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BitPull

BitPull

by SamuealPK
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list_ai_models

Read-only

Lists available proprietary AI models and their credit prices through BitPull, helping you select a model before making requests via official provider APIs.

Instructions

Proprietary AI models (e.g. Claude, GPT, Gemini) that BitPull offers through official APIs, with their price in credits. Requests to these models pass through the BitPull server to the AI provider (Anthropic, OpenAI, Google…): they are NOT end-to-end encrypted. Do not send secrets or personal data the user has not agreed to share. Paid in BitPull credits.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_ai_models"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations only declare readOnlyHint, so the description adds real value by disclosing that requests are proxied through the BitPull server rather than end-to-end encrypted and that usage is paid in credits. It stops short of describing the shape of the returned list or any filtering/pagination behaviour.

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?

Three short sentences, front-loaded with what the models are, followed by the security caveat and the payment note. Every sentence carries distinct information an agent would act on.

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 no-parameter, read-only catalogue tool with no output schema, the description covers the essentials: what is listed, the price unit, and the privacy constraint on downstream requests. Only the structure of the returned entries is left unstated.

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

Parameters4/5

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

The tool takes no parameters and the schema has zero properties, so there is nothing for the description to clarify. Baseline applies.

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?

The description names the specific resource precisely: proprietary AI models offered through official APIs, with price in credits, and gives concrete examples (Claude, GPT, Gemini). It does not, however, distinguish itself from close siblings such as list_group_models or list_open_models, so an agent cannot tell from the text alone which listing tool to pick.

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?

Usage is only implied: an agent can infer this is the catalogue used to check availability and credit prices before calling ask_ai_model. There is no explicit when-to-use or when-not-to-use statement, and no mention of the sibling listing tools that cover group or open models.

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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