MoneyTools Inference Fidelity Audit

fidelity_audit

Audit a seller vs Cloudflare Workers AI reference. Free 2/day then 0.05 USDC. Auth header never stored.

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": {
      "endpoint": "<endpoint>",
      "model": "<model>"
    },
    "name": "fidelity_audit"
  }
}

Input Schema

Name	Required	Description
`model`	Yes	Claimed model id. Supported: llama-3.1-8b, llama-3.2-3b, llama-3.3-70b, llama-4-scout, qwen3-30b, qwen2.5-coder-32b, gpt-oss-20b, gpt-oss-120b, gemma-4-26b, glm-4.7-flash
`apiKey`	No	Unused placeholder for client compatibility; payments use x402 headers / free quota
`endpoint`	Yes	OpenAI-compatible base URL (https://…/v1) or chat completions URL
`authHeader`	No	Optional Authorization value for the target (Bearer …). Used in memory only; never stored or logged.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 10, 2026

changedInput schema / properties / endpoint / description

Previous value: -"OpenAI-compatible base URL (https://…/v1) or chat completions URL, or \"workers-ai\" to use the Workers AI binding as the target (self-check)"New value: +"OpenAI-compatible base URL (https://…/v1) or chat completions URL"

First observedNov 10, 2026

TDQS

B3.3/5.0

Behavior3/5

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

The description adds useful behavior beyond the annotations by disclosing the free quota, the 0.05 USDC charge, and that the auth header is never stored. With readOnlyHint=false and openWorldHint=true already declared, these are meaningful additions, but the description still does not explain what the audit actually performs or what external calls may occur.

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?

The description is extremely concise and front-loaded: purpose first, then pricing, then auth privacy. Every sentence contributes useful information with no filler.

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?

The definition gives enough to identify the tool and its pricing/privacy constraints, and the schema covers all parameters. However, with no output schema, the description should do more to explain what an audit returns or what the result represents.

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 description coverage is 100%, so the input schema already documents the endpoint, model, apiKey placeholder, and authHeader handling. The description adds no parameter-level meaning beyond that baseline.

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 states a specific verb and resource: audit a seller against a Cloudflare Workers AI reference. It is distinguishable from billing and model-list siblings, but it does not explain what the audit actually checks or how it differs from fidelity_quick_check or fidelity_report.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides pricing context but no explicit when-to-use or when-not-to-use guidance relative to the sibling tools. An agent must infer that this is the full audit operation rather than a quick check or report lookup.

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

All MoneyTools Inference Fidelity Audit tools Inspect

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