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ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_list_documents

Read-only

Retrieve documents in a dataset with parsing status and chunk counts to monitor processing progress and verify document readiness.

Instructions

List documents in a dataset with their parsing status and chunk counts.

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": {
      "dataset_id": "<dataset_id>"
    },
    "name": "ragflow_list_documents"
  }
}

Input Schema

NameRequiredDescriptionDefault
descNo
nameNoExact document name
pageNoPage number, starting at 1
orderbyNocreate_time
keywordsNoMatch document names
page_sizeNoItems per page
dataset_idYesDataset (knowledge base) ID
document_idNoReturn only this document

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 establish this as a safe, read-only, closed-world operation, so the description does not need to explain safety. It adds that results include parsing status and chunk counts, but says nothing about pagination behavior, ordering defaults, or whether results are complete or truncated.

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?

The description is a single front-loaded sentence with zero filler. It leads with the action and resource, then adds the most relevant returned attributes.

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 values need not be explained, and annotations cover the safety profile. However, for an 8-parameter list tool with filtering and pagination options, the description omits that filtering by name, keywords, or document_id is possible, leaving the agent to discover those capabilities from the schema alone.

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 several parameters (name, page, page_size, dataset_id, document_id) already described in the schema. The description adds no parameter-level meaning of its own, and the undocumented desc/orderby parameters are only partly self-explanatory from their names, so it neither compensates nor regresses.

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 resource ('List documents in a dataset') and adds the useful detail that parsing status and chunk counts are included. It is distinguishable from dataset-level and chunk-level siblings, though it never explicitly contrasts itself with tools like ragflow_list_files.

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 use this tool versus alternatives such as ragflow_list_files, ragflow_list_chunks, or ragflow_get_dataset. The intended use is only implied by the phrase 'in a dataset'; no exclusions or prerequisites are given.

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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