Local RAG
MCP Local RAG
Busca en documentos privados desde un cliente MCP o desde la terminal sin enviarlos a una API de embeddings.
mcp-local-rag indexa archivos PDF, DOCX, Markdown y de texto en tu máquina. La búsqueda combina similitud semántica con coincidencia de palabras clave, de modo que las consultas pueden coincidir tanto con la intención como con términos técnicos exactos, como nombres de API, nombres de clases y códigos de error.
Características
Se ejecuta localmente: El análisis de documentos, los embeddings, el almacenamiento y la búsqueda se ejecutan en tu máquina. Después de la descarga inicial del modelo, la ingesta de texto y la búsqueda funcionan sin conexión.
Búsqueda híbrida: La recuperación semántica encuentra conceptos relacionados, mientras que la coincidencia de palabras clave potencia los términos técnicos exactos.
Embeddings configurables: Elige un modelo de embeddings de Hugging Face que se ajuste al idioma y al dominio de tus documentos.
Segmentación semántica: Los documentos se dividen en los límites de los temas en lugar de por recuentos fijos de caracteres. Los bloques de código Markdown permanecen intactos.
MCP y CLI: Usa el mismo índice desde una herramienta de codificación con IA o directamente desde la terminal.
No se requiere clave de API, Docker, Python ni una base de datos externa.
Related MCP server: rag-mcp
Inicio rápido
Requisitos
Node.js 22 o posterior
Acceso a Internet en el primer uso para descargar el paquete npm y el modelo de embeddings
Un directorio que contenga los documentos que deseas buscar
Establece BASE_DIR en ese directorio. También es el límite de seguridad para las operaciones
con archivos. Reemplaza /absolute/path/to/your/documents a continuación con la ruta absoluta
del directorio.
mcp-local-rag utiliza el protocolo MCP estándar a través de un servidor stdio local, por lo que funciona con herramientas de codificación con IA y otros hosts MCP que admiten servidores MCP locales.
Usa uno de los ejemplos a continuación, o registra npx -y mcp-local-rag y establece BASE_DIR
usando el formato de configuración MCP de tu cliente.
Para Claude Code: Ejecuta este comando:
claude mcp add local-rag --scope user --env BASE_DIR=/absolute/path/to/your/documents -- npx -y mcp-local-ragPara Codex: Añade a ~/.codex/config.toml:
[mcp_servers.local-rag]
command = "npx"
args = ["-y", "mcp-local-rag"]
[mcp_servers.local-rag.env]
BASE_DIR = "/absolute/path/to/your/documents"Para OpenCode: Añade a ~/.config/opencode/opencode.json (o opencode.jsonc):
{
"$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"
}
}
}
}Para Cursor: Añade a ~/.cursor/mcp.json:
{
"mcpServers": {
"local-rag": {
"command": "npx",
"args": ["-y", "mcp-local-rag"],
"env": {
"BASE_DIR": "/absolute/path/to/your/documents"
}
}
}
}Reinicia el cliente y luego pídele que construya el índice:
Sync all documents in the configured root and wait until it finishes.La primera sincronización descarga el modelo de embeddings predeterminado (aproximadamente 90 MB) y puede tardar de 1 a 2 minutos antes de que comience la ingesta. Las ejecuciones posteriores usan la caché local.
Una vez que la sincronización se complete:
What does the API documentation say about authentication?Inicio rápido con CLI
Para usar la CLI sin un cliente MCP:
npx mcp-local-rag ingest ./docs/
npx mcp-local-rag query "authentication API"La CLI usa el directorio actual como su raíz de documentos de forma predeterminada. Ejecuta ambos
comandos desde el mismo directorio para que usen el mismo índice predeterminado, o establece
BASE_DIR y DB_PATH explícitamente.
Por Qué Existe
Algunos conjuntos de documentos no pueden enviarse a un servicio de embeddings alojado debido a la confidencialidad o a la política organizacional. Mantener el índice local los hace buscables sin añadir un costo de API por consulta.
La búsqueda semántica por sí sola puede omitir identificadores exactos que son importantes en la documentación técnica. El reordenamiento por palabras clave mantiene esos términos visibles sin renunciar a la recuperación en lenguaje natural.
