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Bisibility

by CorgiCorner
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Compare AI prompts

compare_ai_prompts

Compare answers from multiple AI models to the same synthetic prompt, including brand mentions and citations, for side-by-side analysis.

Instructions

Run the same synthetic prompt against supported models and compare answers, brand mentions and returned citations. These tests are not observed ChatGPT/AIO visibility. Requires a guaranteed hard cap or explicit actual-cost consent with an advisory estimate limit, and project write scope.

Input Schema

NameRequiredDescriptionDefault
brandYesBrand name to inspect.
freshNoHard-cap mode bypasses stored results. Actual-cost retries always reuse their durable request identity; fresh never re-dispatches a pending, unknown or completed attempt.
domainYesLocal citation-matching hostname, without a scheme or path. Domain searches remove a leading www. only from the provider filter and require at most 63 characters after removal; local matching keeps the full hostname. Brand searches and prompt comparisons allow up to 253.
modelsNoDistinct model IDs advertised by get_ai_research_catalog.
promptYesSame synthetic prompt for each model; maximum 2000 UTF-8 bytes.
project_idYesThe public project ID. Project membership and token scopes are enforced.
web_searchNoEnable provider web search when supported.
cost_policyNoHard cap or explicitly consented non-guaranteed actual provider cost.hard_cap
estimate_onlyNoReturn the cost estimate without paid provider I/O.
max_cost_centsNoMaximum accepted provider cost in cents. Estimates and cache reads do not spend credits.
idempotency_keyNoStable retry key sent as a header; actual-cost mode requires a UUID also sent in the body.
country_iso_codeNoCountry hint; requires web search.
max_output_tokensNoMaximum output tokens; provider limits also apply.
response_languageNoRequested response language.en
estimate_credentials_refNoCredential identity from the estimate, required for actual-cost execution.
estimated_cost_limit_centsNoAdvisory estimate limit in cents for actual-cost consent.
actual_cost_acknowledgementNoExplicit acknowledgement that actual provider cost has no guaranteed maximum.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.17.1

TDQS

A4.3/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint=false, openWorldHint=true), the description discloses substantial behavior: it requires either a guaranteed hard cap or explicit actual-cost consent with an advisory estimate limit, requires project write scope, and warns the tests are synthetic rather than observed visibility. This is exactly the kind of cost/auth/caveat context annotations cannot carry.

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 tight sentences, front-loaded with the primary action, then the visibility caveat, then the requirements. No waste, though the requirements sentence stacks two distinct constraints and could be split for cleaner scanning.

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 17-parameter tool with a complex oneOf cost model, no output schema, and no cost-relevant annotations, the description covers the essential cost-consent, scope, and caveat context and even sketches the return content (answers, brand mentions, citations). It stops short of the idempotency/retry semantics, but those are covered in the schema.

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 every parameter (cost_policy, max_cost_cents, estimate_credentials_ref, etc.) is already documented in the schema. The description restates the cost-policy concept at a high level but adds no parameter-level detail beyond what the schema provides, so baseline 3 applies.

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?

States a specific verb and resource (run the same synthetic prompt against supported models and compare answers, brand mentions, citations) and explicitly carves out what it is not ('not observed ChatGPT/AIO visibility'), which separates it from the ai_tracking and ai_visibility siblings. An agent can tell what this does without opening the schema.

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 'not observed ChatGPT/AIO visibility' line gives clear context for when this tool is the wrong choice versus observed-visibility tools, and the cost/scope prerequisites are stated. However, it never names a concrete alternative sibling to route to, so the when-not guidance is implied rather than explicit.

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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