llama-mcp-server

by ahays248

Overview Schema Source Related Servers Discussions Admin

TypeScript

Local

llama_chat

Generate chat responses using a local LLM via llama.cpp. Supports OpenAI-compatible parameters for customization.

Instructions

Chat completion (OpenAI-compatible format)

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": "llama_chat"
  }
}

Input Schema

Name	Required	Description
`messages`	Yes	Chat messages
`max_tokens`	No	Maximum tokens to generate
`temperature`	No	Sampling temperature (0-2)
`top_p`	No	Nucleus sampling threshold
`stop`	No	Stop sequences
`seed`	No	Random seed for reproducibility

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Jun 12, 2026

TDQS

C2.9/5.0

Behavior2/5

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

Lacking annotations, the description fails to disclose behavioral traits such as whether a model must be loaded, rate limits, or side effects. It only states the basic purpose without any operational details.

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?

The description is a single, concise sentence that is front-loaded and efficient. However, it omits valuable details, which might be considered under-specification rather than conciseness.

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?

Given the tool has 6 parameters and no output schema, the description is too minimal. It does not explain the return format, error handling, or provide enough context for an agent to use the tool effectively.

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 100%, so the description adds no additional parameter meaning beyond the schema. Baseline of 3 is appropriate as the description does not enhance understanding of parameters.

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 it performs chat completion in an OpenAI-compatible format, which is a specific verb+resource. However, it does not explicitly distinguish from sibling tool llama_complete, though the 'chat' qualifier implies a different use case.

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 like llama_complete or other siblings. No explicit context, prerequisites, or exclusions are provided.

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