HiveLearn

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 mentions the tool's purpose and usage context but lacks details on permissions needed, whether the operation is idempotent, error handling, or what the response looks like. It adds some behavioral context (manual vs. auto-issuance) but is incomplete for a mutation 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 two sentences, front-loaded with the core purpose and followed by usage guidelines. Every sentence adds value without redundancy, making it efficient 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?

Given the tool's complexity (mutation with 6 parameters, no annotations, no output schema), the description is adequate but has gaps. It covers purpose and usage well but lacks details on behavioral traits, parameter meanings, and expected outcomes, leaving the agent with incomplete guidance for correct invocation.

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 low (33%), but the description doesn't directly address parameters. However, it implies that 'user_id' and 'course_id' are required for issuing certificates, and the context of 'bulk backfill or manual awards' hints at optional fields like 'completion_date'. Since no parameters are explicitly explained, baseline is 4 as it compensates somewhat through implied usage.

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 verb ('Issue') and resource ('certificate of completion to a user for a course'), and distinguishes it from siblings by mentioning its specialized use cases (bulk backfill or manual awards) versus the platform's auto-issuance on completion.

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 states when to use this tool ('for bulk backfill or manual awards') and when not to use it ('Normally the platform auto-issues on course completion'), providing clear context and alternatives without naming specific sibling tools.

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. While it mentions the tool creates an 'empty course', it doesn't disclose behavioral traits like required permissions, whether this is a write operation, what happens on success/failure, or any rate limits. For a creation tool with zero annotation coverage, this leaves significant 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 perfectly concise with two sentences that each earn their place. The first sentence states the core purpose, and the second provides crucial sibling differentiation. No wasted words or redundant 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?

For a creation tool with 10 parameters (only 1 required), no annotations, no output schema, and minimal schema descriptions, the description is incomplete. It doesn't address what the tool returns, error conditions, authentication needs, or parameter semantics. The excellent sibling differentiation doesn't compensate for these fundamental gaps.

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 only 10% (only 'visibility' parameter has a description), leaving 9 parameters undocumented in the schema. The description provides no information about any parameters beyond the implied 'title' requirement. It doesn't explain what 'tags', 'difficulty', 'description_json', or other parameters mean or how they affect course creation.

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 ('Create an empty course') and resource ('course'), distinguishing it from the sibling tool hivelearn_create_course_outline by specifying it creates a course with 'no modules/lessons'. This provides explicit differentiation from the alternative.

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 provides when to use this tool ('Create an empty course') versus when to use the alternative ('Prefer hivelearn_create_course_outline when you already know the structure'). It names the specific sibling tool and gives clear context for choosing between them.

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 describes the creation behavior ('creates course, modules, lesson placeholders') and the placeholder nature of lessons ('title/description only, no content yet'). However, it lacks details on permissions needed, whether the course is automatically published, error handling, or response format, which are important for a complex creation 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 highly efficient with three sentences that each serve a distinct purpose: stating the tool's function, explaining the follow-up workflow, and providing usage context. It's front-loaded with the core action and avoids any redundant information, making it easy to parse quickly.

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 (11 parameters, nested objects) and lack of annotations or output schema, the description does a good job of covering the high-level workflow and purpose. It explains the creation of multiple resources in one call and the placeholder nature of lessons. However, it could be more complete by mentioning the return value (e.g., lesson IDs for the follow-up loop) or addressing potential constraints like rate limits or authentication needs.

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 very low at 9%, with only the 'modules' parameter having a description. The description compensates by explaining the overall purpose ('scaffold an entire course') and the structure ('course + modules + lesson placeholders'), which helps interpret the 11 parameters. However, it doesn't detail individual parameters like 'difficulty', 'visibility', or 'description_format', leaving some semantics unclear.

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 ('Scaffold an entire course in one call') and specifies the resources created ('course + modules + lesson placeholders'). It explicitly distinguishes this from sibling tools by mentioning that lesson content must be filled separately with 'hivelearn_update_lesson_content', making it distinct from simpler creation tools like 'hivelearn_create_course' or 'hivelearn_create_lesson'.

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 guidance on when to use this tool ('preferred first step when authoring a new course from a plan') and when not to use it (for filling content, use 'hivelearn_update_lesson_content'). It also clarifies the workflow by stating that this creates placeholders only, with content to be added later in a loop, offering clear alternatives and context.

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 of behavioral disclosure. It effectively describes the mutation action ('Enroll'), the authentication requirement ('authenticated community'), and the rejection behavior for duplicates. However, it lacks details on permissions needed, error handling beyond duplicates, rate limits, or what the response looks like (e.g., success confirmation or enrollment details). For a mutation tool with zero annotation coverage, this is adequate but leaves gaps in behavioral 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 front-loaded with the core action in the first sentence, followed by two critical constraints in the second. Every sentence earns its place by providing essential information without redundancy, making it highly efficient and well-structured for quick understanding.

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 complexity of a mutation tool with 2 required parameters, no annotations, and no output schema, the description is moderately complete. It covers the purpose, key constraints (community membership and duplicate rejection), and parameter context, but lacks details on permissions, error scenarios, and return values. This is sufficient for basic use but could be more robust for an agent to handle edge cases 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 input schema has 0% description coverage, so the description must compensate. It adds meaningful semantics by explaining that 'user_id' and 'course_id' must belong to the authenticated community, which clarifies their purpose beyond just being UUIDs. However, it doesn't specify the format or source of these IDs (e.g., from other tools like 'hivelearn_get_member'), leaving some ambiguity. Given the low schema coverage, the description does a good job but not fully comprehensive.

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 ('Enroll a user in a course') with the target resources (user and course). It distinguishes itself from sibling tools like 'hivelearn_get_enrollment' (which retrieves) and 'hivelearn_list_enrollments' (which lists), making the purpose unambiguous and well-differentiated.

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 for when to use this tool: when enrolling a user in a course, with the constraint that both IDs must belong to the authenticated community. It also mentions that duplicate enrollments are rejected, which helps avoid misuse. However, it doesn't explicitly state when NOT to use it or name specific alternatives among siblings, such as 'hivelearn_update_course' for modifying course details instead of enrollments.

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 mentions that 'event_type controls which field the UI shows', which adds some behavioral context about how the system responds to different event types. However, it doesn't disclose critical behavioral traits like whether this is a mutating operation (implied by 'create' but not explicit), what permissions are required, what happens on success/failure, or any rate limits. The description is insufficient for a mutation tool with zero 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 extremely concise and well-structured in just three sentences. The first sentence states the core purpose, the second provides critical parameter format guidance, and the third explains the relationship between event_type and UI behavior. Every sentence earns its place with 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 complexity (13 parameters, mutation operation, no annotations, no output schema), the description is incomplete. It provides good guidance on date formats and event_type behavior but leaves many parameters unexplained and doesn't describe the return value or error conditions. For a creation tool with this many parameters and no structured metadata, the description should do more to help the agent understand what happens after invocation.

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 value beyond the input schema, which has only 23% description coverage. It clarifies that 'Dates are ISO 8601 strings in UTC', provides guidance for 'virtual events set meeting_url; for in-person set location', and explains that 'event_type controls which field the UI shows'. This compensates well for the low schema coverage, though it doesn't cover all 13 parameters (e.g., tags, max_attendees, cover_image_url, description_json, description_format remain unexplained).

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 'Create' and resource 'calendar event', making the purpose immediately understandable. It distinguishes this tool from sibling 'get' and 'list' tools by specifying creation rather than retrieval. However, it doesn't explicitly differentiate from other 'create' tools like 'create_course' or 'create_lesson' beyond the event focus.

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 implied usage guidance by explaining that 'event_type controls which field the UI shows' and giving examples for virtual vs. in-person events. However, it doesn't explicitly state when to use this tool versus alternatives like 'update_event' or 'list_events', nor does it mention prerequisites or error conditions.

