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ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_list_agents

Read-only

List RAGFlow agents and ingestion pipelines without graphs, with filters for keywords, tags, owners, and canvas category plus pagination.

Instructions

List agents (agent canvases and ingestion pipelines) without their graphs.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "ragflow_list_agents"
  }
}

Input Schema

NameRequiredDescriptionDefault
descNo
pageNoPage number, starting at 1
tagsNoOnly agents with these tags
orderbyNoupdate_time
keywordsNoMatch agent titles
owner_idsNoOnly agents owned by these user IDs
page_sizeNoItems per page
canvas_categoryNoagent_canvas = chat agents, dataflow_canvas = ingestion pipelines

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so safety is covered. The description's one added behavioral fact is that graphs are omitted from the response, which is genuine value for payload-size expectations. It says nothing about pagination behavior, default ordering effects, or result ordering, which matter for an 8-parameter list call.

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 zero filler; the disambiguating parenthetical is placed immediately after the ambiguous noun. Nothing could be removed without losing information.

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?

An output schema exists, so return-shape detail is not needed, and annotations cover safety. However, for a tool with 8 optional parameters and a default page_size of 30, the description offers no pagination or scoping context, leaving the agent to infer how to page through and how to combine the several filter parameters.

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 coverage is 75%, with page, page_size, tags, owner_ids, keywords and canvas_category documented inline, so the schema does most of the work. The description adds no parameter meaning beyond the parenthetical mapping of canvas categories, which basically restates the schema's own enum description. Baseline 3 applies.

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 ("List agents") and immediately disambiguates the ambiguous term "agents" as covering both agent canvases and ingestion pipelines. The addition "without their graphs" signals it is a lighter sibling of ragflow_get_agent, though it never names that sibling explicitly.

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 infers it should call this to enumerate agents and call ragflow_get_agent when the graph is needed. There is no explicit when-to-use/when-not statement, no mention of whether results are scoped to the caller or a workspace, and no guidance on choosing between tags/keywords/owner_ids filters.

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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