. 아래의 /absolute/path/to/your/documents를 디렉터리의 절대 경로로 바꾸세요.
mcp-local-rag는 로컬 stdio 서버를 통해 표준 MCP 프로토콜을 사용하므로, 로컬 MCP 서버를 지원하는 AI 코딩 도구 및 기타 MCP 호스트에서 작동합니다.
아래 예시 중 하나를 사용하거나, npx -y mcp-local-rag를 등록하고 클라이언트의 MCP 구성 형식에 맞춰 BASE_DIR을 설정하세요.
Claude Code의 경우: 다음 명령을 실행하세요:
claude mcp add local-rag --scope user --env BASE_DIR=/absolute/path/to/your/documents -- npx -y mcp-local-ragCodex의 경우: ~/.codex/config.toml에 추가하세요:
[mcp_servers.local-rag]
command = "npx"
args = ["-y", "mcp-local-rag"]
[mcp_servers.local-rag.env]
BASE_DIR = "/absolute/path/to/your/documents"OpenCode의 경우: ~/.config/opencode/opencode.json(또는 opencode.jsonc)에 추가하세요:
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"local-rag": {
"type": "local",
"command": ["npx", "-y", "mcp-local-rag"],
"environment": {
"BASE_DIR": "/absolute/path/to/your/documents"
}
}
}
}Cursor의 경우: ~/.cursor/mcp.json에 추가하세요:
{
"mcpServers": {
"local-rag": {
"command": "npx",
"args": ["-y", "mcp-local-rag"],
"env": {
"BASE_DIR": "/absolute/path/to/your/documents"
}
}
}
}클라이언트를 다시 시작한 다음, 인덱스 구축을 요청하세요:
Sync all documents in the configured root and wait until it finishes.첫 번째 동기화는 기본 임베딩 모델(약 90MB)을 다운로드하며, 수집이 시작되기 전에 1~2분이 걸릴 수 있습니다. 이후 실행에서는 로컬 캐시를 사용합니다.
동기화가 완료되면:
What does the API documentation say about authentication?CLI 빠른 시작
MCP 클라이언트 없이 CLI를 사용하려면:
npx mcp-local-rag ingest ./docs/
npx mcp-local-rag query "authentication API"CLI는 기본적으로 현재 디렉터리를 문서 루트로 사용합니다. 두 명령을 같은 디렉터리에서 실행하여 동일한 기본 인덱스를 사용하거나, BASE_DIR과 DB_PATH를 명시적으로 설정하세요.
왜 이런 도구가 필요한가
일부 문서 세트는 기밀성이나 조직 정책 때문에 호스팅형 임베딩 서비스로 보낼 수 없습니다. 인덱스를 로컬에 유지하면 쿼리당 API 비용을 추가하지 않고도 검색이 가능해집니다.
의미적 검색만으로는 기술 문서에서 중요한 정확한 식별자를 놓칠 수 있습니다. 키워드 재순위화는 자연어 검색을 포기하지 않으면서도 이러한 용어를 계속 표시되게 합니다.
지원 콘텐츠
입력 | 수집 방법 |
PDF, DOCX, TXT, Markdown | 파일 수집 또는 디렉터리 동기화 |
클라이언트가 이미 가져온 HTML |
|
메모리에 있는 일반 텍스트 또는 Markdown | 안정적인 소스 식별자를 사용한 |
HTML 가져오기는 서버에 내장되어 있지 않습니다. MCP 클라이언트가 페이지를 가져와 해당 HTML을 ingest_data에 전달할 수 있습니다.
Excel, PowerPoint, 단독 이미지, 소스 코드 파일 확장자는 파일 수집에서 지원되지 않습니다. PDF는 선택적으로 로컬 비전 모델을 사용하여 그림을 설명할 수 있지만, 이는 OCR이나 이미지 검색이 아닙니다.
MCP 도구
도구 | 용도 |
| 구성된 모든 루트 또는 하나의 경로와 인덱스를 조정 |
| 실행 중인 동기화 작업 폴링 |
| 파일 하나 수집 또는 교체 |
| 클라이언트가 이미 보유한 텍스트, Markdown 또는 HTML 수집 |
| 의미 매칭과 키워드 부스트로 검색 |
| 검색 결과에서 주변 청크 읽기 |
| 지원되는 파일과 수집 상태 표시 |
| 인덱싱된 파일 또는 |
| 인덱스 및 검색 상태 표시 |
문서 루트 동기화
sync_start는 새 파일과 변경된 파일을 수집하고, 바이트 단위로 동일한 파일은 건너뛰며, 더 이상 존재하지 않는 파일의 인덱스 항목을 제거합니다:
Sync everything under the configured document roots and wait for completion.이 도구는 즉시 jobId를 반환합니다. 클라이언트는 sync_status를 폴링하여 상태가 succeeded 또는 failed가 될 때까지 기다려야 합니다. 동기화는 시각적 캡션을 생성하지 않습니다. MCP 서버 환경에서 STORE_IMAGES=true를 설정하면 동기화가 선택한 새 파일 또는 변경된 파일에 대해 지원되는 PDF 및 DOCX 이미지를 저장합니다. 변경되지 않은 파일은 계속 건너뜁니다.
