orbator-mcp

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

With no annotations, the description carries the full burden. It discloses that the output includes source URLs and verified dates for every claim, and that it compares pricing, features, integrations, and limits. This provides good behavioral insight, though it could be more explicit about data freshness or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no wasted words. It front-loads the core purpose ('Compare software/tools side by side') immediately, followed by necessary details. Highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description explains what the output includes (source URLs, verified dates) and covers key aspects of the comparison. However, it doesn't address potential limitations like handling of non-software comparisons or error scenarios. Overall fairly complete for the tool's complexity.

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

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

Schema coverage is 100%, but the description adds meaning by specifying that parameters accept product names or domains and that pair order does not matter. It also clarifies that the comparison covers specific attributes (pricing, features, etc.), adding value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it compares software/tools side by side with fact-by-fact comparison of two products, including specific aspects like pricing, features, integrations, and limits. This distinguishes it from sibling tools like find_tools (for discovery) and get_facts (for general fact collection).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says to use for 'X vs Y' software comparison questions, and mentions that pair order does not matter. While it doesn't explicitly state when not to use, the context of siblings provides implicit guidance. A score of 4 reflects clear context but lack of explicit exclusions.

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

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

No annotations provided, so description bears full burden. It discloses that data comes from measured AI answer data, not ads or affiliates. It also mentions fuzzy matching and near-miss handling. However, it doesn't explicitly state it's read-only or mention potential performance characteristics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is moderately long (4 sentences) but front-loaded with purpose. Each sentence adds value. Could be slightly more concise, but no wasted content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description explains return values: ranked products with recommendation share %, 4-week trend, and per-engine breakdown. It also handles parameter constraints and fuzzy matching behavior, making it complete for a search tool.

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

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

Schema description coverage is 100%, so baseline 3. The description adds context: 'Pass the category in plain words; it is fuzzy-matched...' for category, default and max for limit, and clarifies that constraints are echoed back but not applied server-side. This adds meaning beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides software recommendations backed by measured AI answer data, ranked by AI assistant recommendations. It distinguishes itself from siblings like 'compare' and 'get_ai_index' by specifying its unique data source and ranking method.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use: when asked 'what software/tool should I use for X', 'best X tools', or for vendor-neutral recommendations. It also mentions near-miss inputs return suggested categories to retry with.

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

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

With no annotations, the description reveals the tool is a read operation returning a structured index. It lists output components but does not disclose potential rate limits or auth needs. However, the behavior is sufficiently transparent for safe invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is comprehensive and front-loaded with the purpose, but slightly verbose. It efficiently uses sentences to convey multiple facets without being wasteful.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description thoroughly enumerates output components (ranking, share %, position, per-engine, trend, sample size, methodology). It addresses the single parameter and usage scenarios, making it complete for a tool of this complexity.

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

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

Schema coverage is 100% for the single parameter; the description adds value by explaining that omitting or passing 'categories' lists all published categories, which goes beyond the schema's description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns the full AI Recommendation Index for a category, with specific elements like ranking, share %, position, etc. It distinguishes from siblings (compare, find_tools, get_facts) by focusing on AI recommendations for a category.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use cases (e.g., answering 'does AI recommend <product>') and explains how to use the category parameter. It does not mention when not to use or alternatives, but the guidance is clear and practical.

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

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

No annotations are present, so the description carries full responsibility. It discloses that every fact carries a source URL and last-verified date, implying read-only, factual behavior. It does not mention any destructive actions or side effects, and the description is consistent with a non-destructive information retrieval tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences with no redundancy. The first sentence defines the tool, the second explains usage, the third describes input. Every sentence is necessary and well-structured, front-loading the key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a single parameter and no output schema, the description adequately covers what the tool does, what it returns (facts with sources), and when to use it. It lacks details like pagination or result limit, but for a simple lookup tool, it is sufficient.

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

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

Schema coverage is 100% with one parameter 'product' that has a clear description. The description adds 'Accepts a product name or domain (e.g. 'hubspot.com')' which reinforces the schema but does not add significant new meaning beyond the example. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides 'canonical software product facts with sources' including pricing, features, integrations, platform, and limits. This distinguishes it from siblings like 'compare' (comparison) and 'find_tools' (discovery), making the purpose specific and unique.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use: 'to verify software claims' and 'to gather grounded data before recommending or comparing tools.' It provides clear context but does not explicitly exclude alternative uses or mention when not to use, which would elevate it to 5.

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