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grounded-rag-mcp

grounded-rag-mcp

by chetan1521
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ingest_documents

Add local files or raw text to a named collection for search. Documents are automatically chunked, embedded, and indexed for hybrid semantic and keyword retrieval, enabling grounded Q&A over your own content.

Instructions

Ingest documents into a named collection so they can be searched.

Provide paths (files or directories of .txt/.md) and/or texts (raw strings). Documents are chunked, embedded, and indexed for both semantic and keyword search. Returns how many chunks were added and the collection's new total.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "ingest_documents"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathsNo
textsNo
chunk_sizeNo
collectionNodefault
chunk_overlapNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and discloses the main behavioral pipeline: chunking, embedding, and indexing for semantic and keyword search. It also states the return value (chunks added and new total), which adds useful non-obvious information.

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

Conciseness5/5

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

Four short, front-loaded sentences with no filler. The key action and purpose appear first, followed by the input options, processing behavior, and return value.

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

Completeness4/5

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

The description covers the main inputs, processing steps, and return output, and an output schema exists for return details. It could mention collection creation behavior or defaults for chunking parameters, but it is sufficiently complete for an agent to call this tool correctly.

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

Parameters3/5

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

The description compensates partially for 0% schema coverage by explaining paths and texts, including allowed file extensions and directories. However, it does not explain the semantics of chunk_size, chunk_overlap, or collection beyond vague references to chunking and a named collection.

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

Purpose5/5

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

The description clearly states the action (ingest), the resource (documents into a named collection), and the purpose (so they can be searched). It distinguishes this tool from sibling read/search tools by framing it as the ingestion-side counterpart.

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

Usage Guidelines4/5

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

The description gives clear context for when to use this tool: before searching, by adding documents to a collection. It also explains the two accepted input modes (paths and/or texts), but it does not explicitly name alternatives or say 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.

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