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ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_update_chunk

Modify a RAGFlow chunk's text, keywords, or questions, and enable or disable it for retrieval.

Instructions

Edit a chunk's text, keywords or questions, or switch it on or off for retrieval.

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_update_chunk"
  }
}

Input Schema

NameRequiredDescriptionDefault
contentNo
chunk_idYesChunk ID
availableNoWhether retrieval may return this chunk
questionsNo
dataset_idYesDataset (knowledge base) ID
document_idYesDocument ID
important_keywordsNo

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=false, destructiveHint=false, openWorldHint=false, so the safety profile is covered. The description adds that the edit can be a metadata-only change or a retrieval on/off toggle, but omits key behavioral facts: whether it is a partial update (unspecified fields preserved) and whether at least one field must be supplied.

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?

One front-loaded sentence with no waste; the editable facets are grouped logically and nothing needs to be trimmed.

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?

A mutation tool with an output schema and annotations, so the description needn't explain returns or safety. However, for a 7-parameter partial-update tool it omits whether the update is a merge or replace and whether any optional field is mandatory, which an agent needs to call it safely.

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

Parameters4/5

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

With 57% schema coverage, the description usefully maps its prose to the optional parameters: 'text'→content, 'keywords'→important_keywords, 'questions'→questions, 'switch it on or off'→available. That clarifies intent where the schema leaves content, questions, and important_keywords undocumented, though it says nothing about the three required IDs (which the schema already labels).

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?

States a specific verb and resource ('Edit a chunk') and enumerates the editable facets (text, keywords, questions, retrieval availability), which distinguishes it from ragflow_get_chunk and ragflow_list_chunks. It does not explicitly differentiate from the overlapping ragflow_set_chunks_availability sibling, which also toggles availability.

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?

No explicit when-to-use or when-not guidance. The description implies editing an existing chunk but does not say how it differs from ragflow_set_chunks_availability (single vs batch toggling) or ragflow_add_chunk/delete_chunks, leaving the agent to infer selection.

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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