HiveLearn

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

No annotations are provided, so the description carries the transparency burden. It does disclose useful behaviors: sort_order defines ordering, unlock_after_course_id gates completion, and is_required defaults to true. However, it does not mention side effects such as what happens if the course already exists in the track, validation errors, or the return value. This is adequate but not exhaustive.

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

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

The description is three compact sentences, each carrying useful information. It front-loads the primary purpose and then explains the two most nuanced parameters and a default. There is no redundancy or fluff.

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 straightforward add operation with no output schema, the description covers the essential aspects: what the tool does, how ordering works, how prerequisites work, and the default for is_required. It does not describe the response format or duplicate-handling behavior, but given the tool's simplicity and the absence of an output schema, this is a minor gap.

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 60%, and the description compensates by clarifying the semantics of key parameters: 'sort_order sets the order within the track', 'prerequisite course that must be completed first', and 'is_required defaults to true'. Track_id and course_id are self-explanatory names, so no additional detail is needed.

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 opens with a specific verb and resource: 'Add a course to a track at a given position.' It clearly distinguishes this from sibling tools like create_course (which creates a new course) and create_track (which creates a track). The position aspect is a unique differentiator.

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 that this tool is for adding an existing course to an existing track, with positioning and prerequisites. It does not explicitly name alternatives or state when not to use it, but the context is unambiguous enough for an agent to select it over related 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. It discloses that this is a mutation that bypasses the normal automated process, but it does not mention permissions, idempotency, what happens if a certificate already exists, or the return value. These are significant gaps for a manual issuance 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 main action and purpose, and contains no unnecessary filler. Every sentence adds value.

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

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

Given six parameters, no output schema, and no annotations, the description should cover prerequisites (e.g., enrollment/completion), duplicate handling, and return behavior. It covers only the core purpose and when to use it, leaving major operational details unaddressed.

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 33%, and the description does not compensate. It only implies user_id and course_id; final_score, instructor_name, and completion_date are not explained, and expires_at/completion_date semantics are left entirely to the schema. The description adds no meaningful parameter-level detail.

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 uses a specific verb ('Issue') and clearly identifies the resource ('certificate of completion') and target ('a user for a course'). It also distinguishes itself from sibling certificate tools (get, update, verify, list) by framing this as an issuance action, and differentiates from the normal auto-issuance flow.

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: 'Normally the platform auto-issues on course completion — use this tool for bulk backfill or manual awards.' This provides direct when-to-use guidance and contrasts it with the automatic alternative.

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 a key behavioral trait: the created course will be empty of modules/lessons. While it doesn't mention return values or permissions, for a create operation this scope clarification is valuable and meets the transparency bar.

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 primary action. The second sentence efficiently provides an alternative. Every word earns its place, with no 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 10-parameter tool with no annotations and no output schema, the description gives the core purpose and alternative guidance, but leaves parameter details to the schema, which is mostly undocumented. It is minimally viable but lacks richness; additional details on required fields or default behavior would improve completeness.

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

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

Schema description coverage is only 10% (only 'visibility' has a description), yet the description adds no parameter-specific meaning. It does not explain fields like 'description_json' or 'difficulty', leaving the agent to infer from names. Since coverage is low, the description needed to compensate but did not.

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 function with a specific verb and resource: 'Create an empty course'. It explicitly notes 'no modules/lessons', distinguishing it from creation tools that scaffold structure. It also differentiates from the sibling tool hivelearn_create_course_outline, making the purpose unambiguous.

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?

Provides explicit guidance: 'Prefer hivelearn_create_course_outline when you already know the structure — it scaffolds course + modules + lesson placeholders in one call.' This tells the agent exactly when to use this tool versus an alternative, which is high-quality 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, the description carries the full burden for behavioral disclosure. It transparently states that only placeholders are created ('no content yet') and that it returns lesson ids for a follow-up loop, revealing the composite create behavior. It does not mention potential side effects, permissions, or idempotency, but for a create-style tool, the essential behavior is clearly conveyed.

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

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

The description is three sentences, all of which are information-dense and earn their place. It front-loads the primary action, then explains the follow-up, then gives a clear usage preference. There is no repetition or fluff.

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?

The tool is fairly complex (11 params, nested objects, no output schema), and the description provides a solid high-level overview but leaves gaps. It explains the course outline concept and the follow-up update step, but it does not describe the full return structure (e.g., course id, module ids) or any prerequisites. Since there is no output schema, the description should more fully cover return values and overall workflow.

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 9% (only 'modules' has a description). The description adds some meaning by explaining lessons as 'title/description only' and implying that 'title' is the course title, but it does not cover the many other parameters (tags, difficulty, visibility, etc.) nor provide any syntax or format details. With such low coverage, the description should compensate more than it does.

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 uses a specific verb 'Scaffold' and names the exact resource scope: 'course + modules + lesson placeholders (title/description only, no content yet).' This clearly distinguishes it from sibling tools like hivelearn_create_course (which likely creates only a course) and hivelearn_create_lesson/module, by emphasizing the one-call combined creation.

