Skip to main content
Glama
Sign In
ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_create_chat

Create a chat assistant that answers questions from specified datasets, with configurable retrieval ranking, model settings, and prompts.

Instructions

Create a chat assistant that answers from the given datasets.

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": {
      "name": "<name>"
    },
    "name": "ragflow_create_chat"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesChat assistant name; a duplicate gets a suffix
top_kNoCandidates before reranking
top_nNoChunks passed to the model
llm_idNoChat model as model_name@factory; defaults to the tenant's model
languageNo
rerank_idNoRerank model as model_name@factory
dataset_idsNoDatasets the assistant retrieves from
descriptionNo
llm_settingNoModel parameters such as temperature, top_p, presence_penalty, frequency_penalty, max_tokens
prompt_configNoPrompt settings: system (prompt text, use {knowledge} for retrieved chunks), prologue, empty_response, quote, refine_multiturn, reasoning, cross_languages, parameters
meta_data_filterNoMetadata filter applied to retrieval
similarity_thresholdNo
keywords_similarity_weightNoWeight of keyword match; vector weight is 1 minus this

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 already declare the mutation/safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=false), so the description's burden is lower, but it adds essentially nothing: no note that dataset_ids must reference existing datasets, no default behavior for the tenant model, no effect of omitting optional settings.

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 lean sentence with the core purpose front-loaded. It is efficient but borders on under-specified given the tool's 13 parameters.

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

Completeness2/5

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

For a 13-parameter creation tool, one sentence leaves real gaps: the relationship between dataset_ids and the assistant's retrieval, defaults for llm_id and llm_setting, and the {knowledge} prompt convention are all left to the schema. The presence of an output schema and annotations helps, but the description still under-serves the tool's complexity.

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 77%, so the schema already explains most parameters, including llm_id, prompt_config, and keywords_similarity_weight. The description adds no parameter-level meaning beyond what the schema provides, so the 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?

Clear specific verb+resource: 'Create a chat assistant' plus the key behavior 'answers from the given datasets'. It does not, however, distinguish this from sibling creation tools like ragflow_create_agent or ragflow_create_search_app, which an agent must choose between.

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 guidance on when to use this versus ragflow_create_agent, ragflow_create_search_app, or ragflow_create_chat_session, and no prerequisites (e.g. datasets must already exist) are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools