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Local AI MCP

Local AI MCP

Official
by TMHSDigital

Cancel pull

pull_cancel
Idempotent

Stops a background model pull started with pull_model when background is true; returns finished jobs unchanged.

Instructions

Stop a background pull started with pull_model { background: true }. Finished jobs are returned unchanged.

Input Schema

NameRequiredDescriptionDefault
jobIdYesJob id returned by pull_model

Output Schema

NameRequiredDescriptionDefault
jobYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.6.1

TDQS

A4/5.0
Behavior3/5

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

The annotations already declare readOnly=false, idempotent=true and destructive=false, so the safety profile is covered. The description adds one useful behavioral fact — completed jobs are returned unchanged, reinforcing the idempotent semantics — but says nothing about what happens to in-flight state, partial downloads, or whether resources are freed.

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?

Two short sentences, zero filler, with the scoping condition (background:true) front-loaded and the edge-case behavior second. Every clause carries information.

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

Completeness4/5

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

For a single-parameter mutation with rich annotations and an output schema (so return values need no explanation), this is nearly complete; the only missing piece is what an agent should do if the job is already finished or unknown, and whether partially downloaded data is retained.

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% and the single jobId parameter is already documented as "Job id returned by pull_model". The description reinforces the provenance of that id via the pull_model reference but adds no format, validation, or error-case semantics beyond the schema.

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 and resource ("Stop a background pull") and qualifies it with the exact originating call (pull_model { background: true }), which cleanly separates it from the sibling pull_status (which monitors rather than cancels).

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?

Gives a clear precondition for use: only pulls started with background:true are cancellable, implying foreground pulls are out of scope. It does not explicitly name an alternative (e.g. pull_status for monitoring progress) or state what to do if the job already completed, so it falls short of full when/when-not routing.

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