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 context: 'This is the preferred first step when authoring a new course from a plan.' It also gives a direct follow-up instruction: 'loop over the returned lesson ids with hivelearn_update_lesson_content.' However, it does not state when NOT to use this tool or name alternative approaches (e.g., using hivelearn_create_course + create_module + create_lesson), so it lacks full exclusion 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, the description carries the transparency burden. It discloses two key behaviors: both IDs must belong to the authenticated community and duplicate enrollments are rejected. However, it does not mention return values, error handling, permissions, or side effects, leaving some behavioral ambiguity.

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 action, and every sentence adds meaningful information. No filler or redundant phrasing.

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?

The tool is simple (two required UUID parameters) and the description covers the core purpose and two important constraints. It lacks mention of return value or error behavior, but those could be inferred from the action. Given the simplicity, it is 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 coverage is 0%, so the description must compensate. It adds the community constraint for both user_id and course_id, which is helpful, but it does not individually explain the semantics of each parameter beyond their names. The tool name and first sentence already imply the roles, but the description could go further.

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

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

The description states a clear action ('Enroll a user in a course') with a specific verb and resource, and it adds constraints (community membership, duplicate rejection) that distinguish it from sibling tools. This clearly separates it from create_course, create_certificate, and other create operations.

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

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

The description provides clear context for when to use the tool: to enroll a user in a course. It implies a prerequisite (community membership) and a constraint (duplicates rejected) but does not explicitly name alternative tools or state when not to use it. The context is sufficient for a straightforward creation tool with no direct 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, the description carries the burden and does disclose input format (ISO 8601, UTC) and the relationship between event_type, meeting_url, and location. However, it does not mention return values, side effects, or permission 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 two sentences with no extraneous content. It front-loads the core purpose and provides essential details efficiently.

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 13-parameter tool with no output schema, this description is somewhat minimal. It covers the critical date and event_type logic, but ignores many optional fields and does not describe the response shape, leaving gaps for 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 description adds semantic value by specifying the UTC format for dates and explaining how event_type determines whether meeting_url or location is relevant. This compensates for the low schema coverage, though it leaves many optional parameters 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 'Create a calendar event', using a specific verb and resource, and distinguishes it from other create_* siblings by specifying the domain. The additional detail about event_type further clarifies the tool's purpose.

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 creating calendar events, but does not explicitly discuss when to use this tool over update_event or list_events. It does provide clear context for creation, but lacks exclusions or alternative recommendations.

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

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

With no annotations, the description carries the full burden. It discloses the constraint that content_type must match content_url and explains the module_id null behavior. However, it does not describe potential side effects, return values, authorization needs, or error conditions, leaving significant behavioral gaps for a create 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 two sentences, front-loaded with the core purpose, and every sentence adds value. It avoids redundancy and extraneous detail, making it easy for an agent 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?

For a 13-parameter tool with no output schema and no annotations, the description covers the essential behavior but omits guidance on most optional parameters. It is adequate for a basic invocation but incomplete for a fully informed agent deciding on optional fields like content_format, is_published, or content_json.

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 8% (only is_preview has a description), so the description must compensate. It does add valuable meaning for the non-obvious parameters: content_type/content_url matching (with a concrete youtube example) and module_id null semantics. However, it does not explain other parameters like content_json, content_format, sort_order, or duration_seconds, which remain underspecified.

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 action and resource: 'Add a lesson to a course.' This specific verb+resource phrasing distinguishes it from sibling tools like create_module or update_lesson. The additional context about content_type matching and module assignment further clarifies its 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 clear guidance on how to use the tool for key parameters (content_type must match content_url, module_id=null vs. a module uuid from list_course_modules). It references a sibling tool for valid module IDs. However, it does not explicitly state when to use this tool instead of alternatives like update_lesson or create_module, so it stops short of full when/when-not 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 the full burden. It discloses one behavioral trait: 'sort_order is optional — server auto-appends if omitted.' This is useful, but it omits other important behaviors such as return values, whether the course must exist, and any side effects on existing modules. Moderate transparency.

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 concise sentences, front-loaded with the main action. Every word earns its place, and it is appropriately sized for a simple create operation.

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 basic create tool with 3 parameters and no output schema, the description is adequate: it states the purpose and the key optional behavior. However, it lacks details on what the tool returns, whether the course_id must reference an existing course, and how this relates to other module-focused tools. Complete enough for selection but not for full invocation 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 adds semantic meaning for sort_order ('server auto-appends if omitted'), which the schema alone does not convey. However, it does not explain the meaning or format of course_id or title beyond their names, and schema description coverage is low at 0%. It partially compensates but leaves 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 'Add a module to a course' uses a specific verb (add) and resource (module to a course), clearly distinguishing it from sibling tools like create_lesson or create_course. It is unambiguous about the operation.

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 does not mention prerequisites (e.g., course must exist), nor does it contrast with creating lessons or other components. The usage context is only implied by the verb 'add'.

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 for behavioral disclosure. It does specify key constraints (content/content_json requirement, format matching, category default) but doesn't address edge cases like what happens if both content and content_json are supplied, or what the response/visibility implications of 'publish' are.

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 compact sentences, front-loaded with the primary action and then covering the essential constraints. Every sentence contributes value without repetition or fluff.

