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ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_delete_chat_message

Destructive

Deletes a question and its answer from a chat session by message ID, returning the remaining messages.

Instructions

Delete a question and its answer (they share one message ID) from a chat session; returns the remaining messages.

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": {
      "chat_id": "<chat_id>",
      "session_id": "<session_id>",
      "message_id": "<message_id>"
    },
    "name": "ragflow_delete_chat_message"
  }
}

Input Schema

NameRequiredDescriptionDefault
chat_idYesChat assistant ID
message_idYesMessage ID within the session
session_idYesSession ID

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true and readOnlyHint=false, so the description needn't restate that this is destructive. It does add real behavioral value beyond the annotations: deleting one message ID removes two records (question plus answer) and the call returns the remaining messages, which affects how an agent should interpret the result.

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 sentence with the destructive scope front-loaded and the return behavior appended compactly. No filler, no repetition of the name.

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

Completeness4/5

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

For a destructive 3-parameter tool the description covers scope, the paired-deletion side effect, and the return, and an output schema exists so return shape needn't be spelled out. It stops short of stating irreversibility or behavior when the ID is not found, which would matter for a delete operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3, but the description adds genuine semantic detail: message_id refers to a paired question-and-answer unit rather than a single message, which the schema's 'Message ID within the session' does not convey. chat_id and session_id remain unexplored beyond their schema text.

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?

States a specific verb (delete) and resource (a question and its answer within a chat session), and clarifies the non-obvious fact that both share one message ID. That detail distinguishes it from siblings like ragflow_delete_chat_sessions or ragflow_delete_chats without opening their schemas.

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 implied by the phrase 'from a chat session', but there is no explicit when-to-use guidance and no mention of alternatives such as deleting an entire session versus a single message pair. An agent can infer context but must reason about scope itself.

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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