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WhiteNightShadow

camoufox-reverse-mcp

verify_signer_offline

Verifies a signing function offline by comparing its output against expected values from user-provided samples, returning pass rate and first point of divergence.

Instructions

Offline verify a signing function against user-provided samples.

Typical workflow:

  1. Capture real signed requests via network_capture + list_network_requests

  2. Extract samples into a list

  3. Write candidate signing code

  4. Call this tool -> get pass_rate + first_divergence

  5. Iterate

Args: signer_code: JS evaluating to a function: (sample) => {param: computed_value}. Runs in current page context. samples: List of sample dicts, each with: - id: user-defined identifier - input: dict passed to signer function - expected: dict of {param_name: expected_value_str} compare_params: Which params to compare. If None, compare all keys in each sample's expected.

Returns: dict with total_samples, passed, failed, pass_rate, first_divergence, details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
signer_codeYes
samplesYes
compare_paramsNo
Behavior3/5

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

No annotations are provided, so the description must disclose behavioral traits. It states that signer_code runs in current page context (implying potential side effects) and returns pass_rate and first_divergence. However, it does not explicitly warn about possible mutations or safety. This is adequate but not fully transparent.

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 concise and well-structured: a one-sentence purpose, a numbered workflow list, and an Args section. Every sentence adds value, and the format is easy to parse. No redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description lists the return fields (total_samples, passed, failed, pass_rate, first_divergence, details). The tool's complexity (3 params, JS execution) is fully addressed: workflow, parameter structures, and return values are all covered. The description is complete for an agent to correctly invoke the tool.

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

Parameters5/5

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

The input schema has 0% description coverage, so the description carries full burden. It explains signer_code as JS evaluating to a function with signature (sample) => {...}, details the structure of each sample (id, input, expected), and clarifies compare_params default behavior. This adds significant meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'Offline verify a signing function against user-provided samples' with a specific verb and resource. It clearly differentiates from sibling tools like evaluate_js or hook_function by focusing on offline verification of signing code with sample comparisons.

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?

The description provides a typical 5-step workflow that contextualizes when to use this tool (after capturing samples and writing candidate code). It does not explicitly state when not to use it, but the workflow implies it is for testing signers post-capture. The guidance is clear and useful.

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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