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 moderate-complexity create tool with no output schema and no annotations, the description covers the main usage rules (which fields, format matching, category) but omits edge cases and post-creation behavior. It is adequate for basic invocation but leaves some gaps in understanding the full behavior.

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 already describes content, content_json, and content_format, but the description adds the crucial exclusivity/requirement relationship and clarifies the category default ('general'). This meaningfully supplements the schema, especially for the undocumented category 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 opens with a specific action—'Publish a new post to the community feed'—clearly stating the resource (post) and the destination (community feed). This verb+resource+context combination distinguishes it from other create_* siblings and from update_post.

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 gives clear context for creation (a new post) and explains the required field relationship (either content or content_json) and the format-matching rule. It doesn't explicitly mention when not to use this tool or alternatives like update_post, but the creation context is unambiguous.

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

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

With no annotations, the description must carry the behavioral burden. It provides useful semantics (passing_score as percent, omitting time_limit_seconds/max_attempts for unlimited), but does not disclose permissions, side effects, or return value. This is reasonable but incomplete for a create 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 two concise sentences, front-loading the core purpose and then adding essential parameter semantics. Every word earns its place 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?

For a 7-parameter create tool with no output schema and no annotations, the description is adequate but not complete. It gives key parameter hints but misses return value, prerequisites, and deeper behavioral context, making it minimally viable.

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

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

Schema coverage is 0% and the description partially compensates by explaining passing_score range and the 'omit for unlimited' behavior for two parameters. However, it does not clarify title, lesson_id, description, or shuffle_questions, leaving 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 'Create a quiz attached to a lesson', which specifies the verb, resource, and attachment context. This distinguishes it from sibling tools like create_quiz_question and 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 implies usage for creating quizzes in a lesson context, but does not explicitly state when to use this tool versus alternatives or provide exclusions. It offers some context ('attached to a lesson') but lacks direct comparison with siblings.

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

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

With no annotations, the description must disclose behavioral traits. It explains the conditional behavior for question types (options vs. correct_answer) and adds the validation rule that at least one option must be correct. However, it does not mention prerequisites (e.g., quiz existence), side effects, or safety implications, leaving the full mutation context undisclosed.

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, directly addresses the primary purpose, and uses clear structure to separate multiple_choice and true_false instructions. Every sentence contributes useful information with zero 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 8 parameters, 3 required, low schema coverage, and no output schema, the description is too brief to be contextually complete. It covers the key type-specific behavior but omits guidance on required fields (quiz_id, question_text, question_type) and optional parameters like sort_order and explanation, leaving the agent with gaps in understanding the full invocation 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 low (38%), so the description is expected to compensate. It clarifies the structure of options and the requirement for correct_answer in true_false, adding the 'at least one is_correct:true' rule. It does not explain other parameters like quiz_id, question_text, sort_order, or explanation, so only partial compensation is achieved.

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 opens with 'Add a question to a quiz,' using a specific verb and resource that clearly distinguishes it from sibling tools like create_quiz and update_quiz_question. The focus on adding a question (not updating or listing) directly matches the tool name and intent.

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, type-specific usage guidance: for multiple_choice pass options with at least one correct, for true_false omit options and set a boolean correct_answer. It implies when to use this tool (to create a quiz question) but does not explicitly contrast it with alternatives like update_quiz_question or list_quiz_questions.

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, it provides useful constraints: 'at least one is required' for resource_url/file_url, price > 0, and access_level defaults to public. However, it does not disclose other defaults (is_published, resource_type) or side effects, limiting transparency.

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, front-loaded with purpose, and every clause adds information. Extremely concise without sacrificing 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?

For a 12-parameter tool with no output schema and no annotations, the description is somewhat thin. It covers key usage constraints but omits several default behaviors and parameter roles, making it adequate but not comprehensive.

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

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

Schema coverage is roughly 50%, and the description adds meaning for a few parameters (e.g., the mutual exclusivity of resource_url/file_url, price positivity). But it ignores many parameters like folder_id, is_premium, or cover_image_url, so it only partially compensates for the schema 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 starts with a clear verb+resource: 'Add a resource to the community library.' This distinguishes it from sibling create tools like create_course or create_post, making its purpose unambiguous.

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

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

It implies usage for community library resources but does not explicitly mention when to prefer this tool over alternatives or state exclusions. The guidance on choosing between resource_url and file_url is parameter-level, not tool-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, the description carries the full behavioral burden. It usefully discloses the is_published default and advises deferring publication, but it does not mention the return value, required permissions, or any side effects beyond creation. This leaves some ambiguity about what the agent receives after calling the 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?

Three short sentences with no filler. The first sentence states purpose, the second gives the next action, and the third explains the default behavior. Every sentence earns its place.

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

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

The description covers the creation intent and the follow-up workflow, but because there is no output schema, it should explain what the tool returns—particularly since the track ID is presumably needed for hivelearn_add_track_course. It also leaves several parameters under-elaborated, making the overall guidance useful but incomplete.

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 14% (only is_published gets an inline description). The description repeats that default but leaves title, description, thumbnail_url, estimated_hours, and difficulty_level unexplained beyond their schema types and enum choices. The 'curated course sequence' phrase adds conceptual context but not per-parameter semantics.

