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BitPull

BitPull

by SamuealPK
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

bitpull_download_model

Downloads an open AI model to your computer so it can be shared with the network, checking available disk space and memory before starting.

Instructions

Download an open AI model so this computer can lend it. Check free disk space and memory first.

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": {
      "model": "<model>"
    },
    "name": "bitpull_download_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

Annotations only declare destructiveHint=false, so the description carries most of the burden; it usefully discloses the resource preconditions (disk, memory) that a caller must verify, but says nothing about download size, duration, resumability, where the model is sourced from, or what state the machine is left in afterward.

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?

Two short, front-loaded sentences with no filler; the action comes first and the precondition second. Slight ambiguity from the vague pronoun "it" and the marketing-ish phrase "lend it".

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?

For a one-parameter acquisition tool with no output schema and near-empty annotations, the description leaves the accepted identifier format, post-download outcome, and failure modes for insufficient disk/memory entirely unaddressed. An agent could not reliably construct the call from this text alone.

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

Parameters2/5

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

The single required parameter "model" has 0% schema description coverage (just a 1-100 char string), and the description never explains whether it expects a model name, hub ID, or URL, nor what a valid value looks like. With low coverage the description should compensate and it does not.

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?

States a specific verb and resource ("Download an open AI model") plus the intended outcome (the machine lends it), which distinguishes it from siblings like ask_open_model and use_open_model that presumably consume rather than acquire a model. It stops short of explicitly naming that contrast.

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 sentence "Check free disk space and memory first" gives a concrete precondition for invoking the tool, but there is no guidance on when to choose this over adjacent actions (using an existing model, adding a model folder) or on what to do when the check fails.

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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