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 implies a write operation ('Add a lesson') but does not detail permissions, side effects, error handling, or response format. Some context is given (e.g., content_type matching), but critical behavioral traits like mutation impact or auth requirements are missing.

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 front-loaded with the core purpose and efficiently provides key usage notes in two sentences. Every sentence adds value without redundancy, making it appropriately 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?

Given the complexity (13 parameters, no annotations, no output schema), the description is incomplete. It covers some critical parameters and usage but lacks details on behavioral traits, many parameter meanings, and return values. This is adequate as a minimum viable description but has clear gaps for a mutation 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 very low (8%), so the description must compensate. It adds meaningful semantics for 'content_type' (matching with 'content_url') and 'module_id' (null handling and source), which are not covered in the schema. However, it does not address many other parameters (e.g., 'is_preview', 'sort_order'), leaving gaps that prevent a score of 5.

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 ('Add a lesson to a course') and specifies the resource ('lesson'), making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like 'hivelearn_create_module' or 'hivelearn_update_lesson', which would be needed for a score of 5.

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 on when to use certain parameters (e.g., 'Set module_id=null to leave the lesson unassigned; otherwise pass a module uuid from list_course_modules'), offering practical guidance. It does not explicitly mention when not to use this tool or name alternatives, preventing a score of 5.

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 states 'server auto-appends if omitted' for sort_order, which adds some behavioral context, but lacks critical details: it doesn't disclose that this is a mutation (create operation), potential permissions needed, error conditions, or what the response contains. For a creation tool with zero annotation coverage, this is insufficient.

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, front-loaded with the core purpose and followed by a key parameter detail. Every sentence earns its place with zero waste, making it appropriately sized and 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 the tool's complexity (creation operation with 3 parameters), no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It lacks details on behavioral traits, full parameter semantics, error handling, and return values, making it inadequate for safe and effective use by an AI agent.

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 adds meaning for 'sort_order' by explaining it's optional and server-handled, but doesn't clarify 'course_id' (e.g., must be an existing course UUID) or 'title' (e.g., module name). With 3 parameters and low schema coverage, the description provides partial but incomplete semantic context.

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 'Add' and the resource 'module to a course', making the purpose specific and understandable. It distinguishes from siblings like 'hivelearn_create_course' or 'hivelearn_create_lesson' by specifying the target resource as a module, though it doesn't explicitly contrast with all sibling tools.

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., course must exist), exclusions, or compare to siblings like 'hivelearn_update_module' or 'hivelearn_list_course_modules', leaving usage context implied at best.

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 correctly identifies this as a write operation ('Publish'), mentions the optional category with default behavior, and specifies the content format requirement. However, it doesn't address important behavioral aspects like authentication requirements, rate limits, error conditions, or what happens when publishing fails.

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 with three sentences that each serve a distinct purpose: stating the core function, explaining parameter requirements, and clarifying optional behavior. There's zero wasted language, and the information is front-loaded with the most critical requirement appearing immediately.

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 write operation with 4 parameters, no annotations, and no output schema, the description provides adequate but incomplete context. It covers the basic functionality and parameter relationships well, but lacks information about the response format, error handling, authentication requirements, and side effects that would be important for an agent to use this tool 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 schema description coverage is 75% (3 out of 4 parameters have descriptions), but the description adds significant value beyond the schema. It clarifies the mutual exclusivity requirement ('Either content or content_json is required'), explains the relationship between content_format and the content fields, and provides the default value for category that isn't in the schema. This compensates well for the schema's limitations.

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 ('Publish a new post') and target resource ('to the community feed'), which distinguishes it from sibling tools like hivelearn_update_post or hivelearn_get_post. It provides a precise verb+resource combination that leaves no ambiguity about the tool's function.

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 about when to use this tool (to publish new posts) and includes important prerequisites (either content or content_json is required). However, it doesn't explicitly mention when NOT to use it or name specific alternatives like hivelearn_update_post for editing existing posts, which prevents a perfect score.

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 full burden for behavioral disclosure. It mentions that 'passing_score is 0-100 (percent)' and default behaviors for time_limit_seconds and max_attempts, but doesn't cover critical aspects like whether this is a write operation (implied by 'Create'), authentication requirements, error conditions, or what happens when a quiz is created (e.g., does it become immediately active?).

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 with just two sentences that each provide essential information. The first sentence states the core purpose, and the second adds crucial parameter guidance. There's zero wasted language or redundancy.

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 creation tool with 7 parameters, 0% schema description coverage, no annotations, and no output schema, the description is insufficient. It covers only 3 parameters partially and lacks behavioral context about the write operation, response format, or error handling. The agent would struggle to use this tool correctly without additional information.

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?

With 0% schema description coverage, the description must compensate for all 7 parameters. It explains 'passing_score' range (0-100 percent) and clarifies that omitting 'time_limit_seconds' or 'max_attempts' means unlimited, which adds meaningful context beyond the schema's type constraints. However, it doesn't address other parameters like 'lesson_id', 'title', 'description', or 'shuffle_questions', leaving some semantics undocumented.

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 ('Create a quiz') and the target resource ('attached to a lesson'), which provides a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'hivelearn_create_quiz_question' or 'hivelearn_update_quiz', which would be needed for a perfect score.

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 like 'hivelearn_update_quiz' or 'hivelearn_create_quiz_question'. It mentions parameter defaults ('omit... for unlimited') but doesn't address broader usage context, prerequisites, or 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It helpfully describes validation rules (e.g., 'at least one must be is_correct:true' for multiple_choice) and conditional parameter usage, which goes beyond basic functionality. However, it doesn't mention mutation effects, permissions, error conditions, or response format, leaving gaps for a creation 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 efficiently structured in two sentences: the first states the core purpose, and the second provides essential parameter guidance. Every word earns its place with zero redundancy, making it easy to parse and apply.

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 creation tool with 8 parameters, no annotations, and no output schema, the description does well on parameter semantics but lacks behavioral context like mutation effects, error handling, or response details. It's adequate for basic use but incomplete for robust agent 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?

With only 38% schema description coverage, the description compensates significantly by explaining the semantics of key parameters: it clarifies that 'options' is for 'multiple_choice' with specific object structure, and 'correct_answer' is for 'true_false' with boolean type. This adds crucial meaning beyond the sparse 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 ('Add a question to a quiz') and distinguishes this tool from siblings like 'hivelearn_create_quiz' (which creates the quiz itself) and 'hivelearn_update_quiz_question' (which modifies existing questions). It specifies the resource (quiz) and operation (adding questions) with precision.

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 for when to use this tool (adding questions to quizzes) and implicitly distinguishes it from sibling tools like 'hivelearn_list_quiz_questions' (for listing) and 'hivelearn_update_quiz_question' (for modifying). However, it doesn't explicitly state when NOT to use it or mention prerequisites like quiz existence.

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 full burden for behavioral disclosure. It mentions the response includes 'verification_code and download URL', which adds useful context about return data. However, it doesn't address critical behavioral aspects: whether this is a read-only operation, authentication requirements, error handling for invalid IDs, rate limits, or data freshness.

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?

Two concise sentences with zero waste: the first states the core functionality, the second adds valuable information about response content. Every word earns its place, and the most important information (fetching by ID) comes first.

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 operation with one parameter and no output schema, the description provides adequate basics but lacks completeness. It doesn't cover authentication needs, error scenarios, or relationship to sibling tools. The mention of response fields partially compensates for missing output schema, but behavioral context remains insufficient.

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 specifies 'by id' which clarifies the single parameter's purpose, adding meaningful context beyond the schema's technical UUID format. With 0% schema description coverage and only one parameter, this minimal semantic addition is sufficient. However, it doesn't explain what constitutes a valid certificate ID beyond the format.

