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ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_create_agent

Create an agent from a JSON graph DSL in RAGFlow, defining components like Begin, Agent, and Message to build reusable workflows.

Instructions

Create an agent from a graph (DSL).

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": {
      "title": "<title>",
      "dsl": "<dsl>"
    },
    "name": "ragflow_create_agent"
  }
}

Input Schema

NameRequiredDescriptionDefault
dslYesAgent graph (DSL) as a JSON object with components such as Begin, Agent and Message; take one from ragflow_list_agent_templates or ragflow_get_agent
titleYesAgent title; RAGFlow appends (1), (2) ... if it is taken
permissionNoWho may use and edit the agent
descriptionNo
canvas_categoryNoDefaults to agent_canvas

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description confirms creation but adds no behavioral detail beyond annotations, such as permission effects, title collision handling, or failure behavior.

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 front-loaded sentence with no wasted words. It immediately identifies the action and the required input type.

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

Completeness3/5

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

The tool has an output schema and 80% schema description coverage, reducing the description's burden. However, for a creation tool with several optional parameters, it leaves out important context such as DSL sourcing and permission implications, making it minimally adequate rather than complete.

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 80%, so the schema largely documents the parameters. The description only mentions the DSL input and does not add meaning for title, permission, description, or canvas_category beyond what the schema provides, making the baseline 3 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: create an agent from a graph DSL. It is clear what the tool does, though it does not explicitly distinguish itself from sibling tools such as ragflow_update_agent or ragflow_create_agent_session.

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?

Provides no explicit guidance on when to use this tool versus alternatives or prerequisites. It implies creation from a DSL but does not mention using ragflow_list_agent_templates or ragflow_get_agent as sources, nor when to prefer update_agent.

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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