서버 프로세스에는 하나의 동기화 작업만 유지됩니다. 새 작업이 완료된 레코드를 대체하며, 서버를 다시 시작하면 해당 레코드는 삭제됩니다.
파일 하나 수집
ingest_file은 PDF, DOCX, TXT, Markdown을 허용합니다. MCP 파일 경로는 절대 경로여야 하며 구성된 문서 루트 안에 있어야 합니다:
Ingest the document at /Users/me/docs/api-spec.pdf.동일한 경로를 다시 수집하면 기존 청크가 대체됩니다.
검색 및 추가 컨텍스트 읽기
What does the API documentation say about authentication?
Find the documented behavior of ERR_CONNECTION_REFUSED.결과에는 텍스트, 소스 경로, 제목, 청크 인덱스, 관련성 점수, 해당 청크에 저장된 이미지가 포함됩니다. MCP는 각 이미지를 결과 식별자와 쌍을 이루는 이미지 콘텐츠 블록으로 반환합니다. CLI query는 모든 결과에 { imageIndex, mimeType, data } 형식의 images 배열을 포함합니다. 답변에 더 많은 컨텍스트가 필요할 때 결과의 chunkIndex와 filePath 또는 source를 read_chunk_neighbors에 전달하세요:
Read the surrounding chunks for that authentication result.query_documents와 list_files 모두 선택적 절대 scope 경로 접두사 또는 접두사 목록을 허용합니다. 접두사는 정확한 경로와 그 하위 항목을 매칭합니다.
HTML 수집
MCP 클라이언트가 페이지를 가져온 후 ingest_data를 사용하세요:
Fetch https://example.com/docs and ingest the HTML.서버는 본문 기사를 추출하여 Markdown으로 변환하고, 제공된 소스 식별자 아래에 저장합니다. 동일한 소스를 재사용하면 기존 콘텐츠가 업데이트됩니다.
외부 콘텐츠를 인덱싱할 때는 소스 사이트의 이용 약관과 저작권을 존중하세요.
PDF 시각적 캡션 및 저장된 이미지
시각 모드는 그림이 많은 PDF 페이지에 생성된 캡션을 추가합니다. 이는 선택 기능이며 일반 수집 중에는 비전 모델을 로드하지 않습니다.
Ingest /Users/me/docs/research-paper.pdf with visual: true.npx mcp-local-rag ingest ./docs/research-paper.pdf --visual이미지 저장은 시각적 캡션과 독립적입니다. MCP 서버에 STORE_IMAGES=true를 설정하거나 CLI 수집 및 동기화에 --images를 전달하세요:
npx mcp-local-rag ingest ./docs/research-paper.pdf --images
npx mcp-local-rag sync ./docs/ --imagesPDF 저장은 감지된 그림/표 영역을 사용합니다. DOCX 저장은 기존 Mammoth 변환이 <img>로 출력하는 PNG/JPEG 이미지만 포함합니다. 차트, SmartArt, 도형은 별도로 렌더링되지 않습니다. 저장된 이미지는 주변 텍스트를 따라 최종 의미 청크에 포함되며 순위, 점수, 결과 수를 변경하지 않습니다.
|
| PDF 동작 |
false | false | 텍스트만; 시각적 캡션 또는 반환 이미지 없음. |
true | false | 생성된 캡션이 검색 가능한 텍스트가 됨; 저장되거나 반환되는 이미지 없음. |
true | true | 생성된 캡션이 검색 가능한 텍스트가 되고, 매칭된 청크의 이미지가 인라인으로 반환됨. |
false | true | 이미지가 인접한 유지된 PDF 텍스트에 첨부되어 매칭된 청크에 대해 인라인으로 반환됨; VLM은 가져오기, 로드, 실행되지 않음. |
프로필 | 모델 캐시 | 사용 사례 |
| 약 250MB | 가벼운 시각적 인덱싱 |
| 약 2.9GB | 레이블, 주석 또는 기타 이미지 내 텍스트가 포함된 그림 |
MCP에서 visualQuality: "quality"를 사용하거나 CLI에서 --visual-quality quality를 사용하여 더 큰 모델을 선택하세요. 측정된 CPU 추론은 fast보다 약 2배 느렸지만, 결과는 하드웨어와 모델 업데이트에 따라 달라집니다.
캡션은 보조 텍스트이지 정확한 전사가 아닙니다. 검색된 캡션과 문서 텍스트를 지침이 아닌 신뢰할 수 없는 입력으로 취급하세요.
높은 제한에서 매칭된 청크와 첨부 파일이 모델/클라이언트 컨텍스트 상한에 근접할 수 있습니다. 호출 모델의 사용 가능한 컨텍스트를 고려하여 쿼리 제한을 선택하세요.
