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RAGBrain MCP

RAGBrain MCP

by ragbrain
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragbrain_get_document

Retrieve the complete text of a document from a knowledge base by its ID. Reconstructs full content from chunks to enable thorough reading.

Instructions

Retrieve the full content of a specific document by its ID. Returns the complete reconstructed text from all chunks. Use this when you need to read an entire document.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "doc_id": "<doc_id>"
    },
    "name": "ragbrain_get_document"
  }
}

Input Schema

NameRequiredDescriptionDefault
doc_idYesThe document ID (UUID) to retrieve

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?

No annotations are provided, so the description must cover behavioral traits. It states that it 'Returns the complete reconstructed text from all chunks,' which gives a good understanding of the output. Minor missing details like performance implications but adequate.

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?

Two short sentences with no wasted words. The purpose is front-loaded, and every sentence adds 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?

For a simple tool with one parameter and no output schema, the description covers purpose, return value (reconstructed text), and usage hint. Could mention handling of missing IDs or size limits, but not essential.

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?

Schema coverage is 100% with a single parameter (doc_id) already described in the schema as 'The document ID (UUID) to retrieve.' The description adds no further semantic meaning beyond the schema, so baseline applies.

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 'Retrieve the full content of a specific document by its ID' and distinguishes itself from sibling tools (browse, discover, list, search) by specifying its use for reading an entire document.

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 explicitly says 'Use this when you need to read an entire document,' providing clear context for when to use it. However, it does not mention when not to use it or suggest alternative tools for partial reads.

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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