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ragflow-kb-mcp
by feci

ragflow_get_chunk

Read-only

Retrieve a specific knowledge base chunk by dataset, document, and chunk ID, returning its text, keywords, questions, and availability.

Instructions

Get one chunk with its text, keywords, questions and availability.

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": {
      "dataset_id": "<dataset_id>",
      "document_id": "<document_id>",
      "chunk_id": "<chunk_id>"
    },
    "name": "ragflow_get_chunk"
  }
}

Input Schema

NameRequiredDescriptionDefault
chunk_idYesChunk ID
dataset_idYesDataset (knowledge base) ID
document_idYesDocument ID

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered externally. The description adds only that 'availability' is among the returned fields — mild extra context, with nothing about permissions, hierarchy requirements, or errors.

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?

A single front-loaded sentence with the resource first and the returned fields last; every word carries information and nothing is padded.

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 by-ID read with full schema coverage, complete annotations and an output schema that documents return values, the description is sufficient to call the tool. The remaining gap is the relationship between the three IDs, which is left to the caller to infer.

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 all three IDs described, so the schema does the explanatory work and baseline 3 applies. The description adds no meaning beyond it, such as the need for dataset_id/document_id/chunk_id to be mutually consistent.

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

Purpose4/5

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

Names a specific verb and resource ('Get one chunk') and enumerates the payload it returns (text, keywords, questions, availability), which separates it from ragflow_list_chunks and ragflow_update_chunk. It stops short of naming a sibling explicitly, so it lands at 4 rather than 5.

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

Usage Guidelines2/5

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

The only usage signal is implicit in 'Get one chunk' — a single-ID lookup. It never says when to prefer this over ragflow_list_chunks or ragflow_retrieval, nor that the three IDs must identify the same document hierarchy.

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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