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local-llm-delegation-mcp

local-llm-delegation-mcp

by mrrodriguez
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

get_local_llm_usage_stats

Retrieve and summarize local LLM usage statistics to monitor token consumption and optimize delegation costs.

Instructions

Retrieve and summarize usage statistics for the local LLM MCP server.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_local_llm_usage_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior3/5

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. It states the tool 'retrieve' and 'summarize' statistics, implying a read-only, non-destructive operation, but does not disclose any potential side effects, rate limits, or the specific nature of the statistics returned. This adds some value but lacks depth.

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 a single, concise sentence that front-loads the action and resource. It contains no unnecessary information or repetition, making it perfectly efficient.

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

Completeness5/5

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

For a simple, zero-parameter tool with an output schema present, the description is sufficiently complete. It clearly communicates the tool's purpose without needing to explain return values or parameters. The tool's simplicity relative to its siblings means no additional context is required.

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 tool has zero parameters, and the schema coverage is 100%, meaning there is nothing for the description to add about parameter behavior. The baseline score of 4 is appropriate as the description is not required to clarify any parameter details.

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 clearly states the tool's function: 'Retrieve and summarize usage statistics for the local LLM MCP server.' It uses a specific verb and resource, and distinguishes itself from sibling query tools by focusing on statistics rather than querying.

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 implies usage for obtaining usage statistics, but provides no explicit guidance on when to use this tool versus the sibling query tools. No alternatives or exclusions are mentioned, leaving the agent to infer the appropriate context.

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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