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 ('Fetch') and resource ('one certificate by id'), making the purpose immediately understandable. It distinguishes from sibling 'list_certificates' by specifying retrieval of a single item rather than listing multiple. However, it doesn't explicitly contrast with 'verify_certificate' which might also involve certificate IDs.

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 when needing a specific certificate by its ID, but provides no explicit guidance on when to use this versus alternatives like 'list_certificates' for browsing or 'verify_certificate' for validation. No prerequisites, error conditions, or performance considerations are mentioned.

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 describes the tool's behavior: it returns community information based on API key scoping, implies it's a read-only operation (though not explicitly stated), and clarifies that community switching is not possible. However, it doesn't mention potential errors, rate limits, or authentication requirements.

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 front-loaded with the core purpose in the first sentence, followed by usage guidance in the second. Every sentence earns its place by providing essential information without redundancy, making it highly efficient 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?

Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is nearly complete. It explains what the tool returns and when to use it, but lacks details on output format or error handling. For a straightforward tool, this is sufficient but could be slightly enhanced.

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 has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds context about the API key determining the output, which is valuable semantic information beyond the empty schema. A baseline of 4 is justified since no parameters exist.

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 ('Returns') and resource ('the community the current API key is scoped to'), specifying exactly what the tool does. It distinguishes itself from sibling tools by focusing on API key scoping rather than creating or retrieving specific resources like courses or 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 explicitly states when to use this tool ('Use this first whenever you need the community_id, owner, slug, or tier') and provides a clear exclusion ('you cannot switch community'), guiding the agent on its primary use case and limitations compared to alternatives.

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 full burden. It indicates this is a read operation ('Fetch') and specifies what metadata is returned, but doesn't disclose behavioral traits like authentication requirements, rate limits, error conditions, or whether it's idempotent. It adds some value but leaves important operational context unspecified.

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?

Two sentences with zero waste. First sentence states purpose and scope, second provides crucial sibling differentiation. Every word earns its place, and the most important information (what it does) comes first.

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 operation with 1 parameter and no output schema, the description is reasonably complete. It specifies what metadata is returned and distinguishes from the main alternative tool. However, without annotations or output schema, it could benefit from mentioning return format or error handling for full 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?

With 0% schema description coverage and only 1 parameter, the description doesn't add specific parameter semantics beyond what the schema provides (UUID format). However, the baseline for 0 parameters is 4, and the description implies the 'id' parameter is needed to fetch a specific course, which aligns with the schema's required UUID parameter.

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 ('Fetch one course') and resource ('course'), with explicit scope ('full metadata including description, instructor, tags, flags'). It distinguishes from sibling 'hivelearn_get_course_structure' by specifying what it doesn't include ('lesson tree').

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 provides when-not-to-use guidance: 'For the lesson tree use hivelearn_get_course_structure instead.' This directly addresses the main alternative among sibling tools, giving clear context for tool selection.

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 of behavioral disclosure. The description mentions it's for 'analytics or grading dashboards,' implying read-only data retrieval, but doesn't explicitly state whether it's safe, requires authentication, has rate limits, or what the return format looks like. For a tool with 4 parameters and no annotations, 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 appropriately sized and front-loaded: two concise sentences that directly state the tool's purpose and usage context. Every sentence earns its place with no wasted words, making it easy to parse quickly.

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 complexity (4 parameters, 1 required), no annotations, no output schema, and 50% schema description coverage, the description is incomplete. It doesn't explain return values, error conditions, authentication needs, or how parameters interact (e.g., combining 'user_id' with pagination). For a data retrieval tool with multiple parameters, more context is needed.

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 50% (2 out of 4 parameters have descriptions: 'limit' and 'offset'). The description doesn't add any parameter-specific information beyond what the schema provides. It mentions 'per-user quiz attempts, scores, pass/fail,' which hints at the 'user_id' parameter but doesn't explain its semantics. With partial schema coverage, the description doesn't fully compensate for the gaps.

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: 'Quiz-score gradebook for a course: per-user quiz attempts, scores, pass/fail.' It specifies the resource (course gradebook) and what data it provides (quiz attempts, scores, pass/fail status). However, it doesn't explicitly differentiate from sibling tools like 'hivelearn_get_course_progress' or 'hivelearn_get_quiz', which might also provide related data.

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 implied usage guidance with 'Use for analytics or grading dashboards,' suggesting contexts where this tool is appropriate. However, it doesn't explicitly state when to use this tool versus alternatives like 'hivelearn_get_course_progress' or 'hivelearn_get_quiz', nor does it mention prerequisites or 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 are provided, so the description carries full burden. It discloses the return format ('per-user percent_complete, last_accessed_at, completed_lesson_count') but lacks critical behavioral details: whether this is a read-only operation, if it requires specific permissions, pagination behavior (implied by limit/offset but not explained), or error conditions. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.

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, well-structured sentence that front-loads the core purpose and efficiently lists return values. Every word earns its place—no redundancy or fluff. It's appropriately sized for a tool with moderate complexity, making it easy to parse quickly.

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, 4 parameters with 75% schema coverage, and no output schema, the description is partially complete. It covers the purpose and return format but misses behavioral context (e.g., safety, pagination) and usage guidelines. For a read-like aggregation tool, this is minimally adequate but leaves gaps that could hinder correct invocation without trial-and-error.

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 75% (3 of 4 parameters have descriptions), so the baseline is 3. The description adds no parameter-specific semantics beyond what's in the schema—it doesn't explain how 'course_id' relates to 'enrolled users' or clarify that 'user_id' filters results. It compensates slightly by implying aggregation scope but doesn't detail parameter interactions or defaults beyond schema hints.

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 ('Aggregate lesson-completion progress') on a specific resource ('for a course across enrolled users') and distinguishes it from siblings like 'hivelearn_get_course_gradebook' or 'hivelearn_get_enrollment' by focusing on progress aggregation rather than grades or enrollment details. It uses precise terminology like 'percent_complete' and 'completed_lesson_count' to define scope.

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 like 'hivelearn_get_course_gradebook' (for grades) or 'hivelearn_get_enrollment' (for enrollment status). It mentions 'across enrolled users' but doesn't specify prerequisites (e.g., requires course_id) or exclusions (e.g., not for single-user progress). Usage is implied through parameter descriptions 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 full burden and does well by disclosing key behavioral traits: it's a read operation (implied by 'Return'), returns a hierarchical structure ('tree'), is compact/titles-only, and excludes lesson content. It doesn't mention pagination, rate limits, or authentication needs, but provides sufficient context for basic usage.

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?

Perfectly front-loaded with the core functionality in the first sentence, followed by usage context and exclusions. Every sentence earns its place: first defines what it returns, second states ideal use cases, third clarifies limitations. Zero wasted words while being comprehensive.

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 single-parameter read tool with no output schema, the description provides excellent context about the return format (hierarchical tree), content limitations, and use cases. It doesn't specify the exact tree structure format or error conditions, but given the tool's relative simplicity, it's nearly complete.

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?

With 0% schema description coverage and only 1 parameter, the description doesn't explicitly mention the 'id' parameter. However, the context ('course') strongly implies what the parameter represents. The description compensates well for the schema gap by clearly defining what the tool returns, making parameter semantics reasonably inferable.

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 ('Return a compact titles-only tree'), resource ('course → modules → lessons'), and scope ('Does NOT include lesson body content'). It distinguishes from sibling tools like hivelearn_get_course (which likely returns full course details) and hivelearn_list_course_modules/lessons (which likely return flat lists rather than hierarchical structure).

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 states when to use this tool ('Ideal for agents to plan reorders, spot empty lessons, or summarize a course') and when not to use it ('Does NOT include lesson body content'), providing clear alternatives for different use cases. The description helps agents choose between this structural overview tool and content-focused tools.

