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ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_delete_datasets

Destructive

Permanently deletes RAGFlow datasets, removing all associated documents and chunks in one operation.

Instructions

Permanently delete datasets together with all their documents and chunks.

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_ids": "<dataset_ids>"
    },
    "name": "ragflow_delete_datasets"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idsYesIDs of the datasets to delete

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true and readOnlyHint=false, so safety is covered. The description adds the crucial extra fact that deletion cascades to all documents and chunks, which annotations alone do not convey. It stops short of describing irreversibility/confirmation requirements or permission needs.

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 sentence with zero filler; the most important word ('Permanently') and the cascade scope are both front-loaded.

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?

With an output schema present and annotations covering the destructive profile, the description supplies the one thing structured data cannot: the cascade scope. Missing details such as partial deletion behavior or confirmation requirements are minor for a single-parameter tool.

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?

There is only one parameter (dataset_ids) and schema coverage is 100%, with the schema already saying 'IDs of the datasets to delete'. The description adds no format, batching, or partial-failure semantics, so baseline 3 is correct.

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?

States a specific verb and resource ('Permanently delete datasets') and quantifies the blast radius ('together with all their documents and chunks'), which cleanly separates it from siblings like ragflow_delete_documents and ragflow_delete_chunks that remove narrower resources. An agent can pick this tool without opening the schema.

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

Usage Guidelines3/5

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

The word 'Permanently' signals this is the heavy-handed option, implicitly steering an agent toward narrower delete tools when only documents or chunks are targeted. However, no explicit when-to-use/when-not-to-use guidance or mention of the sibling delete tools is given, so the routing must be inferred.

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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