Contenido Admitido
Entrada | Cómo ingerir |
PDF, DOCX, TXT, Markdown | Ingesta de archivos o sincronización de directorios |
HTML ya obtenido por el cliente |
|
Texto plano o Markdown en memoria |
|
La obtención de HTML no está integrada en el servidor. Un cliente MCP puede obtener una página y
pasar su HTML a ingest_data.
Excel, PowerPoint, imágenes independientes y extensiones de archivos de código fuente no son admitidos por la ingesta de archivos. Los PDF pueden usar opcionalmente un modelo de visión local para describir figuras, pero esto no es OCR ni búsqueda de imágenes.
Herramientas MCP
Herramienta | Propósito |
| Reconciliar el índice con todas las raíces configuradas o una ruta |
| Consultar un trabajo de sincronización en ejecución |
| Ingerir o reemplazar un archivo |
| Ingerir texto, Markdown o HTML ya retenido por el cliente |
| Buscar con coincidencia semántica y refuerzo de palabras clave |
| Leer los fragmentos circundantes de un resultado de búsqueda |
| Mostrar los archivos admitidos y su estado de ingesta |
| Eliminar un archivo indexado o un elemento de |
| Mostrar el estado del índice y de la búsqueda |
Sincronización de una Raíz de Documentos
sync_start ingiere archivos nuevos y modificados, omite archivos idénticos por bytes y elimina
las entradas del índice para archivos que ya no existen:
Sync everything under the configured document roots and wait for completion.La herramienta devuelve un jobId inmediatamente. Los clientes deben consultar sync_status
hasta que su estado se convierta en succeeded o failed. La sincronización no genera
subtítulos visuales. Establece STORE_IMAGES=true en el entorno del servidor MCP para almacenar
imágenes admitidas de PDF y DOCX para archivos nuevos o modificados seleccionados por la
sincronización; los archivos sin cambios permanecen omitidos.
El proceso del servidor conserva solo un trabajo de sincronización. Un trabajo más nuevo reemplaza un registro finalizado, y reiniciar el servidor lo descarta.
Ingesta de un Archivo
ingest_file acepta PDF, DOCX, TXT y Markdown. Las rutas de archivo MCP deben ser absolutas y
deben permanecer dentro de una raíz de documentos configurada:
Ingest the document at /Users/me/docs/api-spec.pdf.Volver a ingerir la misma ruta reemplaza sus fragmentos existentes.
Búsqueda y Lectura de Más Contexto
What does the API documentation say about authentication?
Find the documented behavior of ERR_CONNECTION_REFUSED.Los resultados contienen el texto, la ruta de origen, el título, el índice del fragmento, la
puntuación de relevancia y cualquier imagen almacenada en ese fragmento. MCP devuelve cada imagen
como un bloque de contenido de imagen emparejado con su identidad de resultado; la CLI query
incluye un arreglo images de { imageIndex, mimeType, data } en cada resultado. Pasa el
chunkIndex y ya sea filePath o source de un resultado a read_chunk_neighbors cuando la
respuesta necesite más contexto:
Read the surrounding chunks for that authentication result.Tanto query_documents como list_files aceptan un prefijo de ruta scope absoluto opcional, o
una lista de prefijos. Un prefijo coincide con la ruta exacta y sus descendientes.
Ingesta de HTML
Usa ingest_data después de que el cliente MCP obtenga una página:
Fetch https://example.com/docs and ingest the HTML.El servidor extrae el artículo principal, lo convierte a Markdown y lo almacena bajo el identificador de origen proporcionado. Reutilizar el mismo origen actualiza el contenido existente.
Respeta los términos y los derechos de autor del sitio de origen al indexar contenido externo.
Subtítulos Visuales de PDF e Imágenes Almacenadas
El modo visual añade un subtítulo generado para páginas PDF con muchas figuras. Es opcional y no carga un modelo de visión durante la ingesta normal.
