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ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_create_dataset

Create a knowledge base dataset with a built-in chunking method for indexing documents and enabling retrieval. Configure its name, language, parser, access permission, and embedding model.

Instructions

Create a dataset (knowledge base) that uses a built-in chunking method.

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": {
      "name": "<name>"
    },
    "name": "ragflow_create_dataset"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesDataset name (unique, max 128 characters)
languageNoDataset language, e.g. English
parser_idNoBuilt-in chunking method; 'general' fits most documentsgeneral
permissionNoWho may manage the dataset
descriptionNoShort description
embedding_modelNoEmbedding model as model_name@factory; defaults to the server's configured default

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, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds no behavioral context beyond that — nothing about what defaults are applied (permission, embedding model), whether the operation is reversible, or what the caller gets back, despite this being a creation mutation.

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 waste. It is appropriately short, though that brevity is partly why behavioral and usage context are thin.

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?

With six well-documented parameters, an output schema, and annotations covering the safety profile, the definition is minimally complete. However, for a create mutation it omits any sense of side effects or default configuration, leaving the agent to infer them from the schema defaults 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 description coverage is 100%, so the baseline is 3; every parameter including parser_id and embedding_model is documented in the schema. The description's only added semantic is that the chunking method is 'built-in', which nudges toward parser_id but adds nothing beyond the schema text.

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 a dataset'), and usefully clarifies that a dataset is a knowledge base configured with a built-in chunking method. It doesn't explicitly contrast itself with siblings like ragflow_update_dataset or ragflow_list_datasets, though the create/update/list distinction is intuitive from the names.

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, no prerequisites (e.g., whether documents are linked afterward via ragflow_link_files_to_datasets), and no mention of constraints such as name uniqueness beyond what the schema already says.

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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