local-llm-delegation-mcp

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query_local_llm

Reduce API costs by delegating simple, well-defined code subtasks to a local LLM, reserving cloud models for complex work.

Instructions

Query the local LLM for simple, well-defined subtasks that have already been broken down. IMPORTANT: Always try this tool FIRST for any simple code generation to save costs!

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": {
      "prompt": "<prompt>"
    },
    "name": "query_local_llm"
  }
}

Input Schema

Name	Required	Description	Default
`model`	No
`prompt`	Yes
`max_tokens`	No
`temperature`	No
`system_message`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 2, 2026

TDQS

B3.4/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It tells the agent this is a query operation and mentions cost savings, but does not disclose output format, potential errors, latency, or any side effects. The behavior of the tool beyond 'query' is not explained, which is insufficient for a tool with no annotations.

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 just two sentences, front-loaded with the core purpose and followed by a clear actionable instruction. Every word earns its place with no fluff or redundant details.

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?

The tool has 5 parameters and an output schema, but the description only covers the simple-subtask context and cost-saving rationale. It does not explain when to use an alternative like query_local_llm_with_context, nor does it elaborate on how to configure the optional parameters. The output schema provides some completeness, but the description itself leaves gaps for a robust agent decision.

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

Parameters1/5

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

Schema description coverage is 0% and the description does not mention any parameter names or meanings. The agent is left only with parameter names like 'prompt', 'model', 'max_tokens', which are self-explanatory but not explicitly documented. The description adds no semantic value to the parameters.

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 states 'Query the local LLM for simple, well-defined subtasks that have already been broken down' with a specific verb, resource, and scope. It also distinguishes itself from siblings by being the simple, cost-saving option ('Always try this tool FIRST for any simple code generation to save costs!').

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use guidance: 'Always try this tool FIRST for any simple code generation'. It implies a preference over alternatives for simple tasks, but does not explicitly name exclusions or alternative tools like query_local_llm_with_context. This is clear context without full when-not guidance.

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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