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ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_list_chunks

Read-only

Retrieve paginated parsed chunks from a document in a RAGFlow dataset to inspect or edit its chunk content.

Instructions

List the parsed chunks of a 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": {
      "dataset_id": "<dataset_id>",
      "document_id": "<document_id>"
    },
    "name": "ragflow_list_chunks"
  }
}

Input Schema

NameRequiredDescriptionDefault
pageNoPage number, starting at 1
keywordsNoMatch chunk content
page_sizeNoItems per page
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

C2.9/5.0
Behavior2/5

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

The readOnlyHint/destructiveHint/openWorldHint annotations already cover the safety profile, so the description only needed to add operational context, and it adds almost none: no pagination behavior (default page size 30, max 1000), no note that keyword matching filters results, no ordering or empty-result semantics.

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

Conciseness4/5

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

A single front-loaded sentence with no filler. It is efficient but arguably under-specified rather than concise, leaving no room for any qualifying detail.

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

Completeness3/5

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

Because the input schema is fully documented and an output schema exists, the description needn't explain parameters or return values. What it still lacks is the surrounding workflow context (parsing prerequisite, chunk-level siblings), so it is only minimally sufficient.

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 description coverage is 100%, so all five parameters including page, page_size, and keywords are documented in the schema. The description adds no extra meaning on top of that, which sets the baseline at 3.

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: list the parsed chunks of a document, with both required identifiers implied. It is clear on its own but does nothing to separate it from siblings such as ragflow_get_chunk (single chunk) or ragflow_list_documents (document listing).

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?

There is no statement of when to use this versus ragflow_get_chunk, ragflow_retrieval, or ragflow_list_documents, and no mention that this only makes sense for documents already parsed. The agent must infer the workflow from sibling names alone.

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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