Ingest /Users/me/docs/research-paper.pdf with visual: true.npx mcp-local-rag ingest ./docs/research-paper.pdf --visualEl almacenamiento de imágenes es independiente de los subtítulos visuales. Establece
STORE_IMAGES=true para el servidor MCP, o pasa --images a la ingesta y sincronización de la
CLI:
npx mcp-local-rag ingest ./docs/research-paper.pdf --images
npx mcp-local-rag sync ./docs/ --imagesEl almacenamiento de PDF usa las regiones detectadas de figuras/tablas. El almacenamiento de DOCX
incluye solo imágenes PNG/JPEG que la conversión existente de Mammoth emite como <img>; los
gráficos, SmartArt y las formas no se renderizan por separado. Las imágenes almacenadas siguen a
su texto circundante hasta el fragmento semántico final y no alteran la clasificación, las
puntuaciones ni el recuento de resultados.
|
| Comportamiento de PDF |
false | false | Solo texto; sin subtítulos visuales ni imágenes devueltas. |
true | false | Los subtítulos generados se convierten en texto buscable; no se almacenan ni devuelven imágenes. |
true | true | Los subtítulos generados se convierten en texto buscable, y las imágenes de los fragmentos coincidentes se devuelven en línea. |
false | true | Las imágenes se adjuntan al texto de PDF retenido cercano y se devuelven en línea para los fragmentos coincidentes; el VLM no se importa, carga ni ejecuta. |
Perfil | Caché de modelo | Caso de uso |
| aprox. 250 MB | Indexación visual ligera |
| aprox. 2.9 GB | Figuras que contienen etiquetas, anotaciones u otro texto en la imagen |
Selecciona el modelo más grande con visualQuality: "quality" a través de MCP o
--visual-quality quality a través de la CLI. La inferencia medida en CPU fue aproximadamente el
doble de lenta que fast, aunque los resultados dependen del hardware y de las actualizaciones
del modelo.
Los subtítulos son texto auxiliar, no transcripciones fieles. Trata los subtítulos recuperados y el texto del documento como entrada no confiable, no como instrucciones.
Con límites altos, los fragmentos coincidentes y sus adjuntos pueden acercarse al límite de contexto del modelo/cliente; elige el límite de consulta teniendo en cuenta el contexto disponible del modelo que realiza la llamada.
CLI
La CLI usa el mismo analizador, embedder y almacén de vectores sin un cliente MCP:
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"Las opciones globales como --db-path, --cache-dir y --model-name van antes del subcomando.
Las opciones del subcomando van después:
npx mcp-local-rag --db-path ./my-db query "authentication"Ejecuta npx mcp-local-rag --help para la referencia completa de comandos.
La CLI no lee la configuración del cliente MCP. Establece las mismas variables de entorno o
banderas si ambas interfaces deben compartir un índice. En particular, MODEL_NAME y el
--model-name de la CLI deben coincidir para una base de datos compartida.
Ajuste de Búsqueda
El refuerzo de palabras clave está habilitado de forma predeterminada. La agrupación por brecha de relevancia y los filtros de distancia y de archivos son controles opcionales para corpus que necesitan una selección de resultados más estricta.
Variable | Predeterminado | Descripción |
|
| Factor de refuerzo de palabras clave (0.0–1.0). 0 desactiva el reordenamiento por palabras clave; 1 aplica el refuerzo máximo. |
| (no establecido) |
|
| (no establecido) | Filtra los resultados de baja relevancia (p. ej., |
| (no establecido) | Limita los resultados a los N mejores archivos (p. ej., |
Para especificaciones de API y otros documentos que contienen muchos identificadores, un peso de palabras clave más fuerte puede mejorar la clasificación de términos exactos:
"env": {
"RAG_HYBRID_WEIGHT": "0.7"
}0.7: reordenación de coincidencias exactas ligeramente más fuerte que la predeterminada1.0: impulso máximo de palabras clave
Cómo funciona
Durante la ingesta:
El analizador extrae texto del formato de entrada.
El fragmentador semántico encuentra límites de temas y conserva los bloques de código Markdown.
Transformers.js crea incrustaciones localmente.
LanceDB almacena los fragmentos, metadatos, vectores e índice de texto completo.
Durante la búsqueda:
La consulta se incrusta con el mismo modelo.
La búsqueda vectorial recupera fragmentos semánticamente relacionados.