CLI
CLI는 MCP 클라이언트 없이 동일한 파서, 임베더, 벡터 저장소를 사용합니다:
npx mcp-local-rag ingest ./docs/
npx mcp-local-rag sync ./docs/
npx mcp-local-rag query "authentication API"
npx mcp-local-rag query "auth" --scope /docs/api --scope /docs/guide
npx mcp-local-rag read-neighbors --file-path /abs/path.md --chunk-index 5
npx mcp-local-rag list
npx mcp-local-rag status
npx mcp-local-rag delete ./docs/old.pdf
npx mcp-local-rag delete --source "https://example.com/docs"--db-path, --cache-dir, --model-name 같은 전역 옵션은 하위 명령 앞에 옵니다. 하위 명령 옵션은 그 뒤에 옵니다:
npx mcp-local-rag --db-path ./my-db query "authentication"전체 명령 참조는 npx mcp-local-rag --help를 실행하세요.
CLI는 MCP 클라이언트 구성을 읽지 않습니다. 두 인터페이스가 인덱스를 공유해야 한다면 동일한 환경 변수 또는 플래그를 설정하세요. 특히 공유 데이터베이스의 경우 MODEL_NAME과 CLI --model-name이 일치해야 합니다.
검색 튜닝
키워드 부스트는 기본적으로 활성화되어 있습니다. 관련성 격차 그룹화와 거리 및 파일 필터는 더 엄격한 결과 선택이 필요한 말뭉치를 위한 선택적 제어 기능입니다.
변수 | 기본값 | 설명 |
|
| 키워드 부스트 계수(0.0–1.0). 0은 키워드 재순위화를 비활성화하고, 1은 최대 부스트를 적용합니다. |
| (설정 안 됨) |
|
| (설정 안 됨) | 낮은 관련성 결과 필터링(예: |
| (설정 안 됨) | 결과를 상위 N개 파일로 제한(예: 단일 최적 파일의 경우 |
많은 식별자가 포함된 API 사양 및 기타 문서의 경우, 더 강한 키워드 가중치가 정확한 용어 순위를 개선할 수 있습니다:
"env": {
"RAG_HYBRID_WEIGHT": "0.7"
}0.7: 기본값보다 약간 더 강한 정확 용어 재순위화1.0: 최대 키워드 가중치
작동 방식
수집(ingestion) 중:
파서가 입력 형식에서 텍스트를 추출합니다.
의미론적 청커(semantic chunker)가 주제 경계를 찾고 Markdown 코드 블록을 보존합니다.
Transformers.js가 로컬에서 임베딩을 생성합니다.
LanceDB가 청크, 메타데이터, 벡터, 전체 텍스트 인덱스를 저장합니다.
검색 중:
쿼리가 동일한 모델로 임베딩됩니다.
벡터 검색이 의미론적으로 관련된 청크를 검색합니다.
구성된 경우 선택적 거리 및 관련성 그룹 필터가 후보를 좁힙니다.
전체 텍스트 일치가 정확한 쿼리 용어를 강화합니다.
에이전트 스킬
Agent Skills는 AI 어시스턴트에게 쿼리 및 수집 지침을 제공합니다:
npx mcp-local-rag skills install --claude-code
npx mcp-local-rag skills install --claude-code --global
npx mcp-local-rag skills install --codex설치된 스킬은 쿼리 구성, 결과 정제, HTML 수집을 다룹니다. 자동으로 활성화되지 않으면 어시스턴트에게 mcp-local-rag 스킬을 명시적으로 사용하도록 요청하세요.
구성
MCP 서버는 환경 변수를 읽습니다. CLI는 나열된 전역 환경 변수와 플래그를 허용합니다.
CLI 수집 및 동기화 시 이미지 저장은 --images로만 활성화됩니다.
환경 변수 | CLI 플래그 | 기본값 | 설명 |
|
| 현재 디렉터리 | 문서 루트 하나; CLI 플래그는 |
| 해당 없음 | (설정 안 됨) | 문서 루트의 JSON 배열; |
|
|
| 벡터 데이터베이스 위치 |
|
|
| 모델 캐시 디렉터리 |
|
|
| Hugging Face 임베딩 모델 |
|
|
| 최대 파일 크기(바이트) |
|
|
| 최소 청크 길이(문자, 1–10000) |
| 해당 없음 |
| MCP 서버 전용: 지원되는 PDF/DOCX 이미지를 저장하고 일치하는 청크와 함께 반환합니다. CLI는 |
| 해당 없음 |
| ONNX Runtime 실행 장치 |
| 해당 없음 |
| 선택한 모델에 전달되는 임베딩 데이터 타입 |
문서 루트 (BASE_DIR 및 BASE_DIRS)
mcp-local-rag는 구성된 루트 내에서만 파일 작업을 허용합니다. 여러 루트의 경우
BASE_DIRS는 비어 있지 않은 경로의 JSON 배열이어야 합니다:
export BASE_DIRS='["/Users/me/Documents/work","/Users/me/Projects/specs"]'루트 구성은 다음 순서로 해석됩니다:
CLI
--base-dir <path>플래그 (ingest,list,sync에서 반복 가능)BASE_DIRSBASE_DIR현재 디렉터리
각 소스는 병합하는 대신 우선순위가 낮은 소스를 대체합니다. 잘못된 BASE_DIRS
구성은 BASE_DIR 또는 현재 디렉터리로 대체되지 않고 실패합니다. status는
MCP에서 계속 사용할 수 있으므로 클라이언트가 구성 오류를 보고할 수 있습니다.