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 opens with 'Create a learning track (curated course sequence)', clearly identifying the action and resource. It distinguishes from sibling tools by tagging hivelearn_add_track_course as the next step and separating 'track' from raw course creation.

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

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

It gives explicit workflow guidance: 'After this, add courses in order with hivelearn_add_track_course' and 'publish once the curriculum is set.' This directly instructs the agent on sequencing and when to set is_published, though it does not explicitly list when not to use the tool versus other create_* siblings.

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 burden of behavioral disclosure. 'Fetch' implies a safe read operation, and the mention of 'Includes verification_code and download URL' hints at the return content. However, it does not explicitly state error behavior, permissions, or any side effects, leaving gaps for a simple get 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 exceptionally concise and front-loaded, using two short sentences that convey the essential purpose and key return fields. There is no wasted verbiage, and the key information is immediately accessible.

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 (1 parameter, no annotations, no output schema), the description is largely complete: it states the operation, the parameter, and two useful fields in the response. It could be more complete by noting not-found behavior or explicitly stating read-only status, but these are minor gaps for a straightforward get-by-id 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?

The schema provides no description for the 'id' parameter (0% coverage), so the description must compensate. Saying 'by id' confirms the parameter's role, but it adds little beyond what the tool name and parameter name imply. It does not clarify the UUID format or other constraints already in the schema, so the added value is minimal.

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 uses the specific verb 'Fetch' and resource 'one certificate by id', which clearly states the tool's action and scope. It naturally distinguishes from sibling tools like list_certificates (which retrieves multiple) and verify_certificate (which verifies), making its purpose unambiguous.

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 clearly indicates when to use this tool: when you need to fetch a single certificate by ID. However, it does not explicitly mention alternatives or exclusions, such as using list_certificates for multiple certificates, so it lacks the explicit when-not guidance needed for a 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?

With no annotations, the description carries the full burden. It discloses that the API key scopes the tenancy and that switching is not possible, which is important behavioral context. It also states the fields included in the return value, though it does not describe the full return shape or error 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 two sentences, with the core purpose in the first sentence and usage guidance in the second. There is no filler, and it is immediately scannable.

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 zero-parameter tool with no output schema, the description is complete: it says what is returned, when to use it, and the key tenancy constraint. The agent has everything needed to select and invoke the tool correctly.

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?

There are zero parameters, so the baseline is 4. The description adds valuable context by explaining that the API key itself determines the result, which clarifies why no parameters are needed. This goes beyond the empty 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 identifies the tool's function with the verb 'Returns' and specifies the exact resource: 'the community the current API key is scoped to'. It also lists the key fields returned (community_id, owner, slug, tier), which distinguishes it from any sibling tool.

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

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

It explicitly states when to use the tool: 'Use this first whenever you need the community_id, owner, slug, or tier'. It also explains the API key determines tenancy and that you cannot switch community. It lacks an explicit 'when not to use' clause, but no sibling tool competes for this same purpose.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns metadata and implies it does not return the lesson tree by pointing to the sibling. It also lists specific return fields, adding behavioral context beyond a simple 'get' call, though it omits error handling or auth details.

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

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

The description is two sentences with no filler. The first sentence defines purpose and key fields, the second gives a precise alternative. It is front-loaded and efficient, with every word earning 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?

For a single-parameter read tool with no output schema or annotations, the description covers the main purpose and alternative but lacks details on response format, error handling, or parameter specifics. It is adequate for basic selection but not fully complete for an agent requiring comprehensive 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 0%, and the description does not explain the 'id' parameter at all. It only implies that the id identifies the course, but fails to clarify the special UUID patterns or how to obtain the id, thus not compensating 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 uses the specific verb 'Fetch' with a clear resource 'one course' and lists the metadata fields (description, instructor, tags, flags). It explicitly distinguishes itself from the sibling tool hivelearn_get_course_structure, making the purpose unambiguous.

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

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

It provides direct usage guidance by stating 'For the lesson tree use hivelearn_get_course_structure instead,' which clearly tells the agent when not to use this tool and which alternative to choose. This is an explicit exclusion and alternative reference.

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 must carry the full burden of behavioral disclosure. It describes what data is returned but does not explicitly state that this is a read-only operation, nor does it mention pagination, error behavior, or any side effects. For a get tool, this lack of safety or traversal detail is a notable 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 two short sentences, front-loaded with the core purpose and a usage hint. Every word earns its place; there is no repetition of schema field names or obvious fluff.

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?

With 4 parameters, no output schema, and zero annotations, the description is too brief to be fully actionable. It does not mention pagination (despite limit/offset parameters), response structure, or how 'pass/fail' is represented. For a tool of this complexity, the description should provide more context about the returned data and query behavior.

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 50% (limit and offset have descriptions, course_id and user_id do not). The description adds some meaning by mentioning 'per-user' (user_id) and 'for a course' (course_id), but it does not explain limit/offset beyond what the schema already provides. Overall, the description does not fully compensate for the missing 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 identifies the resource as a quiz-score gradebook for a course, specifying per-user attempts, scores, and pass/fail status. This distinguishes it from any sibling tool, as no other gradebook tool exists. The verb 'get' is implicit in the name, and the description conveys the exact data 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 states 'Use for analytics or grading dashboards,' providing clear context for when this tool is appropriate. However, it does not explicitly mention alternatives or when not to use it, though the unique gradebook purpose makes alternatives less relevant.