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 'Fetch one enrollment with status and progress,' which implies a read-only operation, but doesn't confirm safety (e.g., no destructive effects), rate limits, authentication needs, or error handling. For a tool with zero annotation coverage, this is insufficient to guide the agent's expectations beyond basic functionality.

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 directly states the tool's function without any fluff. It's front-loaded with the core action ('Fetch one enrollment') and includes key details ('with status and progress'). Every word earns its place, making it highly 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?

Given the tool's complexity (a read operation with 1 parameter), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'status and progress' entail, how to interpret the output, or any behavioral constraints. For a tool in this context, more detail is needed to fully guide the agent.

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 has 1 parameter ('id') with 0% description coverage, meaning the schema provides no semantic context. The description adds no information about the 'id' parameter, such as what it represents (e.g., enrollment ID) or where to obtain it. This leaves the parameter's meaning and usage unclear, failing to compensate for the low schema 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 verb ('Fetch') and resource ('one enrollment') with specific attributes ('status and progress'), making the purpose unambiguous. It distinguishes from sibling tools like 'hivelearn_list_enrollments' by specifying 'one enrollment' rather than multiple. However, it doesn't explicitly contrast with other 'get' tools (e.g., 'hivelearn_get_course'), so it's not a perfect 5.

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 when to choose this over 'hivelearn_list_enrollments' for multiple enrollments or other 'get' tools for different resources. There's also no information about prerequisites, such as needing an enrollment ID. This leaves the agent without contextual usage instructions.

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 full burden but only states what data is fetched. It doesn't disclose behavioral traits like whether this is a read-only operation (implied by 'Fetch' but not explicit), authentication requirements, rate limits, error handling, or what happens if the ID doesn't exist.

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 ('Fetch one event by id') followed by additional detail ('with full description and RSVP metadata'). Every word serves a purpose with zero waste.

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 operation with 1 parameter and no output schema, the description covers the basic purpose and parameter semantics adequately. However, without annotations or output schema, it lacks details on return format, error cases, and behavioral context that would make it fully complete.

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 crucial meaning beyond the schema: it explains that 'id' refers to an event identifier and that fetching by this ID returns full description and RSVP metadata. With 0% schema description coverage and only 1 parameter, this adequately compensates, though it doesn't specify the UUID format mentioned in 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 the action ('Fetch'), the resource ('one event'), and the key identifier ('by id'), along with what information is retrieved ('full description and RSVP metadata'). It distinguishes from list operations but doesn't explicitly differentiate from other get_* tools like get_course or get_quiz.

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 when you need detailed event data for a specific ID, but provides no explicit guidance on when to use this versus alternatives like list_events or update_event. It doesn't mention prerequisites, error conditions, or sibling tool comparisons.

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 the return format ('same fields as list_members for one row'), which adds some context, but lacks critical behavioral details like authentication requirements, error handling, or rate limits for this 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 extremely concise with two sentences that are front-loaded and waste-free. Every word serves a purpose: the first sentence defines the action, and the second clarifies the return format, making it efficiently 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?

Given the tool's low complexity (single parameter, read-only operation) and lack of output schema, the description is adequate but incomplete. It covers the basic purpose and return format but misses behavioral context like error cases or permissions, which is a gap for a tool with no annotations.

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 'id' as a UUID. The description adds minimal value by noting it 'matches the id from list_members', but doesn't provide additional semantics 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 ('Fetch a single member'), resource ('member'), and unique identifier ('by profile uuid'), distinguishing it from sibling tools like list_members which returns multiple members. It provides precise verb+resource+scope differentiation.

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 implicitly suggests using this tool when you need a single member's details by UUID, contrasting with list_members for multiple members. However, it doesn't explicitly state when NOT to use it or mention alternatives beyond the implied sibling, missing 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 are provided, so the description carries the full burden. It mentions the tool fetches 'full content' and excludes 'reply thread,' which adds some behavioral context. However, it lacks details on permissions, rate limits, error handling, or response format, which are important for a read operation with 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 front-loaded and efficient with two sentences: the first states the purpose, and the second clarifies exclusions. Every word earns its place, with no redundancy or wasted text.

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, no output schema, and low schema coverage, the description is incomplete. It covers the basic purpose and scope but misses critical details like parameter explanation, return values, error cases, or behavioral traits needed for effective tool 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?

The input schema has 1 parameter with 0% description coverage, and the tool description doesn't mention the 'id' parameter at all. Since schema coverage is low, the description should compensate but doesn't, resulting in a baseline score. It adds no meaning beyond the schema, which only defines 'id' as a UUID.

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 one post with full content.' It specifies the verb ('fetch'), resource ('post'), and scope ('one' with 'full content'). However, it doesn't explicitly differentiate from sibling tools like 'hivelearn_list_posts' or 'hivelearn_update_post', which would require a 5.

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 'Reply thread is NOT included — this is the post itself only,' which implies when to use this tool (for post content only) versus alternatives (for threads). However, it doesn't explicitly name alternatives or provide clear when-not-to-use guidance, keeping it at an implied level.

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 clearly indicates this is a read operation ('Fetch') and specifies what data is returned (metadata including passing_score, time_limit, max_attempts) and what is excluded (questions). However, it doesn't address potential behavioral aspects like authentication requirements, error conditions, or rate limits. For a read tool with no annotations, this provides basic but incomplete behavioral 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 and front-loaded: the first clause establishes the core purpose, the parenthetical adds essential detail about what metadata is included, and the second sentence provides critical exclusion guidance. Every sentence earns its place with zero 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?

For a simple read operation with one parameter and no output schema, the description provides good contextual completeness: it clearly states what the tool returns (metadata fields) and what it doesn't (questions), with explicit alternative guidance. The main gap is the lack of parameter semantics, but given the tool's simplicity and clear behavioral description, this is reasonably complete.

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 has 0% description coverage, providing only technical validation (UUID format) without semantic meaning. The description doesn't mention the 'id' parameter at all, offering no compensation for the schema's lack of semantic documentation. With one required parameter that remains semantically undocumented, this meets the baseline for minimal viability.

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 ('Fetch one quiz') and resource ('quiz with metadata'), explicitly distinguishing it from sibling tools by noting what is NOT included ('Questions are NOT included') and providing an alternative ('use hivelearn_list_quiz_questions'). This precise verb+resource combination with sibling differentiation earns the highest score.

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 guidance on when to use this tool versus alternatives: it specifies that this tool fetches metadata only, and directs users to 'hivelearn_list_quiz_questions' for questions. This clear exclusion and named alternative represent optimal usage guidance.

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 full burden for behavioral disclosure. It mentions the return fields (id, user, course, verification_code, status, issued_at) which is helpful, but doesn't describe pagination behavior (implied by limit/offset parameters), authentication requirements, rate limits, error conditions, or whether this is a read-only operation. The description adds some value but leaves significant behavioral aspects undocumented.

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 functionality ('List issued certificates'), then adds optional scoping, and finally specifies return fields. Every element earns its place with zero wasted words or redundancy.

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 list tool with 4 parameters, 50% schema coverage, no annotations, and no output schema, the description provides adequate basics (purpose, optional filters, return fields) but lacks important context. It doesn't explain pagination strategy, default behavior when no filters are applied, error handling, or relationship to sibling tools. The return fields are listed but without data types or example values.

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 50% (limit and offset have descriptions, user_id and course_id don't). The description adds semantic context for user_id and course_id by stating they're for optional scoping to 'one user or one course', which compensates partially for the schema gap. However, it doesn't provide format details (UUID) or explain the relationship between these parameters (e.g., can both be used together?).

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 ('List') and resource ('issued certificates') with optional scoping to user or course. It distinguishes from sibling 'hivelearn_get_certificate' (singular retrieval) by indicating this returns multiple certificates, but doesn't explicitly differentiate from other list tools like 'hivelearn_list_courses' or 'hivelearn_list_enrollments' beyond the resource type.