npx mcp-local-rag ingest --base-dir /Users/me/work --base-dir /Users/me/specs /Users/me/work/readme.md
npx mcp-local-rag list --base-dir /Users/me/work --base-dir /Users/me/specs
npx mcp-local-rag sync --base-dir /Users/me/work --base-dir /Users/me/specs
BASE_DIRS='["/Users/me/work","/Users/me/specs"]' npx mcp-local-rag list저장소 및 모델
DB_PATH와 CACHE_DIR는 기본적으로 프로세스 작업 디렉터리를 기준으로 합니다. MCP
클라이언트가 다른 프로젝트 디렉터리에서 서버를 시작할 수 있는 경우 절대 경로를 설정하세요.
문서의 언어와 도메인에 맞는 Hugging Face 임베딩 모델을 선택하려면 MODEL_NAME을
설정하거나 --model-name을 전달하세요.
mcp-local-rag는 평균 풀링(mean pooling)과 L2 정규화로 임베딩을 생성합니다. 모델을 선택할 때 풀링 방식이 검색 품질에 영향을 줄 수 있으므로 이러한 설정이 권장 추론 설정과 일치하는지 확인하세요.
MODEL_NAME, RAG_DEVICE 또는 RAG_DTYPE을 변경하면 기존 벡터가 호환되지 않을 수
있습니다. 임베딩 구성을 변경한 후 새 DB_PATH를 사용하거나 기존 인덱스를 삭제하고
다시 수집하세요.
영어 문서용 모델 예시는 Xenova/bge-small-en-v1.5입니다.
보안 및 운영
파일 접근은
BASE_DIR,BASE_DIRS또는 CLI--base-dir루트로 제한됩니다.구성된 모든 루트 외부로 확인되는 심볼릭 링크는 거부됩니다.
문서 처리 및 검색은 필수 모델이 캐시된 후에는 네트워크 요청을 하지 않습니다.
서버는 단일 로컬 사용자를 위해 설계되었으며 인증이나 접근 제어를 제공하지 않습니다.
동일한
DB_PATH에 대해 여러 CLI 또는 MCP 작성자를 실행하지 마세요. 동기화가 활성화된 동안 읽기 전용 쿼리는 실행할 수 있습니다.작성자가 활성화되지 않은 동안
DB_PATH디렉터리를 복사하여 인덱스를 백업하세요.
"결과 없음"
문서를 먼저 수집해야 합니다. "List all ingested files"를 실행하여 확인하세요.
모델 다운로드 실패
인터넷 연결을 확인하세요. 프록시 뒤에 있는 경우 네트워크 설정을 구성하세요. 모델은 수동으로 다운로드할 수도 있습니다.
"파일이 너무 큼"
기본 제한은 100MB입니다. 큰 파일을 분할하거나 MAX_FILE_SIZE를 늘리세요.
쿼리 속도 저하
status로 청크 수를 확인하세요. 청크가 많은 대용량 문서는 쿼리를 느리게 할 수 있습니다. 매우 큰 파일은 분할하는 것을 고려하세요.
"BASE_DIR 외부 경로"
파일 경로가 구성된 루트(BASE_DIR, BASE_DIRS 항목 또는 CLI --base-dir) 중 하나 내에 있는지 확인하세요. 절대 경로를 사용하세요.
"BASE_DIRS는 JSON 배열이어야 합니다..."
BASE_DIRS는 하나 이상의 비어 있지 않은 경로 문자열의 JSON 배열을 허용합니다:
유효:
BASE_DIRS='["/Users/me/work","/Users/me/specs"]'무효:
BASE_DIRS=/a:/b(구분자 구문은 지원되지 않음)무효:
BASE_DIRS='[]'(빈 배열)
MCP 클라이언트가 도구를 인식하지 못함
구성 파일 구문 확인
클라이언트를 완전히 다시 시작 (Mac의 Cursor는 Cmd+Q)
직접 테스트:
npx mcp-local-rag가 오류 없이 실행되어야 함
기여
기여를 환영합니다! 설정 및 지침은 CONTRIBUTING.md를 참조하세요.
라이선스
MIT 라이선스. 개인 및 상업적 사용에 무료입니다.
블로그 게시물
Building a Local RAG for Agentic Coding: 의미론적 청킹과 하이브리드 검색 설계에 대한 기술 심층 분석.
감사의 말
Anthropic의 Model Context Protocol, LanceDB, Transformers.js로 구축되었습니다.
