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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about authentication requirements (environment variables or direct parameters), which is critical for a mutation tool. However, it lacks details on potential side effects (e.g., enrollment confirmation, payment requirements, or error handling), leaving behavioral traits partially covered.

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 with two sentences that efficiently convey key information: the core action and authentication details. It's front-loaded with the main purpose, and each sentence adds value without redundancy, though it could be slightly more structured (e.g., separating authentication notes).

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 complexity as a mutation tool with no annotations and no output schema, the description is moderately complete. It covers authentication needs but misses details like what happens on success/failure, whether enrollment is immediate or requires confirmation, and any rate limits. For a tool that modifies state, more behavioral context would improve 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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning authentication context, but it doesn't provide additional semantic details about parameters like format examples for 'course_url' or security considerations for 'password'. Baseline 3 is appropriate when the schema does the heavy lifting.

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 action ('Enroll in') and resource ('a Coursera course'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'get_my_courses' or 'search_courses', but the enrollment action is distinct enough from read-only operations.

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 some usage context by mentioning authentication requirements and environment variable alternatives, which helps understand when authentication is needed. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_course_details' for checking course information before enrolling, leaving usage guidance implied rather than explicit.

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 of behavioral disclosure. It effectively communicates that this is a read-only operation ('View'), requires authentication, and specifies the data returned. It doesn't mention potential rate limits, error conditions, or pagination, but covers the core behavior adequately for a read 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 extremely concise—two sentences with zero wasted words. The first sentence front-loads the purpose and output details, and the second adds critical authentication requirement. Every element earns its place.

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 (read-only, two optional parameters) and lack of annotations/output schema, the description is reasonably complete. It covers what the tool does, what it returns, and authentication needs. A minor gap is no mention of output format (e.g., JSON structure), but for a simple list tool, this is acceptable.

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 the schema already fully documents both parameters (email and password) with their optional nature and environment variable fallbacks. The description doesn't add any parameter-specific information beyond what's in the schema, meeting the baseline for high coverage.

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 specific action ('View all earned Coursera certificates') and lists the exact data returned ('titles, issuers, dates, and verification URLs'). It distinguishes itself from sibling tools like 'get_course_details' or 'get_my_courses' by focusing specifically on certificates rather than courses.

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 some usage context by stating 'Requires authentication,' which implies when authentication is needed. However, it doesn't explicitly guide when to use this tool versus alternatives like 'get_my_courses' (which might list enrolled courses but not certificates) or mention any prerequisites beyond authentication.

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 of behavioral disclosure. It states the tool retrieves information (implying read-only), but doesn't cover critical aspects like authentication requirements, rate limits, error handling, or data freshness. For a tool accessing external data with no annotation coverage, this leaves significant gaps in understanding its behavior.

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, efficient sentence that front-loads the core purpose. It lists relevant data fields without unnecessary elaboration. However, it could be slightly more structured by separating core functionality from the data list for better readability.

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 moderate complexity (external data fetch, no output schema, no annotations), the description is minimally adequate. It covers what data is returned but lacks context on permissions, reliability, or output structure. Without annotations or output schema, the agent must infer behavior from the description alone, which is incomplete for safe operation.

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 the schema fully documents the single parameter 'course_url' with examples. The description adds no parameter-specific information beyond what's in the schema, such as format constraints or validation rules. Baseline 3 is appropriate when the schema handles parameter documentation effectively.

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 a specific verb ('Get') and resource ('Coursera course'), listing key information types (syllabus, instructor, duration, skills, reviews). It distinguishes from siblings like 'search_courses' by focusing on a specific course rather than searching, but doesn't explicitly contrast with 'get_my_courses' or 'get_certificates'.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a course URL), when to choose this over 'search_courses' for finding course details, or how it differs from 'get_my_courses' for enrolled courses. Usage is implied but not explicitly stated.

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. It discloses the authentication requirement ('Requires authentication'), which is valuable behavioral context. However, it doesn't mention other important traits like whether this is a read-only operation, potential rate limits, error conditions, or what the output format looks like (though there's no output schema).

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 extremely concise (two short sentences) with zero wasted words. The first sentence states the purpose clearly, and the second sentence provides essential behavioral context. Every sentence earns its place.

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 and no output schema, the description provides basic purpose and authentication context but lacks information about return values, error handling, and other behavioral aspects. For a tool with 2 parameters and no structured metadata, the description is adequate but has clear gaps in 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?

Schema description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema. According to guidelines, when schema coverage is high (>80%), the baseline is 3 even with no param info in 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 specific action ('List all enrolled Coursera courses') and specifies what information is included ('with progress and status'). It distinguishes from siblings like 'search_courses' (which searches) and 'get_course_details' (which gets details for a specific course).

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 ('enrolled Coursera courses') and mentions authentication requirements, but does not explicitly state when to use alternatives like 'search_courses' for unenrolled courses or 'get_certificates' for certificate-specific data. It gives good usage context but lacks explicit sibling differentiation.

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 full burden. It mentions what the tool returns (titles, providers, ratings, difficulty, URLs) but doesn't disclose important behavioral traits like whether this is a read-only operation, potential rate limits, authentication requirements, or pagination behavior. The description adds some value about return format but misses key operational context.

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 perfectly concise - two sentences that efficiently convey purpose and return format with zero wasted words. It's front-loaded with the core functionality and follows with output details, making it easy for an agent to quickly understand the tool's value.

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 search tool with 4 parameters and no output schema, the description provides adequate but incomplete context. It covers the purpose and return format but lacks behavioral transparency (no annotations) and doesn't explain result ordering, error conditions, or how the search algorithm works. The 100% schema coverage helps, but the description should do more given the absence of annotations and output schema.

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 the schema already documents all parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it mentions search criteria but doesn't provide additional context about parameter usage, interactions, or examples not already covered in the 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 specific action ('Search Coursera for courses'), the resource ('courses'), and the search criteria ('by topic, skill, or keyword'). It distinguishes itself from siblings like 'get_course_details' (specific course info) and 'get_my_courses' (user's enrolled courses) by focusing on broad search functionality.

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 general course discovery but doesn't explicitly state when to use this tool versus alternatives. For example, it doesn't clarify whether to use 'search_courses' for initial discovery versus 'get_course_details' for detailed information on a specific course, or how it differs from 'get_my_courses' for enrolled courses.

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