Los filtros opcionales de distancia y de grupo de relevancia reducen los candidatos cuando están configurados.
Las coincidencias de texto completo impulsan los términos de consulta exactos.
Habilidades de agente
Las Habilidades de agente proporcionan orientación de consulta e ingesta para asistentes de IA:
npx mcp-local-rag skills install --claude-code
npx mcp-local-rag skills install --claude-code --global
npx mcp-local-rag skills install --codexLas habilidades instaladas cubren la formulación de consultas, el refinamiento de resultados y la ingesta de HTML. Pida al asistente que use la habilidad mcp-local-rag explícitamente si no se activa automáticamente.
Configuración
El servidor MCP lee variables de entorno. La CLI acepta las variables de entorno globales y las banderas enumeradas; el almacenamiento de imágenes en la ingesta y sincronización de la CLI solo está habilitado con --images.
Variable de entorno | Bandeira de CLI | Predeterminado | Descripción |
|
| Directorio actual | Una raíz de documentos; la bandera de CLI es repetible en |
| N/A | (sin establecer) | Matriz JSON de raíces de documentos; tiene prioridad sobre |
|
|
| Ubicación de la base de datos vectorial |
|
|
| Directorio de caché de modelos |
|
|
| Modelo de incrustación de Hugging Face |
|
|
| Tamaño máximo de archivo en bytes |
|
|
| Longitud mínima de fragmento en caracteres (1–10000) |
| N/A |
| Solo servidor MCP: almacena imágenes PDF/DOCX compatibles y las devuelve con los fragmentos coincidentes. La CLI usa |
| N/A |
| Dispositivo de ejecución de ONNX Runtime |
| N/A |
| Tipo de dato de incrustación pasado al modelo seleccionado |
Raíces de documentos (BASE_DIR y BASE_DIRS)
mcp-local-rag solo permite operaciones de archivos dentro de las raíces configuradas. Para múltiples raíces, BASE_DIRS debe ser una matriz JSON de rutas no vacías:
export BASE_DIRS='["/Users/me/Documents/work","/Users/me/Projects/specs"]'La configuración de raíces se resuelve en este orden:
Banderas de CLI
--base-dir <ruta>(repetibles eningest,listysync)BASE_DIRSBASE_DIRDirectorio actual
Cada fuente reemplaza la fuente de menor prioridad en lugar de fusionarse con ella. Una configuración de BASE_DIRS no válida falla en lugar de recurrir a BASE_DIR o al directorio actual. status permanece disponible en MCP para que el cliente pueda informar el error de configuración.
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 listAlmacenamiento y modelos
DB_PATH y CACHE_DIR son relativos al directorio de trabajo del proceso de forma predeterminada. Establezca rutas absolutas cuando el cliente MCP pueda iniciar el servidor desde diferentes directorios de proyecto.
Establezca MODEL_NAME o pase --model-name para elegir un modelo de incrustación de Hugging Face que se ajuste al idioma y dominio de sus documentos.
mcp-local-rag genera incrustaciones con agrupación de medias y normalización L2. Al elegir un modelo, verifique si estos ajustes coinciden con su configuración de inferencia recomendada, ya que el método de agrupación puede afectar la calidad de la recuperación.
Cambiar MODEL_NAME, RAG_DEVICE o RAG_DTYPE puede hacer que los vectores existentes sean incompatibles. Use un DB_PATH nuevo o elimine el índice existente y vuelva a ingerir después de cambiar la configuración de incrustación.
Un ejemplo de modelo para documentos en inglés es Xenova/bge-small-en-v1.5.
Seguridad y operación
El acceso a archivos está restringido a las raíces de
BASE_DIR,BASE_DIRSo--base-dirde la CLI.Se rechazan los enlaces simbólicos que resuelven fuera de cada raíz configurada.
El procesamiento de documentos y la búsqueda no realizan solicitudes de red después de que los modelos requeridos estén en caché.
El servidor está diseñado para un usuario local y no proporciona autenticación ni control de acceso.
No ejecute múltiples escritores de CLI o MCP contra el mismo
DB_PATH. Las consultas de solo lectura pueden ejecutarse mientras una sincronización está activa.Haga una copia de seguridad de un índice copiando su directorio
DB_PATHmientras no haya un escritor activo.
