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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool fetches results but doesn't describe important behavioral traits such as error handling (e.g., what happens for invalid dates), rate limits, authentication needs, or whether it's a read-only operation. The description is too sparse for a tool with no annotation coverage.

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 appropriately concise and well-structured. It starts with a clear purpose statement, followed by an 'Args' section explaining the parameter and a 'Returns' section describing the output. Each sentence earns its place, though the formatting could be slightly cleaner (e.g., bullet points instead of plain text sections).

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 the tool's low complexity (one optional parameter) and lack of annotations/output schema, the description is minimally complete. It covers the basic purpose, parameter, and return type, but misses behavioral details (e.g., error cases, date format) and doesn't leverage sibling context. It's adequate but leaves clear gaps for an agent to use it effectively.

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 description adds meaningful context for the single parameter 'date', explaining its purpose ('The date for fetching game results') and default behavior ('default is today'). Since schema description coverage is 0% and there's only one parameter, this adequately compensates, though it lacks format details (e.g., expected date string format like 'YYYY-MM-DD').

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's purpose: 'Fetch MLB game results for a given date'. It specifies the verb ('fetch'), resource ('MLB game results'), and scope ('for a given date'). However, it doesn't explicitly differentiate from sibling tools like 'get_mlb_schedule' or 'mlb_team_result', which likely serve related but distinct purposes.

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 minimal usage guidance. It mentions the default behavior ('default is today') but doesn't explain when to use this tool versus alternatives like 'get_mlb_schedule' (which might fetch future games) or 'mlb_team_result' (which could be team-specific). No explicit when-not-to-use or prerequisite information is included.

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 provided, so the description carries the full burden. It describes the tool as a retrieval operation, which implies read-only behavior, and specifies the return format. However, it lacks details on error handling, rate limits, or authentication needs, leaving behavioral gaps for a tool with no annotation coverage.

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 well-structured and front-loaded with the core purpose, followed by clear sections for arguments and returns. Each sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 annotations, 3 parameters with 0% schema coverage, and no output schema, the description is largely complete for a read-only tool. It covers purpose, parameters, and return format. However, it lacks some behavioral context like error cases or performance hints, which could enhance completeness.

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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains each parameter's purpose, optional nature, defaults, and formats (e.g., 'YYYY-MM-DD' for dates, team ID as integer), and clarifies that 'team_id' defaults to None for all teams, compensating fully for the schema's lack of documentation.

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's purpose with specific verbs ('retrieves') and resources ('MLB game schedule'), distinguishing it from sibling tools like 'get_daily_results' and 'mlb_team_result' which likely focus on results rather than schedules. It specifies the scope includes date ranges and optional team filtering.

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 by mentioning optional parameters for date ranges and team filtering, but does not explicitly state when to use this tool versus alternatives like the sibling tools. No exclusions or specific contexts are provided beyond the basic functionality.

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 provided, so the description carries the full burden. It discloses key behavioral traits: it retrieves data (read-only implied), returns a dictionary with specific structure or None if no game found, and handles an optional date parameter with a default. It doesn't mention rate limits, authentication needs, or data freshness, but covers core behavior adequately for a read operation.

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 appropriately sized and front-loaded: the first sentence states the purpose clearly. The Args and Returns sections are structured efficiently, with each sentence adding value (e.g., examples, defaults, structure details). No wasted words.

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 the tool's low complexity (2 parameters, no annotations, no output schema), the description is complete. It explains purpose, parameters, return values (including structure and None case), and usage context. No output schema exists, so describing the return structure is necessary and well-done.

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 0%, so the description must compensate. It fully documents both parameters: team_name (name, example) and date (optional, format, default). It adds meaning beyond the bare schema by explaining usage and format, which is essential given the lack of schema descriptions.

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 verb 'retrieves' and the resource 'results (scoring plays and highlights) for a specific MLB team's most recent game.' It distinguishes from sibling tools by specifying it's for a specific team's most recent game, unlike get_daily_results (likely broader) and get_mlb_schedule (likely schedule-focused).

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 clear context: use this to get results for a specific team's most recent game. It doesn't explicitly state when not to use it or name alternatives, but the specificity implies it's not for general results or schedules, which aligns with sibling tool names. No misleading guidance is present.

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