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INITE Visibility

What to fix, in order

visibility_fixes
Read-onlyIdempotent

The fix plan for a site's AI visibility, most important first: what to change, why it matters for being named in AI answers, how many points of the score it should recover, the pages it applies to, and a paste-ready file or snippet when there is one (llms.txt, robots.txt rules, Organization schema). Free from the identity reading, no account; signed in, it is built from your latest finished audit of that site, which covers the answer engines too.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesA public website, e.g. example.com or https://example.com
limitNoHow many fixes to return, most important first (default 12)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
fixesYes
sourceYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/openWorld/non-destructive, so the safety burden is covered. The description adds genuinely useful behavioral context beyond them — no account needed for the identity-reading-free version, and signed-in results derive from the latest finished audit covering answer engines.

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?

The main clause leads with the deliverable ('the fix plan...most important first') so it is well front-loaded. The core is one very long, densely-comma'd sentence, which is efficient but slightly hard to scan; the account/audit note is a reasonable trailing fragment.

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?

With an output schema present, return values need not be re-explained, yet the description covers the fix contents, fallback modes (anonymous vs signed-in), and the source of data. Nothing essential for calling it correctly is missing.

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 coverage is 100%, so url and limit are already fully documented in the schema (including the default of 12 and the 1-30 range). The description's 'pages it applies to' and 'most important first' phrasing adds only marginal meaning beyond the schema, so the 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?

The description states a concrete verb+resource ('the fix plan for a site's AI visibility, most important first') and enumerates exactly what each fix contains (what to change, why it matters, point recovery, pages, paste-ready snippet). This is specific enough that an agent can distinguish it from analyze_site, get_analysis, or visibility_history on purpose alone.

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?

It clearly gives operating context: usable free with no account versus signed in being built from the latest finished audit of that site. That tells the agent when the richer result applies. It does not, however, name or contrast with sibling tools (e.g. get_analysis vs visibility_history), so it stops short of explicit alternative routing.

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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