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RAGFlow MCP Server

RAGFlow MCP Server

by wang-junjian
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

创建聊天

create_chat

Creates a chat assistant linked to a specific dataset for conversational retrieval.

Instructions

创建一个新的聊天助手，基于指定的数据集

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": "create_chat"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYes数据集ID
nameNo聊天助手的名称，可选，默认为'RAGFlow助手'

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations, the description carries full burden. It only states 'create' without disclosing side effects, permissions requirements, error behavior, or whether the operation is reversible.

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?

The description is a single sentence, front-loaded with the core action. It is concise but could be slightly more structured or informative without adding length.

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?

Given the low complexity (2 params, no output schema), the description provides the basic purpose. However, it lacks usage context and behavioral details, making it only minimally 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 coverage is 100% with clear descriptions for both parameters. The description adds no new meaning beyond the schema, which already defines dataset_id (required) and name (optional with default).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (create) and resource (chat assistant) with a specific basis (dataset). It distinguishes from siblings 'chat' and 'list_datasets' by implying creation vs. usage or listing.

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?

No explicit guidance on when to use this tool vs. alternatives (e.g., 'chat' might be for interacting). No mention of prerequisites (e.g., dataset must exist) or when not to use it.

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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