Skip to main content
Glama

Exepal

ai_visibility

Read-only

AI brand visibility report (20 prompts, all engines). Price: $3.00 USDC per call on Base, paid with x402; not charged if the call fails.

Input Schema

NameRequiredDescriptionDefault
brandYesBrand to measure, e.g. Notion
domainNoBrand website, e.g. notion.com
marketNoCountry or market, e.g. US
enginesNoSubset of openai, anthropic, google, perplexity
categoryYesProduct category the prompts are about, e.g. note-taking apps
competitorsNoCompetitor brand names
promptCountNoPrompts to ask, capped by the tier

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already cover read-only and open-world behavior, so the description's real contribution is the cost model: $3.00 USDC on Base via x402, with no charge on failure. That is exactly the kind of context an agent needs before invoking a paid tool, though latency and rate-limit behavior remain undisclosed.

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?

Two short sentences with no filler: purpose and scope first, cost model second. Nothing repeats the name or title unnecessarily and every clause carries information.

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?

With no output schema, the description should say something about what the report contains — visibility scores, per-engine breakdown, competitor comparison — but it does not. Payment behavior is well covered and annotations carry the safety profile, yet the return shape and tier relationship remain gaps.

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 is already documented, and this sets the baseline at 3. The description adds only that 20 prompts and 'all engines' are the defaults, without explicitly tying those to promptCount or engines, so it adds marginal value over the schema.

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 output ('AI brand visibility report') with concrete scope (20 prompts, all engines), which tells an agent what it gets. It sits between ai_visibility_quick and ai_visibility_deep but never names either sibling, so differentiation is only implicit via the '20 prompts' detail.

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?

No guidance on when to choose this over ai_visibility_quick or ai_visibility_deep, which are the obvious alternatives in the sibling list. The only decision-relevant fact given is price, which is insufficient to route between tiers.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.