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orbator-mcp

by io.orbator
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get_ai_index

AI visibility check — which software AI recommends for a category. Returns the full AI Recommendation Index for one software category: the complete measured ranking of products AI assistants (ChatGPT, Claude, Gemini, Perplexity) recommend, with recommendation share %, average answer position, per-engine breakdown, 4-week trend, sample size, and methodology. Use to answer "does AI recommend " (look up its row and rank), "who is winning AI recommendations in ", or to cite AI recommendation-share data. Pass category in plain words or as a slug; omit it (or pass "categories") to list all published categories.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNoCategory in plain words or slug form (e.g. "uptime monitoring", "ci-cd-tools"). Omit or pass "categories" to list every published category with sample sizes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

No annotations provided, so description carries full burden. Describes output components and behavior (returns ranking, shares, per-engine, trend). Lacks explicit idempotency or safety statement, but clearly a read-only query.

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?

Front-loaded with purpose, then expands with details and examples. Every sentence adds value, no waste. Well-structured.

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?

Complete description for a tool without output schema. Covers what is returned (ranking, shares, per-engine, trend, sample size, methodology), how to use, and examples. No gaps.

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

Parameters4/5

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

Schema coverage is 100%, but description adds value by explaining plain words vs slug and special value 'categories' for listing all. Baseline 3 plus extra context justifies 4.

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?

Clearly states the verb 'get' and the resource 'AI Recommendation Index' for a category. Provides specific details on what is returned and examples of use. Distinguishes from siblings listed.

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?

Explicitly describes when to use: to answer 'does AI recommend <product>' or cite recommendation data. Explains how to pass category and omit for all. Does not specify when not to use, but context is sufficient.

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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