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MCP AI Hub

MCP AI Hub

by feiskyer
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

chat

Interact with various AI models from OpenAI, Anthropic, Google, and AWS Bedrock using a unified interface to generate responses for chat inputs.

Instructions

Chat with specified AI model.

    Args:
        model: Model name from configuration (e.g., 'gpt-4', 'claude-sonnet-4')
        inputs: Chat input (string or OpenAI-format messages)

    Returns:
        AI model response as string

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": {
      "model": "<model>",
      "inputs": "<inputs>"
    },
    "name": "chat"
  }
}

Input Schema

NameRequiredDescriptionDefault
inputsYes
modelYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions the tool chats with AI models, it doesn't describe important behavioral aspects like rate limits, authentication requirements, cost implications, error handling, or whether this is a read-only vs. state-changing operation.

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?

The description is perfectly structured and concise - a clear purpose statement followed by well-organized parameter explanations and return value description. Every sentence earns its place with no wasted words.

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?

Given the tool's complexity (interactive AI chat) and the presence of an output schema (which handles return values), the description is adequate but incomplete. It covers parameters well but lacks behavioral context that would be crucial for an AI agent to use this tool appropriately.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds significant value beyond the 0% schema description coverage by explaining both parameters: 'model' is described as 'Model name from configuration' with examples, and 'inputs' is clarified as 'Chat input (string or OpenAI-format messages)'. This compensates well for the schema's lack of descriptions.

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 clearly states the tool's purpose as 'Chat with specified AI model', which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'get_model_info' or 'list_models', which appear to be informational rather than interactive.

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?

The description provides no guidance on when to use this tool versus alternatives. There's no mention of when to choose 'chat' over the sibling tools 'get_model_info' or 'list_models', nor any context about appropriate use cases or limitations.

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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