Available Tools
9 toolsdelete_fileA
Delete a previously ingested file or data from the vector database. Use filePath for files ingested via ingest_file, or source for data ingested via ingest_data. Either filePath or source must be provided. Returns deleted (operation succeeded), removedChunks, and existed (whether anything was actually present).
| Name | Required | Description | Default |
|---|---|---|---|
| source | No | Source identifier used in ingest_data. Examples: "https://example.com/page", "clipboard://2024-12-30" | |
| filePath | No | Absolute path to the file (for ingest_file). Example: "/Users/user/documents/manual.pdf" |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden. Mentions return fields but does not disclose side effects, permissions, or error cases (e.g., what happens if nothing matches).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences with no redundancy. Purpose, usage, and return are clearly separated and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, parameters, constraints, and return values. Lacks explanation of edge cases (both params provided or neither) but is generally sufficient given tool simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, yet description adds context by linking each parameter to the specific ingestion method and clarifying the mutual exclusivity requirement, which is not in schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the action (delete) and object (previously ingested file/data from vector database). Distinguishes from sibling tools which are for ingestion, listing, querying, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs when to use filePath vs. source and states that at least one must be provided. Could further specify behavior if both are given or if the item does not exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ingest_dataA
Ingest in-memory content as a string (use ingest_file for files on disk). The source identifier enables re-ingestion to update existing content. Returns { filePath, chunkCount, timestamp, fileTitle }.
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | The content to ingest (text, HTML, or Markdown) | |
| metadata | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses return format but does not discuss side effects, idempotency, or rate limits. Adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no waste: first sentence states purpose and sibling alternative, second sentence adds key behavioral detail and return format. Front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given nested object parameters and no output schema, the description covers purpose, parameters with examples, and return values. Lacks error conditions or prerequisites, but sufficient for most agents.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Description adds meaning to both parameters: content format types and detailed metadata source examples. Schema coverage is 50% but description compensates with concrete usage guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description explicitly states it ingests in-memory content as a string and differentiates from ingest_file for files on disk. Specific verb+resource with clear distinction from a sibling tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly mentions when to use this tool ('use ingest_file for files on disk') and hints at re-ingestion capability. Lacks explicit when-not-to-use scenarios, but the sibling distinction is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ingest_fileA
Ingest a document file (PDF, DOCX, TXT, MD) into the vector database. Path must be absolute; re-ingesting the same path replaces its existing data. Returns { filePath, chunkCount, timestamp, fileTitle }.
| Name | Required | Description | Default |
|---|---|---|---|
| visual | No | Run VLM captioning on figure pages (PDF only; default false). | |
| filePath | Yes | Absolute path to the file to ingest. Example: "/Users/user/documents/manual.pdf" | |
| visualQuality | No | VLM profile when visual is true (default "fast"). "quality" is more accurate on figures with in-image text but much heavier and slower. Ignored when visual is false. | fast |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that ingestion is a write operation, that re-ingesting replaces existing data, and that it supports VLM captioning for PDFs with different quality profiles. It also specifies the return structure. This is thorough for a tool of this complexity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences. The first sentence front-loads the main purpose, and the second adds critical behavioral details. No extra words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters, no output schema, and no nested objects, the description covers input requirements (absolute path), behavior (replace on re-ingest), return fields, and an optional feature (VLM captioning). It briefly addresses PDF-only behavior. Missing details like error handling or unsupported file types, but overall sufficient for this complexity level.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds context like 'Path must be absolute' and the effect of re-ingesting, but the schema already describes each parameter adequately. No additional semantic depth beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb 'Ingest', the resource 'document file (PDF, DOCX, TXT, MD)', and the destination 'into the vector database'. It distinguishes from siblings like 'delete_file' and 'list_files' by specifying file ingestion. The mention of absolute path and re-ingest behavior adds specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some usage context: 'Path must be absolute' and 're-ingesting the same path replaces its existing data'. However, it does not explicitly state when to use this tool versus alternatives (e.g., 'ingest_data'), nor does it give exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_filesA
List supported files (PDF, DOCX, TXT, MD) under the configured base directories and whether each is ingested. Returns { baseDirs, files, sources }; sources lists ingested items reported apart from the file scan, chiefly ingest_data content (web pages, clipboard, etc.).