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 context by mentioning optional scoping to user or course, suggesting when to use those parameters. However, it doesn't provide explicit guidance on when to choose this tool over alternatives like 'hivelearn_get_certificate' (for single certificate) or other list tools, nor does it mention prerequisites or 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions optional filtering by module, which is useful context. However, it doesn't describe important behavioral aspects: whether this is a read-only operation, what format the lessons are returned in, whether there's pagination, or any rate limits. For a list operation with zero annotation coverage, this leaves significant 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 perfectly concise with two sentences that each earn their place. The first sentence states the core purpose and optional filtering. The second sentence provides the key alternative tool. No wasted words, front-loaded with essential 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 2 parameters with 0% schema coverage, no annotations, and no output schema, the description provides basic completeness for a list operation. It covers the purpose and main alternative, but leaves significant gaps in parameter documentation and behavioral context. For a relatively simple list tool, this is minimally adequate but with clear documentation deficiencies.

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?

With 0% schema description coverage for both parameters, the description must compensate but provides minimal parameter information. It mentions 'optionally filtered to one module' which hints at module_id usage, but doesn't explain course_id at all or provide format guidance for either UUID parameter. The description adds some value but doesn't adequately compensate for the complete 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 tool's purpose: 'List lessons of a course' specifies the verb (list) and resource (lessons of a course). It distinguishes from sibling 'hivelearn_get_course_structure' by mentioning it provides a nested tree, but doesn't explicitly differentiate from other list tools like 'hivelearn_list_course_modules'.

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 for when to use this tool vs. an alternative: 'Use hivelearn_get_course_structure for a nested tree.' This explicitly names the alternative tool and its purpose. However, it doesn't specify when NOT to use this tool or address other potential alternatives among the many sibling tools.

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 of behavioral disclosure. It mentions ordering ('ordered by sort_order') and the relationship between modules and lessons, but it doesn't cover critical aspects like pagination, error handling, authentication requirements, rate limits, or what happens if the course_id is invalid. For a read operation tool with zero 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 highly concise and front-loaded, consisting of two sentences that directly state the tool's purpose and provide additional context. Every sentence earns its place: the first defines the action and ordering, and the second explains the module-lesson relationship and cardinality. There is no wasted verbiage.

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 (a read operation with one parameter) and the lack of annotations and output schema, the description is incomplete. It doesn't explain the return format (e.g., what fields are included in the module list), error conditions, or any side effects. While it covers basic purpose, it misses key contextual details needed for effective tool invocation.

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 doesn't explicitly mention the 'course_id' parameter or add any semantic details beyond what the input schema provides. However, with only one parameter and 0% schema description coverage, the schema itself defines the parameter as a UUID with a specific pattern. The description implies the parameter's purpose by stating 'List modules (sections) of a course,' but it doesn't elaborate on format or constraints. This meets the baseline for minimal parameter information.

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: 'List modules (sections) of a course, ordered by sort_order.' It specifies the verb ('list'), resource ('modules of a course'), and ordering behavior. However, it doesn't explicitly differentiate from sibling tools like 'hivelearn_get_course_structure' or 'hivelearn_list_course_lessons', which might have overlapping 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 provides minimal usage guidance. It mentions that 'Modules group lessons; a course has 1..N modules,' which gives some context about when modules exist, but it doesn't specify when to use this tool versus alternatives like 'hivelearn_get_course_structure' (which might include modules) or 'hivelearn_list_course_lessons' (which lists lessons, not modules). No explicit when/when-not or alternative tool references are provided.

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 that it lists courses in the authenticated community and describes the effect of the 'status' parameter, but lacks details on authentication needs, rate limits, pagination behavior (beyond schema hints), or what happens if no courses exist. It adds some context but is incomplete 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 front-loaded with the main purpose and includes key details in two sentences. It's efficient with no wasted words, though it could be slightly more structured by separating parameter effects from return values.

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 partially compensates by listing return fields (id, title, etc.) and parameter effects. However, for a list tool with pagination parameters, it should ideally mention default behaviors or response format more explicitly to be fully complete.

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. The description adds value by explaining the semantic effect of 'status=published' (returns only courses visible to learners), which goes beyond the schema's enum definition. However, it doesn't provide additional context for 'limit' or 'offset', keeping it at baseline.

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 ('List courses') and scope ('in the authenticated community'), which is specific and distinguishes it from sibling tools like 'hivelearn_get_course' or 'hivelearn_list_course_lessons'. However, it doesn't explicitly differentiate from other list tools like 'hivelearn_list_enrollments' beyond the resource type, missing full sibling distinction.

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 'status=published returns only courses visible to learners', which suggests when to use this parameter for filtering. However, it lacks explicit guidance on when to choose this tool over alternatives like 'hivelearn_get_course' for single courses or other list tools, and no exclusions or prerequisites are 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?

No annotations are provided, so the description carries full burden. It discloses the return fields (enrollment_id, user_id, etc.), which is useful behavioral context. However, it doesn't mention pagination behavior (implied by limit/offset but not explained), rate limits, authentication needs, or whether it's read-only (implied by 'List' but not explicit).

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 action ('List course enrollments') and includes essential details (optional scoping, return fields). Every part earns its place with zero waste or redundancy.

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, no output schema, and 50% schema coverage, the description is moderately complete. It covers the purpose, optional filtering, and return fields, but lacks details on pagination, authentication, error handling, or how results are ordered. For a list tool with 4 parameters, this leaves gaps in operational context.

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 50% (limit and offset have descriptions; user_id and course_id do not). The description adds value by explaining that user_id and course_id are for optional scoping ('scoped to one course or one user'), clarifying their purpose beyond the schema's UUID patterns. However, it doesn't detail format requirements or default behaviors for these parameters.

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 ('List') and resource ('course enrollments') with optional scoping. It distinguishes from sibling 'hivelearn_get_enrollment' (singular) by indicating this lists multiple enrollments. However, it doesn't explicitly differentiate from other list tools like 'hivelearn_list_courses' or 'hivelearn_list_members' beyond the resource type.

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 through 'optionally scoped to one course or one user,' suggesting when to use filtering parameters. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'hivelearn_get_enrollment' (singular) or other list tools, nor does it mention prerequisites or 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 are provided, so the description carries full burden. It discloses the return format ('Returns id, title, start/end dates, event_type, location/meeting_url, attendee counts'), which is valuable behavioral information. However, it doesn't mention pagination behavior (implied by limit/offset but not explained), rate limits, authentication requirements, or whether this is a read-only 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 perfectly concise and well-structured: two sentences that efficiently convey purpose, parameter guidance, and return format. Every word earns its place with zero redundancy or unnecessary elaboration.

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 list tool with no annotations but 100% schema coverage and no output schema, the description provides good context: it explains what's returned, gives practical parameter guidance, and clarifies the tool's scope. The main gap is lack of explicit read-only confirmation and pagination behavior explanation, but overall it's reasonably complete for this complexity level.

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 all three parameters. The description adds context for the 'upcoming' parameter ('for future events only' and 'recommended for calendar UI'), which provides additional semantic meaning beyond the schema's technical description. This justifies a baseline score of 3 with some added value.

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: 'List community events' specifies both the verb (list) and resource (community events). It distinguishes from sibling 'hivelearn_get_event' (singular retrieval) by indicating this is a listing operation, though it doesn't explicitly contrast with other list tools like 'hivelearn_list_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 clear context for when to use the 'upcoming' parameter: 'Use upcoming=true for future events only (recommended for calendar UI).' This gives practical guidance on parameter usage. However, it doesn't specify when to use this tool versus alternatives like 'hivelearn_get_event' or other list tools.