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

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

With no annotations, the description carries the burden of disclosing behavior. It does disclose return fields (percent_complete, last_accessed_at, completed_lesson_count) and the aggregate nature, but omits details like pagination semantics, whether user_id filters to a single user, sorting behavior, or any access constraints. This is adequate but not rich.

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

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

Two concise sentences, front-loaded with a clear verb and resource. The first sentence states the action and scope; the second lists the return fields. No filler 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 get-type tool with 4 parameters and no output schema, the description covers the essential purpose and return shape. It adequately explains that results are per-user rows, which is critical. Gaps include lack of ordering/pagination context and clarification of the user_id optionality, but these are minor given the simple nature of the 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 high (75%), so the baseline is 3. The description adds some context beyond the schema by mentioning 'per-user' and 'course', but it does not clarify the course_id parameter (which lacks a schema description) or the optional user_id filter behavior. It adds marginal value over the structured data.

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 function with a specific verb ('Aggregate') and resource ('lesson-completion progress for a course across enrolled users'), immediately distinguishing it from sibling progress tools like hivelearn_get_track_progress. It also lists the key returned fields, leaving no ambiguity about purpose.

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 tool: aggregating lesson-completion progress for a course across enrolled users. It does not explicitly mention alternatives or exclusions, but the context is strong enough to differentiate from similar tools like get_course_gradebook or get_enrollment.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It reveals the output is a compact tree with only titles, and explicitly states what is excluded (lesson body content). This gives useful behavioral insight, though it does not cover all possible aspects like error handling or permissions.

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 concise with two sentences. The first sentence states the core function, the second provides use cases and a key limitation. No unnecessary words are used, and it is 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?

The tool is simple with a single parameter and no output schema. The description explains the return structure at a high level (course → modules → lessons) and notes the exclusion of body content. This is sufficient for the tool's complexity, though a bit more detail on node structure would be 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?

The schema has one parameter (id) with 0% description coverage. The description does not explicitly explain the parameter, but the phrase 'of the course' and the tool name imply that the id refers to a course. While not fully compensating, the parameter's meaning is largely self-evident, so a mid-range score is appropriate.

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

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

The description clearly states the tool returns a 'compact titles-only tree of the course: course → modules → lessons', which is a specific verb and resource. It also distinguishes itself by explicitly noting it does NOT include lesson body content, separating it from sibling tools like get_lesson or list_course_lessons.

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 usage context by saying it is 'Ideal for agents to plan reorders, spot empty lessons, or summarize a course'. It does not explicitly mention alternatives or when not to use it, but the intended scenarios are clearly 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, the description carries the full burden. It discloses the return content (status and progress) and the action (fetch), implying read-only behavior. However, it does not mention error behavior, permissions, or side effects, which would add risk 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 a single sentence with no redundant words. It is front-loaded with the action and resource, making it highly concise and scannable.

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 get-by-ID tool with one parameter and no output schema, the description provides sufficient context about the return (status and progress). It could be more complete by mentioning not-found behavior or permissions, but given the tool's simplicity, it covers the essentials.

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 'id' parameter. The description adds meaning by clarifying that the ID refers to an enrollment ('one enrollment'), but it does not explicitly state 'the enrollment ID' or provide format details beyond the schema's UUID pattern.

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 uses a specific verb ('Fetch'), states the resource ('one enrollment'), and includes scope ('with status and progress'). It clearly distinguishes this from sibling tools like list_enrollments by indicating it fetches a single enrollment.

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

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

The description implies usage for fetching a single enrollment by ID, but does not explicitly state when to prefer this over list_enrollments or mention alternatives. It is clear enough for the reader to infer, but lacks explicit 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?

No annotations are provided, so the description carries the burden. It discloses that the response includes full description and RSVP metadata, giving some insight into return value. However, it does not mention potential errors, authorization needs, or any other behavioral traits beyond the core fetch action. The read-only nature is implied by 'Fetch' but not explicitly stated.

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

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

The description is one concise sentence that immediately states the action and resource. It contains no fluff and every word adds value, 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?

The tool has a single simple parameter, no output schema, and no annotations. The description covers the core functionality and indicates the response content (full description and RSVP metadata). It is sufficiently complete for such a straightforward get-by-id tool, though it could mention the return format or error behavior 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?

The input schema has only one parameter (id) with format details, and schema description coverage is 0%. The description clarifies that the id refers to an event, which adds semantic meaning, but it does not provide additional details about the parameter itself. Given the simplicity of the single parameter, a score of 3 is appropriate.

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

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

The description uses a specific verb 'Fetch' with a clear resource ('one event') and identifier ('by id'), and distinguishes the action from sibling tools like list_events or create_event by specifying the retrieval of a single event with additional context. It clearly communicates the tool's scope and purpose.

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 when to use the tool (when a single event's full details are needed by id) but does not explicitly exclude alternatives like list_events. It provides clear context without stating when-not-to-use or naming alternatives, which places it at a strong 4.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It does add value by disclosing that each entry contains 'rank and the points for the selected period, plus level info,' and explains period windows. However, it does not explicitly state that this is a read-only operation, nor does it clarify pagination behavior or ordering direction beyond 'ranked.'