"No se encontraron resultados"
Los documentos deben ingerirse primero. Ejecute "List all ingested files" para verificar.
Falló la descarga del modelo
Verifique la conexión a internet. Si está detrás de un proxy, configure los ajustes de red. El modelo también se puede descargar manualmente.
"Archivo demasiado grande"
El límite predeterminado es 100MB. Divida archivos grandes o aumente MAX_FILE_SIZE.
Consultas lentas
Verifique el número de fragmentos con status. Los documentos grandes con muchos fragmentos pueden ralentizar las consultas. Considere dividir archivos muy grandes.
"Ruta fuera de BASE_DIR"
Asegúrese de que las rutas de archivo estén dentro de una de las raíces configuradas (BASE_DIR, cualquier entrada de BASE_DIRS o cualquier --base-dir de la CLI). Use rutas absolutas.
"BASE_DIRS debe ser una matriz JSON..."
BASE_DIRS acepta una matriz JSON de una o más cadenas de ruta no vacías:
Válido:
BASE_DIRS='["/Users/me/work","/Users/me/specs"]'No válido:
BASE_DIRS=/a:/b(la sintaxis de delimitador no es compatible)No válido:
BASE_DIRS='[]'(matriz vacía)
El cliente MCP no ve las herramientas
Verifique la sintaxis del archivo de configuración
Reinicie el cliente por completo (Cmd+Q en Mac para Cursor)
Pruebe directamente:
npx mcp-local-ragdebería ejecutarse sin errores
Contribuciones
¡Las contribuciones son bienvenidas! Consulte CONTRIBUTING.md para la configuración y las pautas.
Licencia
Licencia MIT. Gratuita para uso personal y comercial.
Publicaciones de blog
Building a Local RAG for Agentic Coding: Análisis técnico en profundidad del diseño de fragmentación semántica y búsqueda híbrida.
Agradecimientos
Construido con Model Context Protocol de Anthropic, LanceDB y Transformers.js.
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 are provided, so the description carries the full burden. It discloses return values (deleted, removedChunks, existed) and the conditional requirement, which goes beyond the schema. It does not explicitly state irreversibility, but the term 'delete' implies it, and chunk removal 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the action, and every sentence adds value. It is concise without sacrificing important details like return values and parameter selection.
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 the moderate complexity of the tool, the description covers the essential decision (filePath vs source), the required parameter constraint, and the return values. It lacks edge-case details like what happens if both parameters are provided, but this is minor given the overall clarity.
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%, but the description adds critical semantic context by mapping parameters to ingestion methods and clarifying the 'either/or' requirement. This goes beyond the schema's examples and helps the agent choose correctly.
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 deletes a previously ingested file or data from the vector database, using specific verbs and resource. It distinguishes itself from sibling tools like ingest_file and query_documents, as deletion is a unique operation.
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 explains when to use each parameter: filePath for files ingested via ingest_file, source for data ingested via ingest_data. It also states that either must be provided, guiding the agent on parameter selection.
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?
With no annotations provided, the description carries the burden of behavioral disclosure. It reveals a key behavioral trait—re-ingestion with the same source identifier updates existing content—and specifies the return shape. While it does not mention auth, permissions, or side effects, it covers the main behavioral consequences for the typical use case.
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 long and front-loaded with the tool's core purpose. It efficiently packs the alternative tool reference, the re-ingestion behavior, and the return value into a compact, well-structured format with zero filler.
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 the tool has 2 parameters (one nested), no annotations, and no output schema, the description provides a complete picture: what it does, when to use it, how the source identifier behaves, and what it returns. It lacks explicit error scenarios or prerequisites, but these are not critical for a simple ingestion tool. The return value statement covers the output side.
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 50% (content and format described in schema, metadata partially). The description adds significant value by explaining the 'source' identifier protocol with examples (e.g., 'clipboard://2024-12-30', 'chat://2024-12-30/project-discussion'), which is essential for correct invocation. This compensates well for the moderate schema coverage.