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 describes the return fields (id, role, email, etc.) and filtering capability, which is helpful. However, it lacks details on authentication requirements, pagination behavior (implied by limit/offset but not explained), rate limits, or error handling, making it adequate but incomplete for a list 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 front-loaded with the core purpose in the first sentence, followed by return details and a usage tip, with no wasted words. It efficiently conveys essential information in two concise sentences, making it easy to parse.

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 (list operation with 3 parameters), 100% schema coverage, and no output schema, the description is reasonably complete. It covers purpose, return fields, and a key usage scenario. However, without annotations or output schema, it could benefit from more behavioral context (e.g., pagination details) to achieve full 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 (role, limit, offset) with descriptions and constraints. The description adds minimal value by mentioning role filtering but does not provide additional semantics beyond what the schema offers, aligning with the baseline score for high schema 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 tool's purpose with a specific verb ('List') and resource ('members of the authenticated community'), distinguishing it from siblings like 'hivelearn_get_member' (singular) and 'hivelearn_list_courses' (different resource). It explicitly defines the scope as the authenticated community, which is precise.

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 for usage by mentioning filtering by role to find admins/owners, which helps guide when to use this tool. However, it does not explicitly state when not to use it or name alternatives among siblings, such as 'hivelearn_get_member' for single-member retrieval, leaving room for improvement.

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 mentions the return fields (id, title, etc.) and ordering, but lacks details on permissions, rate limits, pagination behavior beyond limit/offset, or error handling. For a list tool with zero annotation coverage, this is a significant gap in behavioral disclosure.

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 front-loaded with the core purpose and usage, followed by return details, all in two efficient sentences with no wasted words. Every sentence adds value, making it appropriately 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?

Given the tool's complexity (a read-only list operation), 100% schema coverage, and no output schema, the description covers the basics (purpose, filtering, returns) but lacks behavioral context like pagination details or error scenarios. It is adequate but has clear gaps, especially with no annotations to supplement it.

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 (limit, offset, category). The description adds minimal value by mentioning category filtering with an example ('announcements'), but does not provide additional syntax or format details beyond what the schema offers. Baseline 3 is appropriate when the schema handles most 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 verb ('List') and resource ('community feed posts'), specifies the ordering ('newest first'), and distinguishes this tool from its sibling 'hivelearn_get_post' by indicating it returns multiple posts rather than a single one. This is specific and helpful for differentiation.

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 for usage by mentioning filtering by category (e.g., 'announcements'), which helps guide when to apply this tool. However, it does not explicitly state when not to use it or name alternatives among siblings, such as 'hivelearn_get_post' for single posts, leaving room for improvement.

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 states the tool lists questions with ordering and includes options/answers, but doesn't disclose behavioral traits like whether it's read-only (implied by 'List'), pagination, rate limits, authentication needs, error conditions, or what happens with invalid quiz_id. For a tool with zero annotation coverage, this leaves significant 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, efficient sentence that front-loads the core action ('List all questions for a quiz') and adds essential details (ordering, included data) without waste. Every word earns its place, making it highly 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?

Given no annotations, no output schema, and low schema coverage, the description is minimally adequate. It covers the basic purpose and parameter intent but lacks details on behavior, output format, error handling, or usage context. For a read operation with one parameter, it's passable but incomplete for robust agent 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 0%, but the description compensates by implicitly explaining the 'quiz_id' parameter's purpose (to specify which quiz's questions to list). It doesn't detail the UUID format or constraints, but adds meaningful context beyond the bare schema. With only one parameter, this is sufficient for a high score.

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 ('List all questions') and resource ('for a quiz'), specifying the ordering ('by sort_order') and included data ('options and correct answer'). It distinguishes from siblings like 'hivelearn_get_quiz' (which likely gets quiz metadata) and 'hivelearn_create_quiz_question' (a write operation), but doesn't explicitly contrast with 'hivelearn_list_quizzes' (which lists quizzes, not questions).

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?

No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a valid quiz_id), exclusions, or comparisons to other list tools like 'hivelearn_list_course_lessons' or 'hivelearn_list_posts'. The description implies usage for retrieving quiz questions but lacks contextual boundaries.

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 lists quizzes with optional scoping and notes that 'Each quiz belongs to one lesson', which adds useful context about data relationships. However, it fails to describe critical behaviors like pagination (implied by 'limit' and 'offset' parameters but not explained), return format, or any rate limits or authentication needs, leaving significant gaps for a list 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 a single, efficient sentence that front-loads the core purpose ('List quizzes') and adds necessary context. There's no wasted text, but it could be slightly more structured (e.g., separating scoping details into a second sentence for clarity).

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 (4 parameters, no output schema, no annotations), the description is incomplete. It covers the basic purpose and scoping but lacks details on pagination behavior, return values (e.g., quiz fields), error conditions, or how it differs from sibling tools. For a list tool with multiple parameters and no structured output, this leaves the agent with insufficient guidance.

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 50% (only 'limit' and 'offset' have descriptions), and the description mentions optional scoping to 'course or a specific lesson', which maps to the 'course_id' and 'lesson_id' parameters. This adds some meaning beyond the schema's pattern/format details for those parameters. However, it doesn't fully compensate for the coverage gap (e.g., no details on UUID formats or default behaviors beyond 'limit'), resulting in marginal added value.

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 ('List quizzes') and specifies the resource scope ('optionally scoped to a course or a specific lesson'), which distinguishes it from sibling tools like 'hivelearn_get_quiz' (singular retrieval). However, it doesn't explicitly differentiate from other list tools like 'hivelearn_list_courses' or 'hivelearn_list_quiz_questions', missing full sibling distinction.

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 through the optional scoping parameters ('course_id', 'lesson_id'), suggesting when to filter results. However, it lacks explicit guidance on when to use this tool versus alternatives like 'hivelearn_get_quiz' for single quizzes or other list tools for different resources, and provides no exclusions or prerequisites.

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 and does well by disclosing key behaviors: it performs validation (checking lesson content_url), mutates state (sets is_published=true), and returns errors for empty lessons. It doesn't mention permissions, rate limits, or idempotency, but covers the core mutation and validation logic adequately.

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 front-loaded with the main action and validation logic in the first sentence, followed by error handling and diagnostic advice in the second. Every sentence earns its place with no wasted words, making it highly efficient and easy to parse.

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 (a mutation with validation), no annotations, and no output schema, the description is quite complete: it explains the action, validation criteria, error conditions, and diagnostic steps. It could mention the return value format or success behavior, but covers the essential context well.

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 schema has 0% description coverage for the single parameter 'id', but the description implies its purpose by referring to 'a course'. This adds meaningful context beyond the schema's UUID format, though it doesn't specify if 'id' refers to a course ID or something else. For one parameter with low schema coverage, this is good compensation.

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 ('publish') and resource ('course'), explaining it sets 'is_published=true' after validation. It distinguishes from siblings like 'hivelearn_update_course' by focusing on the publishing action rather than general updates.

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 guidance on when to use this tool: after ensuring all lessons have content_url, and it names an alternative tool ('hivelearn_get_course_structure') to diagnose issues before publishing. This clearly defines the prerequisite condition and troubleshooting path.

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 mutation operation ('Set'), describes the atomic nature of the call, and specifies the completeness requirement for lesson_ids. However, it doesn't mention permission requirements, error conditions, or what happens to lessons not included in the list.

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 front-loaded with the core purpose in the first clause, followed by essential usage constraints. Both sentences earn their place by providing critical information with zero wasted words or redundancy.

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 mutation tool with no annotations and no output schema, the description provides adequate basic information about what the tool does and key constraints. However, it lacks details about error handling, permission requirements, and what the tool returns, which would be helpful given the tool's complexity and mutation nature.

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?

With 0% schema description coverage, the description must compensate for the lack of parameter documentation. It provides crucial semantic information about lesson_ids ('desired final order', 'every lesson currently in the module must appear exactly once'), which adds significant value beyond the bare schema. However, it doesn't explain the module_id parameter's purpose or format.

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 ('Set the full lesson order'), target resource ('within a module'), and method ('in one atomic call'), distinguishing it from sibling tools like update_module or list_course_modules. It precisely defines the verb+resource+scope combination.

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 about when to use this tool ('Set the full lesson order within a module') and includes important constraints ('every lesson currently in the module must appear exactly once'), but doesn't explicitly mention when NOT to use it or name specific alternatives among the many sibling tools.

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 of behavioral disclosure. It mentions that using 'revoked' invalidates the certificate and causes the verify endpoint to reject, which adds useful context about consequences. However, it fails to disclose critical behavioral traits such as required permissions, whether the change is reversible, rate limits, or error handling, which are essential for a mutation 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 appropriately sized with two concise sentences that directly address the tool's function and a key usage note. It is front-loaded with the primary purpose, and every sentence adds value without waste. A slight improvement could be made by structuring it more explicitly, but it remains efficient and clear.

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 complexity of a mutation tool with no annotations, no output schema, and 2 parameters (one with enums), the description is incomplete. It lacks details on permissions, reversibility, error cases, or what the tool returns, which are crucial for safe and effective use. The partial parameter explanation and behavioral hint are insufficient for full contextual understanding.

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 adds meaning by explaining the effect of the 'status' parameter, specifically for 'revoked', which clarifies semantics beyond the enum values. However, it doesn't address the 'id' parameter or other status values ('active', 'expired'), leaving gaps. With 2 parameters and partial coverage, this meets the baseline for minimal value addition.

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 ('Change') and resource ('certificate status'), making the purpose specific and understandable. It distinguishes from siblings like 'hivelearn_get_certificate' or 'hivelearn_verify_certificate' by focusing on modification rather than retrieval or verification. However, it doesn't explicitly differentiate from other update tools (e.g., 'hivelearn_update_course'), which slightly limits clarity.

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 implied usage guidance by mentioning 'Use "revoked" to invalidate (verify endpoint will reject)', which suggests when to use the 'revoked' status. However, it lacks explicit when-to-use vs. alternatives (e.g., compared to 'hivelearn_update_course' or other update tools), prerequisites, or exclusions. This leaves gaps in guiding the agent on optimal tool selection.

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 full burden for behavioral disclosure. While it mentions that publishing requires a different tool due to validation, it doesn't address other critical behaviors: whether this is a mutation (implied by 'Edit'), what permissions are needed, whether changes are reversible, or what happens to unspecified fields. For a 14-parameter mutation tool, this is insufficient.

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 with just two sentences that each serve a clear purpose: the first states the tool's function, the second provides critical usage guidance. There's zero wasted language or redundancy.

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 complex mutation tool with 14 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. While it provides excellent usage guidance regarding publishing, it lacks essential information about parameter meanings, behavioral expectations, and response format. The agent would struggle to use this tool correctly without additional context.

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 provides no information about any of the 14 parameters. With 0% schema description coverage, the description fails to compensate by explaining what fields can be edited, their purposes, or constraints. Parameters like 'description_json', 'description_format', and 'difficulty' remain completely undocumented in both schema and 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 verb ('Edit') and resource ('course metadata'), making the purpose specific and understandable. It distinguishes from sibling tools like 'hivelearn_create_course' by focusing on updates rather than creation, though it doesn't explicitly mention all siblings like 'hivelearn_get_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 explicit guidance on when NOT to use this tool: 'Use hivelearn_publish_course instead of setting is_published=true directly — publish runs validation.' This clearly distinguishes between editing metadata and publishing, naming the alternative tool and explaining the rationale.

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 mentions that setting 'is_cancelled=true' preserves history, which is useful context beyond the basic edit action. However, it lacks critical information such as required permissions, whether changes are reversible, error handling for invalid inputs, or what the response looks like (e.g., success confirmation or updated event data). For a mutation tool with 15 parameters and no annotations, this is a significant gap.

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 with two sentences that directly address key functionality. Every word earns its place by providing actionable guidance on cancellation and attendee caps, with no redundant or vague language. It is front-loaded with the core purpose ('Edit an event.') and follows with specific usage 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 complexity (15 parameters, no annotations, no output schema), the description is incomplete. It covers basic purpose and two parameter examples but misses critical context: no explanation of return values, error conditions, authentication needs, or most parameter meanings. For a mutation tool with rich input schema and no structured support, the description should provide more comprehensive guidance to be fully helpful.

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 for all 15 parameters. It only provides semantics for two parameters ('is_cancelled' and 'max_attendees'), leaving the other 13 undocumented. While the examples for those two parameters are helpful, the description fails to explain the purpose, format, or constraints for most parameters (e.g., 'id' as UUID, 'event_type' enum, date formats), making it inadequate given the low 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 verb 'Edit' and resource 'an event', making the purpose specific and understandable. It distinguishes from sibling 'hivelearn_create_event' by focusing on modification rather than creation. However, it doesn't explicitly differentiate from other update tools like 'hivelearn_update_course' beyond the resource type.

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 editing existing events and provides specific guidance on cancellation and attendee cap removal, which suggests when to use those features. However, it doesn't explicitly state when to use this tool versus alternatives like 'hivelearn_get_event' for viewing or other update tools for different resources, nor does it mention prerequisites or error conditions.

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 of behavioral disclosure. It mentions that hivelearn_update_lesson_content is 'safer,' implying this tool might have higher risk (e.g., broader changes), but doesn't detail what 'edit' entails—such as whether it's a mutation, requires specific permissions, or has side effects. For a tool with 13 parameters and no annotations, this is insufficient.

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 zero waste: the first states the purpose, and the second provides crucial usage guidance. It's front-loaded and efficiently conveys essential information without redundancy.

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 complexity (13 parameters, no schema descriptions, no annotations, no output schema), the description is incomplete. It covers purpose and usage guidelines well but lacks details on behavioral traits, parameter meanings, and expected outcomes. For a mutation tool with many undocumented parameters, this leaves significant gaps for an agent.

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%, meaning none of the 13 parameters are documented in the schema. The description only mentions 'metadata' and 'move it between modules,' which vaguely hints at parameters like 'title,' 'module_id,' or 'sort_order,' but doesn't explain any parameters' purposes, formats, or constraints. This fails to compensate for the lack of schema 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: 'Edit lesson metadata or move it between modules.' This specifies the verb ('Edit') and resource ('lesson metadata') with additional scope ('move it between modules'). However, it doesn't explicitly distinguish this from all sibling tools like 'hivelearn_update_course' or 'hivelearn_update_module', which might also edit metadata of other resources.

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 usage guidance: 'To edit ONLY the content body use hivelearn_update_lesson_content (narrower scope, safer).' This clearly states when to use an alternative tool, distinguishing based on scope (metadata vs. content) and safety considerations, which helps the agent choose correctly.

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 mentions the tool is 'safe for bulk agent writes,' which implies reliability for automated operations, but lacks details on permissions, error handling, or mutation effects (e.g., whether changes are reversible). The description adds some context but falls short of fully explaining behavioral traits for a mutation 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 efficiently structured in two sentences with zero waste. The first sentence states the purpose and key parameters, while the second clarifies exclusions and sibling differentiation. Every phrase adds value, making it front-loaded and appropriately sized for the tool's complexity.

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 (8 parameters, mutation operation, no annotations, no output schema), the description is incomplete. It covers purpose and usage well but lacks details on behavioral aspects like permissions or error handling, and parameter semantics are partially addressed. For a mutation tool with rich input schema, more context would be beneficial.

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 schema has 0% description coverage, so the description must compensate. It lists the parameters ('URL, content_type, and optional description/duration/thumbnail'), which maps to 5 of the 8 parameters, providing meaningful context beyond the schema. However, it doesn't cover all parameters (e.g., 'id', 'content_json', 'content_format'), leaving some semantics undocumented.