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 tool's purpose. Every sentence provides useful information: the resource, the period windows, and the response entry structure. No unnecessary words or repetition.

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 tool with three self-explanatory parameters and no output schema, the description is quite complete. It covers purpose, response contents, and period semantics. However, it could be marginally more complete by explicitly noting that pagination via limit/offset applies and that the operation is read-only, though these are implied by the parameter schema and the tool name.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds some semantic context by explaining that 'period chooses the window' and that points are 'for the selected period,' which complements the schema's enum. However, it does not add further detail for limit or offset beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: 'Ranked member leaderboard for the community.' It uses a specific verb ('get') and resource ('leaderboard'), and distinguishes it from other tools by describing the leaderboard specific to the community. The period options are explicitly mentioned, further clarifying its functionality.

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

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

The description implies usage for retrieving a community leaderboard and explains the period parameter window, but it does not explicitly state when to use this tool over alternatives or provide any exclusions. Sibling tools like get_member_points exist but are not referenced, leaving the guidance implicit.

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 states the return format ('same fields as list_members for one row') and implies a read-only 'fetch' operation, but it does not disclose error behavior, whether the member must exist, or any authorization requirements. It adds some context beyond the schema but leaves 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 two concise sentences, front-loaded with the action, and every clause contributes: it specifies the identifier and clarifies the output shape by referencing list_members. No unnecessary 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?

Given the tool's simplicity (one parameter, no output schema), the description provides sufficient context by linking the return fields to list_members. However, it omits behavior for not-found cases or errors, which would make it fully complete. For a simple fetch, this is a minor gap.

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%; the parameter 'id' already has a detailed description, format, and pattern. The tool description simply restates 'profile uuid' and references list_members, which adds marginal value but essentially duplicates schema information. Baseline 3 is appropriate.

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

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

The description clearly states the action ('Fetch a single member'), the resource ('member'), and the method ('by profile uuid'). It distinguishes from sibling tools like list_members (by emphasizing 'single' vs. the list) and get_member_points (by the resource being member, not points).

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 when to use this tool: when you need one member identified by profile uuid. It also clarifies the output is a single row with same fields as list_members, which suggests using this instead of list_members for a specific member. However, it doesn't explicitly mention alternatives or exclusions for edge cases.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It explicitly mentions the 404 error case for members without a points record, and clarifies the lookup key ('by auth user id'). It does not discuss rate limits or auth, but for a read-only get operation, this is solid 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 two sentences with no filler. The first sentence front-loads the key information and enumerates the output components; the second adds the error condition. Every word earns its place.

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

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

For a single-parameter get tool with no output schema, the description spells out the return fields and the 404 behavior. It does not explain the meaning of 'period totals' or the community context, but that is minor for this simple tool. The description is essentially complete for the agent to select and invoke 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 coverage is 100% and the schema already describes user_id as 'auth user id (community_members.user_id)'. The description reinforces this by saying 'by auth user id' but adds little new meaning about the parameter itself. Baseline 3 is appropriate given the schema covers it fully.

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 a specific verb ('get') and resource ('member points'), enumerating the exact contents (total, per-category, period totals, level). It distinguishes from siblings like get_member (profile) and get_leaderboard (ranking) by detailing the breakdown.

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 context is clear: this is for retrieving a full points breakdown for one member. It does not explicitly mention alternatives or when-not-to-use, but the specialized scope and the 404 condition effectively convey its purpose. No exclusions are stated, so this remains a clear if not exhaustive guideline.

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 discloses a key trait: the reply thread is not included, which is valuable. However, it does not mention auth requirements, return format, or error handling, so transparency is partial but adequate for a simple fetch 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 two short sentences with the verb and resource front-loaded. Every word earns its place, and the exclusion note is concise and informative 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?

For a single-resource get tool with one parameter and no output schema, the description is fairly complete. It specifies what is returned (full post content) and what is not (reply thread), covering the essential usage context. Minor omissions like error conditions are acceptable for such a simple 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?

The schema provides the 'id' parameter with a UUID format but no description. The description's phrase 'Fetch one post' makes it obvious that 'id' refers to the post identifier, effectively compensating for the 0% schema description coverage. This is sufficient for a single self-evident 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 uses a specific verb ('Fetch') and resource ('one post'), clearly distinguishing it from list operations like hivelearn_list_posts. The phrase 'with full content' adds specificity about what is retrieved, making the tool's purpose unmistakable.

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 clearly implies this tool is for retrieving a single post's full content, and the explicit note that the reply thread is NOT included helps prevent misuse when a thread is needed. It does not name an alternative sibling tool, but the implied usage context is clear and the exclusion note provides practical 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 the burden of behavioral disclosure. It adds important context about what is not included (questions) and what metadata fields to expect, but it does not disclose error behavior, authentication requirements, or return format beyond the listed fields.

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, front-loaded sentence that immediately states the primary action and resource, then adds one clarifying exclusion and a concrete alternative. Every word earns its place, with no 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?

Given the low complexity (one parameter, no output schema), the description covers the key aspects: what is returned, what is not returned, and where to go for the missing part. It falls short only in omitting possible error/not-found behavior, but for a simple get-by-id tool this is a minor gap.

