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ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_chat

Ask a RAGFlow chat assistant a question and receive an answer with cited source chunks; continue sessions or query the default model without retrieval.

Instructions

Ask a chat assistant a question and get its answer with the cited chunks.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "ragflow_chat"
  }
}

Input Schema

NameRequiredDescriptionDefault
llm_idNoOverride the chat model
chat_idNoChat assistant to ask; without it the tenant's default chat model answers with no retrieval
messagesNoConversation as [{role, content}], last one from the user
questionNoThe user's question (or pass messages instead)
thinkingNoTurn model reasoning on or off where supported
max_tokensNo
session_idNoContinue this session; without it a new session is created
temperatureNo
store_historyNofalse answers from messages without storing a session

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

B3.4/5.0
Behavior3/5

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 and the description does not contradict it. The description adds that answers are grounded in cited chunks, which is meaningful retrieval context, but it omits the mutating side effect (a session is created and history stored by default) that the non-read-only annotation hints at. With annotations present the bar is lower, but more could be said.

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 filler; the return content is stated up front. It is arguably too terse for a 9-parameter tool, but nothing is wasted or buried.

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 carry the safety profile. However, with 9 optional parameters and several overlapping chat siblings, a one-sentence description leaves the agent without guidance on chat_id vs. default-model mode or session reuse, which the schema only partially covers.

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 78%, so the schema already explains llm_id, chat_id, messages, question, thinking, session_id and store_history, including the important 'without it a new session is created' and 'without it the tenant's default chat model answers with no retrieval' semantics. The description adds nothing about parameters, so the baseline 3 applies; max_tokens and temperature remain undocumented anywhere.

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 ('Ask') and resource ('a chat assistant') and says what comes back ('answer with the cited chunks'). It is clear on its own, but does not distinguish itself from overlapping siblings such as ragflow_agent_chat or ragflow_openai_chat_completion, which an agent could plausibly confuse it with.

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 only implied by the phrasing 'ask a chat assistant a question'; there is no explicit when-to-use, when-not-to-use, or pointer to the alternatives (agent chat, openai chat completion, retrieval, related questions). The agent must infer its niche from the name alone.

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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