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 ('Replace just the content of a lesson') and identifies the exact resources affected ('URL, content_type, and optional description/duration/thumbnail'). It explicitly distinguishes this tool from its sibling 'update_lesson' by specifying what it does NOT change ('title, sort_order, or module assignment'), making the purpose highly specific and differentiated.

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 guidance on when to use this tool versus alternatives: it names the sibling tool 'update_lesson' as a broader alternative and states this is 'narrower' and 'safe for bulk agent writes.' It also clearly indicates what fields are excluded, helping the agent understand the appropriate context for selection.

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 of behavioral disclosure. While it mentions the actions (rename/reorder), it lacks critical details such as required permissions, whether the operation is idempotent, potential side effects, or error conditions. For a mutation tool with zero annotation coverage, this is a significant gap.

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, well-structured sentence that efficiently conveys the tool's purpose and usage guideline without any redundant information. It is front-loaded and every word serves a clear purpose.

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 complexity of a mutation tool with 3 parameters (1 required), 0% schema description coverage, no annotations, and no output schema, the description is incomplete. It lacks essential details about parameters, behavioral traits, and expected outcomes, making it inadequate for safe and effective tool invocation.

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%, meaning none of the three parameters (id, title, sort_order) are documented in the schema. The description does not explain what these parameters mean, their purposes, or how they interact (e.g., that 'id' is required to identify the module, 'title' is for renaming, and 'sort_order' is for reordering). This leaves the agent with insufficient guidance.

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 actions ('rename or reorder a module') and distinguishes it from a sibling tool ('hivelearn_reorder_module_lessons') by specifying that reordering lessons within a module requires a different tool. This provides precise verb+resource differentiation.

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 provides when-not-to-use guidance by stating 'To reorder lessons within a module use hivelearn_reorder_module_lessons,' which clearly directs the agent to an alternative tool for a specific related task. This eliminates ambiguity about tool selection.

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 mentions the partial update behavior ('Only include fields you want to change') and the pinning functionality, which is helpful. However, it doesn't address critical aspects like authentication requirements, error handling (e.g., what happens if the ID doesn't exist), rate limits, or whether the operation is idempotent. For a mutation tool with zero annotation coverage, this leaves significant 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 extremely concise with just two sentences that each serve a clear purpose: stating the tool's function and providing specific usage guidance. There's no wasted language, and the information is front-loaded effectively.

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 complexity (6 parameters, mutation operation, no output schema, 0% schema description coverage, and no annotations), the description is insufficient. It covers basic purpose and partial update behavior but misses critical context like authentication needs, error conditions, response format, and detailed parameter explanations. For a tool that modifies data, this level of documentation creates significant uncertainty for an agent.

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 some semantic context beyond the schema: it explains that only changed fields should be included (partial update pattern) and clarifies the purpose of 'is_pinned' for pinning/unpinning. However, with 0% schema description coverage and 6 parameters, it doesn't explain parameters like 'content_json', 'content_format', or 'category', leaving them undocumented. The baseline is 3 since it provides some value but doesn't fully compensate for the schema coverage gap.

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 ('Edit an existing post') and resource ('post'), making the purpose immediately understandable. It distinguishes itself from sibling tools like 'hivelearn_create_post' by focusing on editing rather than creation. However, it doesn't specify what aspects of a post can be edited beyond the implied fields.

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 guidance with 'Only include fields you want to change' and mentions the specific use case for 'is_pinned' to pin/unpin posts. However, it doesn't explicitly state when to use this tool versus alternatives like 'hivelearn_update_course' or other update tools, nor does it mention prerequisites or error conditions.

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 mentions a constraint about moving quizzes, which is useful, but lacks critical information: it doesn't specify if this is a mutation (implied by 'Edit'), what permissions are required, whether changes are reversible, or what happens to unspecified fields. For a 7-parameter update tool with zero annotation coverage, this is insufficient.

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 with just two sentences that are front-loaded and waste no words. The first sentence states the core purpose, and the second adds crucial usage guidance, making every word earn 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 complexity (7 parameters, 1 required, no output schema, and no annotations), the description is incomplete. It covers purpose and one constraint but misses parameter explanations, behavioral details like permissions or side effects, and output information. This is inadequate for an update tool with multiple parameters.

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 schema description coverage is 0%, meaning none of the 7 parameters have descriptions in the schema. The tool description provides no information about any parameters—it doesn't mention 'id' (required), 'title', 'description', 'max_attempts', 'passing_score', 'shuffle_questions', or 'time_limit_seconds'. This leaves all parameters undocumented, failing to compensate for the schema gap.

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 the verb 'Edit' and resource 'quiz settings', making it immediately understandable. However, it doesn't explicitly differentiate from sibling update tools like 'hivelearn_update_quiz_question' or 'hivelearn_update_course', which might also involve editing educational content.

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 guidance on when NOT to use this tool ('Cannot move a quiz to a different lesson') and suggests an alternative ('create a new one instead'). This helps distinguish it from creation tools, though it doesn't specify when to use it versus other update tools for quizzes or related resources.

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 mentions a behavioral constraint about resupplying data when changing question_type, which is valuable. However, it doesn't cover critical aspects like whether this is a mutation tool (implied but not stated), permission requirements, error handling, or what happens to unspecified fields during update.

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 with just two sentences that directly address the core functionality and a key behavioral constraint. Every word earns its place with zero wasted text, making it easy to parse and understand quickly.

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 mutation tool with 8 parameters, 0% schema description coverage, no annotations, and no output schema, the description is incomplete. It covers the basic purpose and one behavioral constraint but lacks information about what fields can be updated, how partial updates work, what the response contains, or error conditions that might occur.

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?

With 0% schema description coverage for 8 parameters, the description adds minimal value beyond the schema. It mentions 'options' and 'correct_answer' in the context of question_type changes, providing some semantic context for those two parameters, but leaves the other 6 parameters (id, points, sort_order, explanation, question_text, question_type) without additional meaning beyond what the schema provides.

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 ('Edit') and resource ('a question'), making the purpose immediately understandable. It distinguishes from sibling 'hivelearn_create_quiz_question' by focusing on editing rather than creation, though it doesn't explicitly contrast with other update tools like 'hivelearn_update_quiz'.

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 implied usage guidance by mentioning that changing question_type requires resupplying options or correct_answer, which suggests when to use this tool versus alternatives like creating a new question. However, it lacks explicit when-to-use or when-not-to-use statements compared to other update tools or the create sibling.

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 full burden. It discloses the return structure ('Returns { valid, certificate } if found and active') which is valuable behavioral information. However, it doesn't mention error conditions, authentication requirements (though 'Public' implies none), rate limits, or what happens with invalid codes. For a verification tool with zero annotation coverage, this is adequate but incomplete.

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?

Two sentences with zero waste. The first sentence states purpose and behavior, the second provides usage guidance. Every word earns its place, and the structure is front-loaded with the core functionality followed by contextual guidance.

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 single-parameter verification tool with no annotations and no output schema, the description provides good coverage: purpose, usage context, return structure, and parameter context. It doesn't explain error cases or the exact format of the certificate object, but given the tool's relative simplicity and the clear return specification, it's mostly complete.

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 the 'code' parameter. The description adds minimal value by specifying it's a 'verification_code printed on the certificate' which provides real-world context. With excellent schema coverage, the baseline is 3, and the description earns an extra point for adding practical context about the code's origin.

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 ('Public verification lookup'), identifies the resource ('by verification_code'), and distinguishes it from siblings like 'hivelearn_get_certificate' by specifying it's for third-party verification rather than internal retrieval. It provides a clear verb+resource combination with explicit scope.

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 states when to use this tool: 'Use this when a third party presents a certificate code.' This provides clear contextual guidance that differentiates it from internal lookup tools like 'hivelearn_get_certificate' and indicates it's for external verification scenarios.

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