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 uses the specific verb 'Ingest' and clearly specifies the resource: 'in-memory content as a string'. It further distinguishes itself from a sibling tool by explicitly pointing to 'ingest_file' for disk files, making the purpose unambiguous.
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 explicitly states when to use this tool (in-memory string content) and when not to (files on disk, via 'use ingest_file for files on disk'). This provides clear alternatives and context for selection.
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?
With no annotations provided, the description carries the burden of disclosing behavior. It explicitly states that re-ingesting replaces existing data, revealing a destructive side effect, and also specifies the return object structure. It does not cover other potential behaviors like long-running VLM processing, but the key destructive behavior is disclosed.
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 zero waste. First sentence front-loads action and resource, second sentence covers constraints and return value. Highly scannable 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 no output schema and no annotations, the description covers essential operational context: supported file types, absolute path requirement, replacement behavior, and return format. It lacks explicit separation from sibling ingest tools (ingest_data, ingest_directory) but otherwise provides a complete picture for a single-file ingestion tool.
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%, so baseline is 3. The description adds value by listing allowed file extensions (PDF, DOCX, TXT, MD) for filePath, which the schema does not specify. It also clarifies replacement semantics tied to the file path. It does not add extra detail for visual or visualQuality beyond what the schema provides, so a slight uplift is appropriate.
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 'Ingest' and resource 'document file (PDF, DOCX, TXT, MD) into the vector database'. It implicitly distinguishes itself from sibling tools like ingest_directory by focusing on a single document file with an absolute path, and from ingest_data by specifying file formats.
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 states clear context: use for ingesting a document file, path must be absolute, and re-ingesting the same path replaces existing data. It does not explicitly name alternatives or exclusions, but the singular 'file' and mention of supported extensions imply single-file use, differentiating from ingest_directory.
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 full burden. It discloses the return structure ({ baseDirs, files, sources }), explains that sources lists non-file ingested items, and the scope parameter details behavior for relative/out-of-base prefixes. It does not explicitly state read-only intent or mention recursion depth/sorting, but for a list tool the essentials are covered. Edge cases for scope are thoroughly disclosed.
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: first states the core function and return object, second clarifies what 'sources' contains. No filler, front-loaded with the verb 'List'. The parameter schema is detailed but that is separate. The main description earns its place.
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 list tool with no output schema, the description explains the return object and the meaning of sources. The scope parameter covers traversal/filtering behavior. It could additionally disclose recursion depth or permission requirements, but these are less critical for a read-only list operation. Overall, sufficiently complete for an agent to invoke correctly.
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% for the single optional parameter 'scope', and its description is detailed (absolute path, prefix matching, union for arrays, behavior for relative/out-of-base). The tool description adds no extra parameter semantics beyond the schema. Per guidelines, high schema coverage yields a baseline of 3, which is appropriate.
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 what the tool does: 'List supported files (PDF, DOCX, TXT, MD) under the configured base directories and whether each is ingested.' This is a specific verb+resource combination with scope (supported extensions, base directories, ingestion status). It distinguishes from siblings: query_documents and status focus on querying, ingest_* on adding content, delete_file on removal. The tool is unambiguously the file-listing utility.
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 usage: use this when you need to see files and their ingestion status, and it notes that sources include non-file ingest_data content (web pages, clipboard). The scope parameter description adds clear guidance about absolute path prefixes and how to interpret empty results (compare against baseDirs). However, it does not explicitly contrast with alternative tools such as query_documents or status, nor state when not to use it.
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. Use the returned order as the ranking; score may disagree with it. Each has filePath, chunkIndex, text, fileTitle, score (lower is closer), and source (for ingest_data items). Optional sourceContext provides heading paths and original line or physical PDF page ranges; multiple heading paths mean the chunk crosses sections.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 10, range 1-100). 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 carries the full burden, and it does disclose non-obvious behavior: result ordering is authoritative over score, score is lower-is-closer, source is only populated for ingest_data items, and multiple heading paths signal a cross-section chunk. It omits any statement of read-only safety, rate limits, or empty-result behavior, keeping it below a 5.
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?
Front-loaded with the core purpose, then dense but load-bearing detail about result fields and sourceContext semantics. Every sentence carries information, though the back half packs several distinct facts into long clauses that could be slightly tighter.
