Skip to main content
Glama
Sign In
ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_mind_map

Builds a hierarchical mind map from your datasets by generating name/children nodes for a topic, helping you visualize and explore relationships. Runs multiple LLM calls, so allow minutes for completion.

Instructions

Build a mind map (a tree of name/children nodes) of a topic from datasets; runs several LLM calls and can take minutes.

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>",
      "dataset_ids": "<dataset_ids>"
    },
    "name": "ragflow_mind_map"
  }
}

Input Schema

NameRequiredDescriptionDefault
questionYesThe user's question
search_idNoSearch app whose datasets and settings to add
dataset_idsYesDatasets to build the map from

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A3.9/5.0
Behavior4/5

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

Goes beyond the annotations by disclosing that the tool 'runs several LLM calls and can take minutes,' which is important latency/cost context an agent needs before invoking it. Annotations already establish it is non-destructive and not read-only, so the added performance warning is the key value. It still omits prerequisites like datasets needing to be parsed or authentication context.

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 tightly written sentence that front-loads the action and output shape, then appends the critical cost warning. Every clause earns its place.

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

Completeness5/5

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

Given full schema coverage, an output schema, and annotations covering safety, the description supplies the one non-obvious piece an agent needs: that this is a slow, multi-LLM-call operation. Nothing essential for correct invocation is missing.

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 all three parameters are already documented in the input schema. The description adds only the loose notions of 'topic' and 'from datasets,' without clarifying how question, search_id, or the dataset_ids array interact. Baseline 3 is appropriate.

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: 'Build a mind map (a tree of name/children nodes) of a topic from datasets,' and even describes the output shape. It is clearly distinct from siblings like ragflow_chat or ragflow_retrieval, but it does not explicitly name or contrast those alternatives, keeping it below 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 Guidelines3/5

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

The description implies when to use it (you want a hierarchically structured map of a topic drawn from datasets), but it gives no explicit when-not-to-use guidance and never mentions alternatives such as ragflow_related_questions or ragflow_retrieval. Usage is inferable but not guided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools