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

With no annotations provided, the description carries the full burden. It discloses that the tool accesses public information and lists the returned fields, but it does not disclose error handling, rate limits, or confirmation that it is read-only. This is adequate but leaves some gaps.

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 a single, focused sentence that front-loads the core purpose and lists the output fields efficiently. Every word contributes meaning, and it is appropriately sized for the tool's simplicity.

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?

For a simple profile lookup tool with one parameter and no output schema, the description is sufficiently complete by enumerating the returned data fields. It adds context that is not present in the schema, though it could mention behavior for nonexistent users or error conditions.

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?

The input schema already provides full documentation for the single 'username' parameter with a clear description and example. The tool description does not add additional parameter behavior or syntax beyond what the schema covers, so the baseline score of 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 the tool searches public GitHub user or organization information, and enumerates the specific fields returned (name, bio, company, location, followers, public repos). This distinguishes it from sibling tools like list_top_repos and get_language_stats, which focus on repositories and language statistics.

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 implies usage for fetching profile data, but it does not explicitly mention when to use this tool over alternatives. No exclusion criteria or alternatives are provided, though the scoped description makes the use case fairly evident.

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 discloses key behavior: it only includes public non-fork repositories and uses the main language of each repo. This goes beyond the schema, though it doesn't describe edge cases (e.g., repos without a language) or return format specifics.

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 one concise sentence, front-loaded with the main action and object. Every phrase earns its place—no redundant details or filler.

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?

For a simple 1-parameter tool with no output schema, the description provides adequate details on scope and method. It could mention whether the result is a list/map or whether it includes all languages, but the core purpose is clear.

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 'username' parameter. The description adds no new meaning beyond the schema, which already describes it as a GitHub username (login). Baseline of 3 applies.

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 states a specific verb ('Calcula' / calculates), a precise resource (percentage distribution of programming languages in public non-fork repositories of a GitHub user), and the methodology (based on each repo's primary language). This clearly differentiates it from siblings like get_github_profile and list_top_repos.

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 gives clear context: use when you need language distribution for a user's public, non-fork repos. It does not explicitly mention when not to use it or name alternatives, but the distinct purpose makes usage straightforward.

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 provided, the description carries the full burden for behavioral disclosure. It specifies that it returns public repositories with main language and star count, which is useful, but it does not mention pagination, rate limits, authentication, or error behavior. The description adds some context but is not rich.

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 a single, effective sentence that is front-loaded with the verb 'Lista' and clearly communicates the tool's purpose. It contains no unnecessary words or repetition, making it both concise and well-structured.

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?

For a simple read-only list tool with 2 parameters and full schema coverage, the description is adequate. It states the output fields (language and star count) and implies a list structure, though it lacks an explicit output schema. It does not mention potential edge cases like user not found, but overall it provides enough context for typical use.

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%, with both 'username' and 'limit' parameters having descriptive text. The tool description does not add additional parameter meaning beyond what the schema already provides, so a baseline score of 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 lists the most starred public repositories of a GitHub user with language and star count, which is a specific verb+resource+scope. It distinguishes itself from sibling tools like get_github_profile and get_language_stats by focusing on repositories rather than profile or language statistics.

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 implies its usage (for listing a user's top repos) but provides no explicit guidance on when to use this tool versus alternatives like get_github_profile or get_language_stats. It does not mention any exclusions or prerequisites, so it offers only implied usage.

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