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ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_related_questions

Generates related questions from a user's question to expand topic searches and surface useful follow-up queries.

Instructions

Generate related questions that help search for a topic.

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": {
      "question": "<question>"
    },
    "name": "ragflow_related_questions"
  }
}

Input Schema

NameRequiredDescriptionDefault
questionYesThe user's question
search_idNoSearch app whose model settings to use

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?

Annotations already declare readOnlyHint=false, openWorldHint=false, and destructiveHint=false, so the safety profile is covered. The description adds nothing beyond that: it does not say this invokes a model (implying latency/cost), whether it requires a configured search app, or how many questions it returns. Nothing contradicts the annotations.

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 wasted clauses. It is concise to the point of being thin, but structurally there is nothing extraneous to cut.

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

Completeness2/5

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

With an output schema present, return values need not be described, and the annotations plus 100% schema coverage cover safety and parameters. Still, for a model-backed generation tool the description omits invocation context (search app dependency, latency, query-expansion intent), leaving real gaps for an agent deciding whether to call it.

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 both 'question' and 'search_id' are already documented in the schema, and the description adds no extra meaning (e.g., whether 'question' is a topic or a full query). Baseline 3 applies when the schema carries parameter semantics alone.

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 states a specific verb and output ('Generate related questions') tied to a purpose ('help search for a topic'), so the core action is unambiguous. It never distinguishes itself from near-neighbors such as ragflow_search_app_completion or ragflow_retrieval, which is the only thing keeping it from a 5.

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 guidance on when to call this versus ragflow_search_app_completion, ragflow_retrieval, or ragflow_chat, nor any prerequisite (e.g., needing a search app). The phrase 'help search for a topic' hints at a query-expansion role but leaves the agent to infer it.

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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