| Name | Required | Description | Default |
|---|---|---|---|
| scope | No | Optional absolute path prefix(es) — one string or a list (unioned) — restricting the listing to files reachable at a path equal to or under a prefix within the base directories. "/docs/api" matches "/docs/api/x.md" but not "/docs/apiv2". Must be absolute (server OS style); a relative prefix matches nothing. A prefix outside every base directory yields an empty files list, so compare it against the baseDirs in the response before concluding no files exist. Scope filters files by their scan path; ingest_data sources, which have no base-directory path, are always listed. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It adds value by explaining that 'sources' contains ingested items like web pages/clipboard, and that files are scanned from base directories. It doesn't explicitly state this is read-only or describe side effects, but the 'list' verb implies safety. Some edge behavior (e.g., invalid scope yielding empty files list) is only visible in the schema, not the description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences: the first states the core purpose, and the second explains the return structure and the 'sources' nuance. There is no redundancy or filler, and the most important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a listing tool with one optional parameter and no output schema, the description sufficiently covers the purpose, return shape, and the non-obvious 'sources' concept. It doesn't need to explain return values in detail since the return shape is stated. Path edge cases are handled in the schema, so the description is complete enough for correct selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description covers 100% of the parameter 'scope' with a detailed explanation of prefix matching and path constraints. The tool description adds no additional parameter semantics beyond what the schema already provides, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('list') and resource ('supported files (PDF, DOCX, TXT, MD)') under configured base directories, plus the ingestion status. This clearly distinguishes it from sibling tools like ingest_file, delete_file, and sync_status, which perform different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies the tool is for inspecting the file inventory and its ingestion status, which is a distinct use case. It also explains the return shape to set expectations. However, it doesn't explicitly mention when not to use it or reference sibling alternatives, though the purpose is clear enough for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_documentsA
Search ingested documents with hybrid keyword + semantic matching. Returns results sorted by relevance, each with filePath, chunkIndex, text, fileTitle, score (0 = best, higher = worse), and source (for ingest_data items).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 10, range 1-20). Lower favors precision, higher recall. | |
| query | Yes | Search query. Preserve specific user terms (for keyword match); add context when the query is vague (for semantic match). | |
| scope | No | Optional absolute path prefix(es) — one string or a list (unioned) — restricting results to a filePath equal to or under a prefix. "/docs/api" matches "/docs/api/auth.md" but not "/docs/apiv2". Must be absolute (server OS style); a relative prefix matches nothing — derive one from a filePath returned by an earlier query, or omit scope. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description details return fields, sorting by relevance, and score meaning (0=best, higher=worse). It lacks pagination details but is generally transparent for a read-only search tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with clear, front-loaded purpose and a concise list of return fields. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description covers purpose, behavior, and return fields comprehensively. Context from sibling tools and parameter count is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed parameter descriptions. The description adds value by listing output fields not present in schema, enhancing parameter context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches ingested documents using hybrid keyword and semantic matching, and lists the return fields. It is distinct from sibling tools like list_files and read_chunk_neighbors.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use (for searching documents) but does not explicitly state when not to use or provide alternatives among siblings. No exclusion criteria mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_chunk_neighborsA
Read the chunks immediately before and after a query_documents result, in the same document, for more surrounding context. Pass chunkIndex from the result plus exactly one of filePath (ingest_file) or source (ingest_data). Returns the target chunk (isTarget: true) and its neighbors, ascending by chunkIndex; an out-of-range chunkIndex returns []. Defaults: before=2, after=2 (max 50 each).
| Name | Required | Description | Default |
|---|---|---|---|
| after | No | Number of chunks to retrieve after the target (0–50, default 2). | |
| before | No | Number of chunks to retrieve before the target (0–50, default 2). | |
| source | No | Source identifier (for ingest_data documents). Provide exactly one of filePath or source. Examples: "https://example.com/page", "clipboard://2024-12-30". | |
| filePath | No | Absolute path to the file (for ingest_file documents). Provide exactly one of filePath or source. Example: "/Users/user/documents/manual.pdf". | |
| chunkIndex | Yes | Zero-based target chunk index (non-negative integer). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It details the behavior (reads neighbors), return structure (target with isTarget: true, ascending order), edge case (out-of-range returns []), and limits (defaults before/after=2, max 50 each). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, concise, and front-loaded with the most important information. No redundant words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description fully covers the tool's behavior, parameter usage, return structure, and edge cases. It ties to the sibling tool query_documents, providing necessary context for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already has 100% coverage with descriptions for all five parameters. The description adds value by explaining the mutual exclusivity of filePath and source, the default values for before and after, and the connection to query_documents for chunkIndex.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the verb ('Read'), the resource ('chunks immediately before and after'), and the context ('in the same document, for more surrounding context'). It ties the tool to query_documents, distinguishing it from siblings like query_documents itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides explicit instructions on parameter usage: pass chunkIndex from query_documents and exactly one of filePath or source. It also states defaults and max limits. However, it doesn't explicitly state when not to use this tool or mention alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
statusA
Get index status: { documentCount, chunkCount, memoryUsage (MB), uptime (s), ftsIndexEnabled, searchMode }.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description does not explicitly state read-only nature or other behavioral traits like cost or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with action, no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, no-output-schema tool, description fully covers functionality and return format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist; description adds value by listing return fields beyond the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Explicitly states it gets index status and lists return fields, clearly distinguishing from sibling tools like delete_file or query_documents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use vs alternatives, but the simple nature (no parameters) makes usage implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sync_startA
Reconcile the index with the files on disk: ingest new and changed files, leave unchanged files alone, and remove index entries for files that are gone. Returns { jobId } without waiting for the run to finish; poll sync_status with that jobId for progress and the final outcome. Only one job is kept, and it is lost when the server process exits.