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?
There is no output schema, and the description compensates well by enumerating the return fields (filePath, chunkIndex, text, fileTitle, score, source) and explaining sourceContext. With no annotations, it should also cover the read-only nature and any scope-omission fallback, which it leaves implicit.
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%, so the schema already documents query, limit, and scope thoroughly (including the absolute-path prefix rule). The description's detail is almost entirely about returned fields, not input parameters, so it adds little beyond the schema; 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?
States a specific verb and resource ('Search ingested documents') and specifies the retrieval mechanism ('hybrid keyword + semantic matching'), so the agent knows exactly what capability this is. It does not name or contrast any sibling (e.g. read_chunk_neighbors or list_files), so it falls short of the sibling-differentiating 5.
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?
Provides real guidance on interpreting output ('use the returned order as the ranking; score may disagree with it') and on query construction, which implies the tool's role. However, it never says when to prefer this over siblings like read_chunk_neighbors or list_files, and gives no exclusions or prerequisites, so usage is only implied.
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 []. Optional sourceContext provides heading paths and original line or physical PDF page ranges; multiple heading paths mean the chunk crosses sections. 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?
With no annotations present, the description carries the full behavioral burden and does so: it discloses the return shape (target chunk flagged isTarget: true plus neighbors, ascending by chunkIndex), the edge case (out-of-range chunkIndex returns []), and the defaults/limits (before=2, after=2, max 50 each). Optional sourceContext is explained, including that multiple heading paths signal a section-crossing chunk.
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?
Front-loaded with purpose, then how to call it, then return behavior, then the optional sourceContext detail, then defaults. Every sentence contributes, though it is a dense single block rather than cleanly separated clauses.
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?
No output schema exists, so the description must explain returns, and it does (target + neighbors, ordering, empty result). Combined with the documented defaults, limits, and input constraints, an agent has everything needed to call this correctly against a 5-parameter schema.
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 already 100%, so the baseline is 3. The description adds genuine meaning beyond the schema by stating that chunkIndex originates from a query_documents result and by restating the mutual-exclusion rule for filePath/source, plus the semantics of sourceContext's heading paths.
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?
States a specific verb and resource (read the chunks immediately before/after a query_documents result) and ties itself directly to the sibling query_documents, so an agent can distinguish it from a plain search. The scope constraint 'in the same document' further narrows what it returns.
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?
Explains the triggering context clearly: use it after a query_documents result when you need more surrounding context, and it names the exact input to carry over (chunkIndex plus one of filePath/source). It gives no explicit 'when not to use it' or alternative (e.g., re-running query_documents with a wider window), so it stops short of a 5.
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?
With no annotations, the description must carry the burden of behavioral disclosure. It communicates the output fields and implies a read-only operation via 'Get', but it does not explicitly state safety, side effects, or any operational conditions. The field list adds context, but some behavioral aspects remain implicit.
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, front-loaded sentence that immediately states the tool's purpose and then lists the output fields in a structured way. There is no redundancy or 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 simple status tool with no parameters and no output schema, the description adequately covers the return values and purpose. It does not mention potential delays or whether the status is a snapshot, but for its simplicity, the information provided is sufficient for basic 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 tool has no parameters and an empty input schema, so parameter semantics are not needed. The description adds value by enumerating the output fields, which helps the agent understand what to expect, thus justifying a baseline score of 4.
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 action (Get) and resource (index status), and the listed field names specify exactly what the tool returns. This distinguishes it from sibling tools like query_documents or sync_status, which have different purposes.
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 is given on when to use this tool versus alternatives such as sync_status, which may also report on index state. There is no mention of exclusions or typical use cases, leaving the agent to infer when this tool is appropriate.
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. Each changed PDF is re-ingested with the visual profile ("fast" or "quality") already recorded for it, so a PDF indexed with VLM captions keeps them; a PDF with no recorded profile stays text-only. There is no option to change a profile here — use the CLI (mcp-local-rag sync --visual) to set one, or ingest_file to replace the file, where a normal ingest clears the recorded profile. Stored images are unrelated: STORE_IMAGES applies to whatever this run re-ingests and never makes a file changed. 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?
