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ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_delete_search_app

Destructive

Permanently deletes a RAGFlow search app by ID while preserving its datasets, so you can remove an obsolete app without losing associated knowledge data.

Instructions

Permanently delete a search app (its datasets stay).

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": {
      "search_id": "<search_id>"
    },
    "name": "ragflow_delete_search_app"
  }
}

Input Schema

NameRequiredDescriptionDefault
search_idYesSearch app ID

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 the description's job is lighter. It still earns credit for two facts beyond the annotations: the deletion is permanent (irreversible) and datasets are preserved, which directly answers the main blast-radius question for this tool.

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 short sentence with the destructive verb front-loaded and the scope caveat immediately after. Nothing is wasted and nothing important is buried.

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?

An output schema exists, so return values need not be explained, and annotations cover the safety profile. The description supplies permanence and non-destruction of datasets, though it omits error behavior (e.g., what happens with an invalid or missing search_id).

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 a single parameter with 100% schema description coverage ('Search app ID' with a pattern constraint), so the schema already carries the meaning. The description adds nothing about the ID's format or how to obtain it, making baseline 3 appropriate.

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?

Names a specific verb and resource ('Permanently delete a search app') and adds a scope clarification that the parenthetical datasets remain. Read against siblings like ragflow_delete_datasets and ragflow_delete_agent, the name plus this scope note make the target unambiguous.

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?

Usage is only implied: an agent can infer this is the tool for removing a search app, and the parenthetical hints that dataset removal belongs elsewhere. There is no explicit 'use this instead of X' routing or statement of prerequisites such as required ownership or confirmation.

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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