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by SamuealPK
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ask_open_model

Run simple AI tasks such as summaries, searches, routine refactors, or tests on open models hosted by other BitPull users to save your own tokens; usage costs BitPull credits.

Instructions

Ask an open model (Qwen, Llama, Mistral, DeepSeek…) running on another BitPull user's computer. Use it for simple tasks (summaries, searches, routine refactors, tests) to save your own tokens. Costs BitPull credits.

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": "ask_open_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
tierNoModel size: small for simple tasks, large for harder ones
modelNoExact model name, e.g. qwen2.5:7b; overrides tier
promptYes
systemNo
maxTokensNo
placementNoauto (default): the person's own network computers first, for free, then the community. mine: never leaves the person's own computers; use it for private data.
waitMinutesNoHow long to wait for the answer; default: the person's setting (10 minutes). Models on a lender's processor can be slow.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

Beyond openWorldHint, the description discloses two genuinely important traits: execution happens on another person's machine (implying the prompt leaves the caller's environment) and each call consumes BitPull credits. It does not discuss availability risk (lender offline), timeouts, or what happens if no model is found, which the waitMinutes parameter only hints at.

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?

Three short sentences, front-loaded with what the tool does before the usage hint and the cost note. The parenthetical model list is slightly padded but does real work by signalling the model family the agent should expect.

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?

With 7 parameters, no output schema, and only a single openWorldHint annotation, the description carries a lot of burden and leaves gaps: it never says what a successful call returns (presumably the model's text reply) or how failures/timeouts surface. The cost and placement story is reasonably covered between the description and inline schema text.

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 57% and the description adds essentially no parameter semantics — tier, placement, model, and waitMinutes are already explained inline in the schema, while system and maxTokens remain undocumented in both places. The model-name list vaguely echoes the 'model' parameter but the schema's own 'qwen2.5:7b' example is more useful.

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 (ask an open model) and adds concrete detail about which models (Qwen, Llama, Mistral, DeepSeek) and where they run (another BitPull user's computer). It does not explicitly contrast itself with the sibling 'ask_ai_model' or 'ask_group_model', so the agent must infer the distinction from the phrase 'open model' alone.

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 positive usage context with concrete task examples ('summaries, searches, routine refactors, tests') and a rationale (save your own tokens). It does not state when NOT to use this, nor name the hosted-model sibling that would be the alternative for harder or private work, leaving that inference to the agent.

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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