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sandcastlelabs

collimer-mcp

Collimer AI-visibility scan

collimer_scan
Read-only

Run a free scan to measure brand visibility in AI search — ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. Returns an AI-visibility score (0-100), confidence interval, biggest gap, and report URL.

Instructions

Run a free Collimer scan on a website to measure how visible its brand is in AI search — ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. Returns an AI-visibility score (0–100), a confidence interval, the single biggest gap, and a branded report URL. The full report (share of voice across each engine + every recommendation) unlocks with a free account on the web. Tip: after the site makes changes, re-run the scan to measure the delta.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailNoOptional — emails the report and speeds claiming the account later.
domainYesThe website to scan, e.g. 'example.com' or 'https://example.com'.
Behavior4/5

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

Annotations indicate readOnlyHint=true and openWorldHint=true; description adds context about free scan, return values, account unlocking, and re-running for deltas. No contradictions.

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?

Description is three sentences plus tip, front-loaded with purpose. Efficient but first sentence is slightly long.

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?

Given 2 parameters and no output schema, description explains return values (score, confidence interval, gap, report URL) and mentions tip about re-running. Fully covers what agent needs.

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?

Both parameters (domain, email) have descriptions in schema. Description adds no extra meaning beyond what's already in schema. Schema coverage is 100%, baseline score of 3.

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?

Description clearly states it runs a scan on a website to measure AI visibility, listing specific AI models and return values. It distinguishes from sibling (beacon_free_scan) by name but does not explicitly differentiate functionality.

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?

Describes when to use (to measure AI visibility) and includes a tip to re-run after changes. However, no explicit guidance on when not to use or comparison with sibling tool.

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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