infocepo-infra-mcp

by ynotopec

Overview Schema Source Related Servers Discussions Admin

Python

Remote

llm_chat

Chat with an LLM to get answers, reason through problems, or generate code using configurable models and settings.

Instructions

Chat completions using the infocepo LLM API (OpenAI-compatible). Supports chat, reasoning, code generation.

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": {
      "messages": "<messages>"
    },
    "name": "llm_chat"
  }
}

Input Schema

Name	Required	Description
`model`	No	Model name: ai-default, ai-thinking, ai-fast, ai-embedding, ai-stt, ai-tts, ai-image, ai-vision
`messages`	Yes	Chat messages array: [{role: 'user'\|'system'\|'assistant', content: 'text'}]
`max_tokens`	No	Max tokens in response.
`temperature`	No	Sampling temperature (0-2). Default 0.7.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 30, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as cost, rate limits, idempotency, or side effects. For a generative API, basic transparency about resource usage is missing.

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 extremely concise with two sentences, no redundancy, and front-loaded with the core purpose. Every word earns its place.

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?

Despite good schema coverage, the description lacks details about return values, error handling, streaming, or authentication. With no output schema, the agent is left without context on what to expect from a response.

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?

Input schema has 100% description coverage, so parameters are already documented. The description adds minimal extra meaning beyond the schema, only mentioning supported capability categories.

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?

Description clearly states it provides chat completions using infocepo LLM API and lists supported capabilities (chat, reasoning, code generation). However, it does not differentiate from sibling tools like llm_vision or embeddins_create, leaving some ambiguity about scope.

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?

No guidance on when to use this tool versus alternatives or when not to use it. The description only lists what it supports, lacking context for tool selection.

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