| Name | Required | Description | Default |
|---|---|---|---|
| path | No | Optional absolute path to a file or directory inside a configured base directory; list_files returns those directories as baseDirs. A file synchronizes only itself and a directory only its own subtree, leaving every path outside it untouched. Omit it to synchronize every configured base directory. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses key behaviors: asynchronous execution (returns jobId without waiting), single-job constraint, and job loss on server exit. It also notes that index entries are removed for missing files, making side effects transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences: the first states the core purpose, the second explains the return value and polling, and the third adds a critical lifecycle constraint. Every sentence contributes essential information with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one optional parameter, no annotations, and no output schema, the description fully captures the tool's behavior, return format, and lifecycle. It also refers to sync_status for progress, completing the operational picture.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has a 100% coverage description for the 'path' parameter, explaining its optionality, scope, and behavior. The tool description adds no extra parameter context, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Reconcile the index with the files on disk' and enumerates specific behaviors (ingest new/changed, leave unchanged, remove gone entries). This distinguishes it from sibling tools like ingest_file or delete_file, which handle single files.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (reconciling an index with disk state) and implicitly contrasts with sync_status for polling. It lacks explicit 'when not to use' statements or alternative tool names, but the context is unambiguous enough for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sync_statusA
Get the current or latest sync job record: { jobId, state ("running" | "succeeded" | "failed"), total (null until scanning has counted the files on disk), completed (upserted + skipped + empty; pruned is counted separately), summary { upserted, skipped, empty, pruned }, warnings, error (null unless the job failed) }. An unknown jobId means the job was replaced by a newer one or lost with a previous server process.
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | Yes | Identifier returned by sync_start. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description takes full responsibility for behavioral disclosure. It transparently explains field nullability (total null until scanning, error null unless failed), the enumerated state values, and the unknown jobId case. This gives the agent a complete picture of expected behavior and edge cases.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense but intentionally structured to mirror the returned object, making the field relationships clear. The second sentence adds essential edge-case information without fluff. It is compact given the amount of detail about the response shape.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema, the description fully defines every return field and its conditional behavior, including a nested summary object. It also references sync_start and server process loss to situate usage, making the tool self-sufficient in context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already covers jobId ('Identifier returned by sync_start') at 100% coverage. The description adds extra meaning beyond the schema by explaining the consequences of an unknown jobId, which enriches the parameter's semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Get the current or latest sync job record', providing a specific verb+resource. It further details the exact output shape including state values, total/completed semantics, and nested summary, clearly distinguishing it from sibling tools like sync_start.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies usage: after starting a sync job, call this to retrieve its status. It explains the meaning of an unknown jobId (replaced or lost with server process), which guides the agent on interpreting results. However, it does not explicitly name alternatives or state when not to use this tool.
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. Dates show when Glama detected each change.
3 tool updates
v0.17.3- Changed
list_files1 field changed- changed
Input schema / properties / scope / descriptionPrevious value: -"Optional absolute path prefix(es) — one string or a list (unioned) — restricting the listing to files reachable at a path equal to or under a prefix within the base directories. \"/docs/api\" matches \"/docs/api/x.md\" but not \"/docs/apiv2\". Must be absolute (server OS style); a relative prefix matches nothing. Scope filters files by their scan path; ingest_data sources, which have no base-directory path, are always listed."New value: +"Optional absolute path prefix(es) — one string or a list (unioned) — restricting the listing to files reachable at a path equal to or under a prefix within the base directories. \"/docs/api\" matches \"/docs/api/x.md\" but not \"/docs/apiv2\". Must be absolute (server OS style); a relative prefix matches nothing. A prefix outside every base directory yields an empty files list, so compare it against the baseDirs in the response before concluding no files exist. Scope filters files by their scan path; ingest_data sources, which have no base-directory path, are always listed."
- Added
sync_start - Added
sync_status
1 tool update
v0.16.1- Changed
list_files1 field changed- added
Input schema / properties / scopeAdded value: +{ + "description": "Optional absolute path prefix(es) — one string or a list (unioned) — restricting the listing to files reachable at a path equal to or under a prefix within the base directories. \"/docs/api\" matches \"/docs/api/x.md\" but not \"/docs/apiv2\". Must be absolute (server OS style); a relative prefix matches nothing. Scope filters files by their scan path; ingest_data sources, which have no base-directory path, are always listed.", + "oneOf": [ + { + "type": "string" + }, + { + "items": { + "type": "string" + }, + "type": "array" + } + ] +}
4 tool updates
v0.15.3- Changed
ingest_data1 field changed- changed
Input schema / properties / metadata / properties / format / descriptionPrevious value: -"Content format: \"text\", \"html\", or \"markdown\""New value: +"Content format: text (plain/copied text), html (fetched web pages), or markdown."
- Changed
ingest_file2 fields changed- changed
Input schema / properties / visual / descriptionPrevious value: -"If true and the file is a PDF, run VLM captioning on figure pages. No effect on non-PDF files."New value: +"Run VLM captioning on figure pages (PDF only; default false)." - changed
Input schema / properties / visualQuality / descriptionPrevious value: -"VLM profile to use when visual is true. \"fast\" (default) is the lightweight SmolVLM-256M; \"quality\" is Qwen2.5-VL-3B-Instruct-ONNX with higher fidelity on figures with in-image text (~10x model-cache footprint, ~2x per-page inference). The server also accepts an empty string as a synonym for omitted (normalized to \"fast\"). Silently ignored when visual is false."New value: +"VLM profile when visual is true (default \"fast\"). \"quality\" is more accurate on figures with in-image text but much heavier and slower. Ignored when visual is false."