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 one 'id' parameter with no description (0% coverage). The tool description does not explicitly explain that 'id' refers to the quiz ID, though the tool name and 'Fetch one quiz' make it inferable. This is minimally sufficient but does not fully compensate for the missing schema 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 uses a specific verb 'Fetch' with a clear resource 'one quiz' and enumerates the included metadata (passing_score, time_limit, max_attempts). It also explicitly differentiates from sibling tools by stating questions are not included and naming the alternative tool.

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 clearly indicates when to use the tool (fetching a single quiz's metadata) and provides an explicit when-not-to-use scenario by stating questions are excluded and pointing to hivelearn_list_quiz_questions. It does not mention using hivelearn_list_quizzes for fetching multiple quizzes, but the 'one quiz' phrasing implies the distinction.

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

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

No annotations are present, so the description carries the transparency burden. It discloses key behaviors: only published resources are returned, 404 is returned for unpublished or cross-community resources, and the author is joined. This is substantial, though it omits details like authentication requirements or return format.

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 action and resource. It includes essential behavioral notes without unnecessary verbosity.

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 getter with one parameter and no output schema, the description covers the core functionality, success condition, and error condition. It is not exhaustive but sufficient for an agent to select and invoke the tool correctly. The absence of alternative guidance is a minor gap.

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%, and the description only restates 'by id' without adding meaning beyond the schema's own id property name and UUID format. The description does not compensate for the lack of parameter 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 identifies the tool as fetching a single published resource by ID, with the joined author. It distinguishes from siblings by specifying 'published' and the 404 condition, making its scope 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 implies usage for retrieving one resource by ID, and the 404 behavior clarifies when not to use it. However, it does not explicitly mention alternatives like list_resources for listing, nor does it state when to prefer this over other getters.

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 must carry the full burden. It only says 'with full metadata'—a vague term—and does not disclose auth requirements, rate limits, error cases, or what exactly 'full metadata' includes. The read-only nature is implied by 'fetch' but not explicitly stated.

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 short sentences with no filler. The first sentence states the action, the second provides a crucial alternative. Every word earns its place.

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

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

For a simple get-by-id tool, the description covers the essential purpose and the main alternative. However, without an output schema, the phrase 'full metadata' is vague and could leave the agent uncertain about the return structure. Still, the tool is simple and the description is largely 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 only parameter is 'id', and the description clarifies it refers to the track id via 'by id.' However, with 0% schema coverage, the description adds no extra detail about the parameter (e.g., format, constraints), though the schema already provides format and pattern. It is adequate but not compensating beyond the obvious.

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?

'Fetch one learning track by id with full metadata' uses a specific verb and resource, clearly distinguishing it from related tools. The reference to 'ordered curriculum' differentiates it from hivelearn_list_track_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 explicitly states when to use this tool (to fetch a single track's metadata) and provides a direct alternative: 'For its ordered curriculum use hivelearn_list_track_courses.' This is clear and actionable.

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 transparency. It discloses what data is returned but does not state whether the operation is read-only, what authentication or permissions are required, or any rate limits or error behavior. For a retrieval tool, this lack of explicit non-mutating guarantee is a 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 compact and front-loaded with the tool's purpose, followed by a usage suggestion. Every sentence contributes: the first defines what the tool returns, the second gives context. There is no repetition of schema constraints.

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?

With no output schema and no annotations, the description needs to explain the return shape and safety profile. It lists key output fields but not the full response structure (e.g., whether it's a list object with pagination metadata). It also does not state the read-only nature of the operation. However, the tool is relatively simple, and the schema covers pagination parameters. This is adequate-but-gapped.

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 documents limit and offset with descriptions (67% coverage), but track_id has no description. The description implies track_id is the track of interest ('through a learning track') but does not add detail about UUID format or behavior. It also does not explain pagination behavior beyond what schema already says.

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 this tool returns per-member progress through a learning track, enumerating specific fields (enrolled_at, started_at, completed_at, total_courses, completed counts). This differentiates it from the sibling get_course_progress, which is presumably per-course. The verb ('get') is implied by the tool name, and the resource (track progress) is explicit.

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 a clear use case ('cohort/track analytics'), which helps the agent decide when to invoke it. However, it does not explicitly name alternative tools for course-level progress, such as hivelearn_get_course_progress, nor does it state when not to use it. This is clear context without 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 burden of behavioral disclosure. It does mention return fields and optional scoping, but it omits behavioral traits such as default ordering, whether results are sorted by issued_at, and whether status is filterable. The phrase 'issued certificates' is slightly ambiguous regarding whether it includes all statuses or only 'issued' status. This is a minimum-viable disclosure but lacks depth.

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, information-dense sentence that front-loads the verb and resource, lists filter options, and enumerates return fields. Every word earns its place with no redundancy or filler.

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

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

For a list tool with optional filters and no output schema, the description provides essential information: return fields, filter semantics, and the list operation itself. It does not explicitly state pagination defaults or how filters combine, but the schema covers pagination and the description's 'optionally scoped' implies flexibility. Overall, it is nearly complete for common usage scenarios.