No annotations are provided, so the description carries the full burden. It discloses side effects (index updates/removals), non-blocking execution, job retention, and process-exit loss. It also clarifies the behavior regarding visual profiles and STORE_IMAGES, ensuring the agent understands the tool's operational nuances.
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 overly verbose and repetitive. Phrases like 'visual profile' and 'only one job is kept' are repeated, and the whole text could be condensed to a few sentences without losing meaning. The single-paragraph structure lacks scannable clarity, and the redundancy detracts from conciseness.
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?
The description covers the main operational aspects: return value, polling mechanism, job limitations, and process-exit behavior. It is slightly incomplete regarding error cases or permission requirements, but given the moderate complexity and the absence of an output schema, it provides sufficient context for correct 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 for 'path' is already highly detailed, covering optionality, file/directory semantics, baseDirs, and omission behavior. The tool description repeats this verbatim without adding new semantic information, so the value beyond the schema is minimal, resulting in the baseline score of 3.
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 purpose: '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.' It also specifies the non-blocking return of a jobId, making the tool's primary function unambiguous.
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 alternatives and complementary tools: 'use the CLI (mcp-local-rag sync --visual) to set one, or ingest_file to replace the file' and 'poll sync_status with that jobId'. Also clarifies scope behavior and omission of path to sync all base directories, providing clear guidance on when and how to use the tool.
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, the description carries the full burden. It clearly states the return shape (jobId, state, total, completed, summary, warnings, error) and explains null/unknown meanings. It does not mention side effects, but 'Get' implies a read-only operation, and no side effects are likely. The level of detail is strong for a status 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 longer than a typical one-liner, but it is dense and structured with the return object in braces, using spaces and parentheses for clarity. Every sentence adds value, though it could be slightly trimmed without loss. It is front-loaded with the core action.
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 single-parameter status tool with no output schema, the description is remarkably complete. It details all possible state values, null semantics, and the meaning of an unknown jobId. There is no obvious missing behavior; the tool's functionality is fully described.
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 covers jobId with 'Identifier returned by sync_start.' The description adds meaning by explaining that an unknown jobId indicates replacement/loss, and by showing jobId in the return structure. This goes beyond the schema's basic parameter comment.
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 starts with 'Get the current or latest sync job record' — a specific verb and resource. It clearly distinguishes itself from siblings like sync_start (which creates a job) and status (which likely relates to something else) by focusing on sync job state retrieval.
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 the intended context: to poll or inspect a previously started sync job. It does not explicitly name alternatives, but the mention of 'unknown jobId means the job was replaced by a newer one or lost with a previous server process' gives practical guidance on interpreting results, which helps decide when this tool is useful.
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 tool update
v0.19.0- Changed
query_documents2 fields changed- changed
Input schema / properties / limit / descriptionPrevious value: -"Max results (default 10, range 1-20). Lower favors precision, higher recall."New value: +"Max results (default 10, range 1-100). Lower favors precision, higher recall." - changed
Input schema / properties / limit / maximumPrevious value: -20New value: +100
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" +}
TDQS
Scored across 9 tools
Most tools have clearly distinct purposes: ingest_file vs ingest_data split by source type, query_documents vs read_chunk_neighbors split by search vs context, and delete_file/list_files/sync_start are unique. The only mild overlap is status (index state) vs sync_status (sync job record), but descriptions disambiguate them well.
Seven of nine tools follow a clean verb_noun pattern (query_documents, read_chunk_neighbors, delete_file, ingest_data, ingest_file, list_files, sync_start). The two noun-only names (status, sync_status) are minor deviations but still readable and consistent with each other.
Nine tools is well-scoped for a local RAG server, with each tool earning its place across ingest, query, context, delete, list, sync, and status concerns. No redundant or filler tools.
The lifecycle is well covered: ingest (file/data), query, context retrieval, list, delete, sync, and status. The main gap is that changing a PDF's visual profile is only possible via the CLI, not through an MCP tool, though re-ingest provides a workaround.
Maintenance
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Self-hosted AI workspace: semantic RAG document search, long-term memory, and AI chat via MCP.
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