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local_llm

Run private or bulk text tasks on a local LLM, keeping data on your machine. Pass file paths for local processing; only the model's answer returns.

Instructions

Run a task on a local LLM on this machine. Nothing is sent to the cloud.

Use it for (1) private data that must not leave the machine: pass file paths in files instead of reading them yourself; this server reads them and only the local model's answer comes back to you; and (2) bulk, mechanical text work (summarizing, classifying, translating, reformatting) where a smaller model is good enough. Do not use it for tasks that need strong reasoning or careful code changes.

Files must be under the home directory or /tmp and must not be hidden (no path component starting with ".").

Args: prompt: The instruction for the local model. Write it self-contained; the model sees nothing else. files: Text files to include after the prompt (UTF-8). Total input is capped (~60k characters). output_path: If set, the answer is written to this new file and only a short notice is returned, so even the answer stays out of the conversation. Existing files are never overwritten. system: Optional system prompt. max_tokens: Maximum tokens to generate. thinking: Enable the model's thinking mode, if it has one (slower, sometimes more accurate).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filesNo
promptYes
systemNo
thinkingNo
max_tokensNo
output_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A5/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and delivers: privacy guarantee, path restrictions (under home or /tmp, no hidden components), an input size cap (~60k chars), non-overwrite safety for output_path, and the fact that output_path suppresses the answer from the conversation. These are substantive behavioral facts an agent could not infer.

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?

Front-loaded with the core purpose, then use cases, then constraints, then a clean Args block. Dense but every sentence adds a fact that affects invocation (privacy, path rules, overwrite behavior, thinking tradeoff). No filler.

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?

An output schema exists, so return values need not be described. Combined with full parameter semantics, safety constraints, and usage routing, an agent has everything needed to call this correctly and predict the side effects.

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

Parameters5/5

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

Schema coverage is 0%, so the description must compensate, and it documents all six parameters: prompt is self-contained instruction, files are UTF-8 text appended after the prompt, output_path redirects the answer and never overwrites, system is an optional system prompt, max_tokens and thinking are explained including the slower/sometimes-more-accurate tradeoff. No parameter is left to guesswork.

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?

States a specific verb+resource ('Run a task on a local LLM on this machine') and immediately distinguishes the scope with 'Nothing is sent to the cloud', which is the defining characteristic versus a remote LLM tool. An agent knows exactly what this does and what makes it different.

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

Usage Guidelines5/5

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

Explicitly enumerates two use cases (private data that must not leave the machine; bulk mechanical text work) and explicitly states when not to use it ('tasks that need strong reasoning or careful code changes'). It even explains the alternative pattern for private data (pass file paths rather than reading them yourself). This is textbook when/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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