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ragflow-kb-mcp
by feci

ragflow_rate_chat_message

Rate an assistant answer by giving a thumbs up or down, with optional feedback to flag bad responses in a RAGFlow chat session.

Instructions

Give thumbs up or down, with optional feedback, to an assistant answer.

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>",
      "thumbup": "<thumbup>"
    },
    "name": "ragflow_rate_chat_message"
  }
}

Input Schema

NameRequiredDescriptionDefault
chat_idYesChat assistant ID
thumbupYestrue for a good answer, false for a bad one
feedbackNoWhy the answer was bad (kept only with thumbup false)
message_idYesID of an assistant message
session_idYesSession ID

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 declare readOnlyHint=false, destructiveHint=false, and openWorldHint=false, so the mutation is known to be a non-destructive local write. The description adds nothing beyond that: it does not say the rating persists on the session, whether it overwrites a prior rating, or whether feedback is discarded when thumbup is true (that rule lives only in the schema).

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?

One short sentence, front-loaded with the action and zero filler. It is efficient, though the terseness leaves room that could have been spent on the workflow prerequisites rather than omitted entirely.

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 and annotations cover the safety profile, so return values and risk do not need explaining. However, for a 5-parameter mutation tied to chat/session/message IDs, the description omits how to obtain those IDs and what the rating affects, leaving it minimally viable rather than 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 description coverage is 100%, so chat_id, session_id, message_id, thumbup, and feedback are all already documented in the schema, including the 'feedback kept only with thumbup false' rule. The description's mention of thumbs up/down and optional feedback merely restates those fields at a lower resolution, so 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?

The description states a specific verb and resource: rating (thumbs up/down) a chat message that contains an assistant answer. This is distinguishable from siblings like ragflow_delete_chat_message or ragflow_chat, though it never names a sibling to contrast with.

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 when-to-use or when-not-to-use guidance, no prerequisites (e.g. needing an existing message_id from a completed chat turn), and no mention of what to do instead if the user simply wants to re-ask. The agent must infer the entire workflow context.

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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