Skip to main content
Glama
Sign In
BitPull

BitPull

by SamuealPK
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

ask_ai_model

Send a prompt to a proprietary AI model from Anthropic, OpenAI, or Google through BitPull, billed in credits; pick a model ID from the available list first.

Instructions

Ask a proprietary AI model offered by BitPull (see list_ai_models). Costs credits per token. 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

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>",
      "prompt": "<prompt>"
    },
    "name": "ask_ai_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelYesModel id from list_ai_models
promptYes
systemNo
maxTokensNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does substantial work: it discloses per-token credit cost, that requests transit the BitPull server to third-party providers, and that traffic is NOT end-to-end encrypted. It stops short of describing failure modes (insufficient credits, provider errors) or the shape of the returned content.

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?

Four compact sentences, front-loaded with the purpose and the model-list pointer, followed by the cost and privacy warnings. The closing 'Paid in BitPull credits' mildly duplicates 'Costs credits per token', but nothing is padded or buried.

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?

For a 4-parameter, no-annotation, no-output-schema tool, the definition covers purpose, cost, and data-handling risk well. It is still incomplete on response contents and on how system/maxTokens should be set, which an agent must currently guess at from the schema 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?

Schema coverage is only 25% – just 'model' is documented, and the description largely repeats that pointer. The prompt, system, and maxTokens parameters (the latter capped at 128000) get no added meaning, even though the 'costs credits per token' note makes maxTokens a cost-relevant choice the agent should understand.

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 a proprietary AI model offered by BitPull', and points to list_ai_models for valid model ids. The word 'proprietary' implicitly separates it from ask_open_model/ask_group_model, but it never names those siblings outright, so the differentiation is inferential rather than explicit.

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?

Gives a clear precondition (model id must come from list_ai_models) and a strong constraint ('do not send secrets or personal data the user has not agreed to share'). It does not say when to choose this tool over ask_group_model, ask_open_model, or use_open_model, leaving the routing decision to the agent.

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

Deploy Server

Other Tools