- Changed
query_documents3 fields changed- changed
Input schema / properties / limit / descriptionPrevious value: -"Maximum number of results to return (default: 10, range: 1-20). Recommended: 5 for precision, 10 for balance, 20 for broad exploration."New value: +"Max results (default 10, range 1-20). Lower favors precision, higher recall." - changed
Input schema / properties / query / descriptionPrevious value: -"Search query. Include specific terms and add context if needed."New value: +"Search query. Preserve specific user terms (for keyword match); add context when the query is vague (for semantic match)." - added
Input schema / properties / scopeAdded value: +{ + "description": "Optional absolute path prefix(es) — one string or a list (unioned) — restricting results to a filePath equal to or under a prefix. \"/docs/api\" matches \"/docs/api/auth.md\" but not \"/docs/apiv2\". Must be absolute (server OS style); a relative prefix matches nothing — derive one from a filePath returned by an earlier query, or omit scope.", + "oneOf": [ + { + "type": "string" + }, + { + "items": { + "type": "string" + }, + "type": "array" + } + ] +}
- Changed
read_chunk_neighbors2 fields changed- changed
Input schema / properties / filePath / descriptionPrevious value: -"Absolute path to the file (for documents ingested via ingest_file). Example: \"/Users/user/documents/manual.pdf\". Provide either filePath or source, not both."New value: +"Absolute path to the file (for ingest_file documents). Provide exactly one of filePath or source. Example: \"/Users/user/documents/manual.pdf\"." - changed
Input schema / properties / source / descriptionPrevious value: -"Source identifier used in ingest_data (for data ingested via ingest_data). Examples: \"https://example.com/page\", \"clipboard://2024-12-30\". Provide either filePath or source, not both."New value: +"Source identifier (for ingest_data documents). Provide exactly one of filePath or source. Examples: \"https://example.com/page\", \"clipboard://2024-12-30\"."
1 tool update
v0.15.0- Changed
query_documents3 fields changed- changed
Input schema / properties / limit / descriptionPrevious value: -"Maximum number of results to return (default: 10). Recommended: 5 for precision, 10 for balance, 20 for broad exploration."New value: +"Maximum number of results to return (default: 10, range: 1-20). Recommended: 5 for precision, 10 for balance, 20 for broad exploration." - added
Input schema / properties / limit / maximumAdded value: +20 - added
Input schema / properties / limit / minimumAdded value: +1
1 tool update
v0.14.1- Changed
ingest_file1 field changed- added
Input schema / properties / visualQualityAdded value: +{ + "default": "fast", + "description": "VLM profile to use when visual is true. \"fast\" (default) is the lightweight SmolVLM-256M; \"quality\" is Qwen2.5-VL-3B-Instruct-ONNX with higher fidelity on figures with in-image text (~10x model-cache footprint, ~2x per-page inference). The server also accepts an empty string as a synonym for omitted (normalized to \"fast\"). Silently ignored when visual is false.", + "enum": [ + "fast", + "quality" + ], + "type": "string" +}
1 tool update
v0.14.0- Changed
ingest_file1 field changed- added
Input schema / properties / visualAdded value: +{ + "description": "If true and the file is a PDF, run VLM captioning on figure pages. No effect on non-PDF files.", + "type": "boolean" +}
1 tool update
v0.13.0- Added
read_chunk_neighbors
3 tool updates
v1.0.0- Added
delete_file - Added
ingest_data - Changed
query_documents2 fields changed- changed
Input schema / properties / limit / descriptionPrevious value: -"Maximum number of results to return (default: 5, max recommended: 20)"New value: +"Maximum number of results to return (default: 10). Recommended: 5 for precision, 10 for balance, 20 for broad exploration." - changed
Input schema / properties / query / descriptionPrevious value: -"Natural language search query (e.g., \"transformer architecture\", \"API documentation\")"New value: +"Search query. Include specific terms and add context if needed."
4 tool updates
- First observed
ingest_file - First observed
list_files - First observed
query_documents - First observed
status
TDQS
Each tool has a distinct purpose: sync_status tracks job progress while status reports index stats; ingest_file vs ingest_data clearly separate file-based and in-memory ingestion; query_documents, read_chunk_neighbors, delete_file, list_files, and sync_start all target different operations. No two tools are likely to be confused.
Most tools follow a verb_noun pattern (query_documents, ingest_file, delete_file, list_files, read_chunk_neighbors), but sync_status, sync_start, and status deviate, using noun compounds or a standalone noun. The mix is readable but not uniform.
9 tools is well-scoped for a local RAG server, covering ingestion (file and data), deletion, querying, context expansion, file listing, and status/sync operations without unnecessary redundancy or bloat.
The set covers the core lifecycle: ingest (file/data), delete, search, and context retrieval. Minor gaps include no direct way to fetch all chunks of a specific document or a bulk clear operation, but these can be worked around with existing tools like query_documents and sync_start.
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