ragflow-kb-mcp

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ragflow_replace_chat_settings

Replace a chat assistant's model and prompt settings using PUT semantics, so provided llm_setting and prompt_config objects overwrite stored configurations.

Instructions

Update a chat assistant with PUT semantics: llm_setting and prompt_config you pass replace the stored objects.

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>"
    },
    "name": "ragflow_replace_chat_settings"
  }
}

Input Schema

Name	Required	Description
`name`	No
`top_k`	No
`top_n`	No
`llm_id`	No
`chat_id`	Yes	Chat assistant ID
`language`	No
`rerank_id`	No
`dataset_ids`	No	Datasets the assistant retrieves from
`description`	No
`llm_setting`	No	Replaces the whole model settings object
`prompt_config`	No	Replaces the whole prompt settings object
`meta_data_filter`	No
`similarity_threshold`	No
`keywords_similarity_weight`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.1Oct 29, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already declare readOnly=false and destructive=false, so the safety bar is lower, but the description adds genuinely useful context by warning that llm_setting and prompt_config overwrite (not merge) stored objects. It stops short of saying whether the remaining scalar fields also replace or are partial, leaving a behavioral gap.

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 zero waste, front-loading the most decision-relevant fact (PUT replace semantics) immediately after the verb.

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?

For a 14-parameter mutation tool, the description omits two things an agent needs: how it differs from ragflow_update_chat, and the merge/replace behavior of the non-nested fields. The output schema covers return values, but the update semantics remain under-specified.

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 only 29%, so compensation is needed. The description explains replace behavior for llm_setting and prompt_config (matching their schema descriptions) but says nothing about the other twelve parameters, including whether dataset_ids, meta_data_filter, or scalars merge or replace.

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 (update/replace) and resource (chat assistant settings) and layers on the PUT replace semantics, which is concrete. However it never names or distinguishes itself from the obvious sibling ragflow_update_chat, so an agent can't tell from the description alone which to pick.

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?

'PUT semantics' implies this is for full-replacement updates, but the description gives no explicit when-to-use, when-not, or alternative tool. The agent must infer the routing decision against ragflow_update_chat.

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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