mcp-ragchat

ragchat_test

Test a domain's chat by sending a message that triggers RAG search and LLM generation, mimicking production to verify the knowledge base is functioning correctly.

Instructions

Send a test message to a domain's chat. Uses RAG search + LLM to generate a response, same as production. Good for verifying the knowledge base works.

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": {
      "domain": "<domain>",
      "message": "<message>"
    },
    "name": "ragchat_test"
  }
}

Input Schema

Name	Required	Description	Default
`domain`	Yes	Domain to test
`message`	Yes	Test message (e.g. 'What is your product?')

Schema Changelog

Changes observed during successful MCP inspections.

AddedApr 30, 2026

TDQS

A3.8/5.0

Behavior3/5

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

It discloses that the tool uses RAG search + LLM and mirrors production behavior, but omits potential side effects, authentication requirements, or error handling. With no annotations, this is a moderate disclosure.

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?

Two sentences, no unnecessary words. The information is front-loaded and efficient.

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 simple test tool with no output schema, the description covers the core function and use case. It could mention the response format, but it's reasonably complete given the context.

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 the schema already defines 'domain' and 'message'. The description adds an example message but does not significantly enhance parameter meaning beyond the schema.

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?

The description explicitly states the tool sends a test message to a domain's chat using RAG search and LLM, identical to production. It clearly distinguishes from siblings like ragchat_serve or ragchat_setup by focusing on testing.

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?

The description says 'Good for verifying the knowledge base works,' implying a testing use case, but lacks explicit when-not-to-use or alternatives among siblings.

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

Deploy Server