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ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_delete_documents

Destructive

Permanently remove documents and their chunks from a RAGFlow dataset by specifying dataset and document IDs; use it to clean up knowledge base content.

Instructions

Permanently delete documents and their chunks from a dataset.

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_ids": "<document_ids>"
    },
    "name": "ragflow_delete_documents"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYesDataset (knowledge base) ID
document_idsYesIDs of the documents to delete

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A3.6/5.0
Behavior4/5

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

The annotations already declare destructiveHint=true and readOnlyHint=false, so safety is covered. The description adds useful behavioral context beyond annotations by stating that deletion is permanent and cascades to document chunks.

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?

The description is a single front-loaded sentence with no filler. Every word contributes to stating scope, permanence, and cascade behavior.

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?

Given the annotations, output schema, and fully documented input schema, the description provides enough context to call the tool correctly. It could mention permissions or confirmation expectations, but the core destructive behavior is adequately disclosed.

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 schema has 100% description coverage and clearly documents dataset_id and document_ids. The description does not add meaning beyond those schema descriptions, so it meets the baseline for fully documented parameters.

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?

The description names a specific verb and resource: delete documents and their chunks from a dataset. It is clear enough to distinguish this from dataset-level and chunk-level deletion siblings, though it does not explicitly route between ragflow_delete_documents and ragflow_delete_files.

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?

It gives no explicit guidance on when to use this tool versus alternatives such as ragflow_delete_files, ragflow_delete_chunks, or ragflow_delete_datasets. Usage is only implied by the permanent-deletion wording.

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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