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 meaning to the user_id and course_id parameters by explaining they scope results ('scoped to one user or one course'). The limit and offset parameters are not mentioned, but the schema descriptions for them are explicit (page size, rows to skip). With 50% schema coverage, the description compensates for the filter parameters, which are the core semantic aspects.

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 issued certificates') and the resource, with optional scoping to user or course. It also enumerates return fields, distinguishing it from siblings like get_certificate and verify_certificate. The verb + resource + scoping is specific and unambiguous.

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 tool: listing certificates, optionally filtered by one user or one course. It does not explicitly name alternatives or exclusions, but the scoping language effectively communicates the intended use cases. Sibling names imply alternatives (e.g., get_certificate for single retrieval), but the description itself stops short of explicit when-not 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 must carry the behavioral burden. It discloses that this tool returns a flat list (as opposed to a nested tree) and that it optionally filters by module, which are important behavioral traits. However, it does not mention pagination, ordering, or error behavior. Still, for a simple list operation, this is reasonably transparent and exceeds the minimum.

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 exceptionally concise: two short sentences that front-load the action and include a valuable alternative pointer. Every word earns its place, with no redundancy or filler.

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

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

For a tool with only two parameters, no annotations, and no output schema, the description provides sufficient context. It tells the agent what the tool does, how to optionally restrict scope, and when to use a sibling tool instead. This covers the essential selection and invocation requirements for a list operation.

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

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

Schema description coverage is 0%, so the description should compensate. It adds meaning to module_id by stating 'optionally filtered to one module,' which clarifies its role as a filter. However, course_id is only implicit from the tool name and required status, and no extra detail is given about parameter formats or relationships. This is modest compensation for the lack of schema description.

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

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

The description clearly states the tool's function: 'List lessons of a course, optionally filtered to one module.' It uses a specific verb ('List'), identifies the resource (lessons of a course), and clarifies scope (optionally filtered to one module). It also distinguishes itself from sibling tool hivelearn_get_course_structure by pointing out that the latter provides a nested 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 provides explicit usage guidance: 'Use hivelearn_get_course_structure for a nested tree' implicitly tells the agent to use this tool for flat lists. It also mentions the optional module filtering, clarifying when that parameter is relevant. This gives clear context for choosing between this and a key alternative.

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

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

With no annotations provided, the description carries the full burden. It discloses the ordering behavior ('ordered by sort_order') and the domain relationship ('Modules group lessons; a course has 1..N modules'), which are useful. However, it does not mention safety (e.g., read-only), auth requirements, errors, or pagination, leaving some behavioral traits undisclosed.

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

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

The description is extremely concise: two sentences, no fluff, and front-loaded with the purpose. Every phrase adds value—'List modules (sections)' defines what, 'ordered by sort_order' adds behavioral detail, and the second sentence provides domain context. This is an exemplary concise structure.

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 one-parameter list tool, the description covers the core purpose and ordering. However, there is no output schema, so the description should explain return values but does not mention what the module list contains (e.g., fields, pagination). The parameter semantics are also weak. The description provides enough for basic understanding but leaves gaps in expected output and parameter handling.

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, and the description does not explain the course_id parameter beyond saying 'of a course.' It does not name course_id or add any detail about its format, purpose, or constraints, leaving the agent to infer the mapping from the tool name alone. This is insufficient compensation for the schema's lack of description.

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

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

The description clearly states the tool's function: 'List modules (sections) of a course, ordered by sort_order.' It uses a specific verb ('List') and resource ('modules (sections) of a course'), and adds the ordering detail. It also distinguishes from siblings like list_course_lessons by explaining that modules group lessons, making the purpose unambiguous.

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 stating that modules group lessons and a course has 1..N modules, which helps an agent understand when to use this tool. However, it does not explicitly mention alternatives (e.g., list_course_lessons, get_course_structure) or provide when-not-to-use guidance, so the usage context is only implied rather than fully explicit.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It transparently lists the returned fields and explains the status parameter's behavioral implications (published = visible to learners), which is valuable. It does not mention pagination behavior or error handling, but the schema covers the parameters and the read-only nature is implied by '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 two sentences long, with the main action front-loaded. It includes the most important filter behavior and return fields without any filler or redundant content, 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?

Despite lacking an output schema and annotations, the description covers the key return fields, the authentication scope, and the behavioral significance of the status filter. For a straightforward list operation, this is sufficient for an agent to understand what the tool does and what to expect, with only minor omissions like sorting or error scenarios that are not critical for selection.

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 fully describes all three parameters, so the baseline is 3. The description adds semantic value by explaining that status=published returns only courses visible to learners, which enriches the enum meaning. It also indicates the return fields, which provides additional context beyond the schema.

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

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

The description clearly states the tool lists courses in the authenticated community, with a specific verb ('List') and resource ('courses'). It distinguishes from sibling tools like get_course (single course) and create_course by implying a collection operation. The status clarification adds specificity to the purpose.

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 tool (listing courses) and explains the status=published filter's effect on learner visibility, guiding selection. However, it does not explicitly mention alternatives like get_course for single-course retrieval or exclusions, so it falls short of full 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, the description carries the burden. It discloses the returned fields and scoping options, which adds value. However, it does not explicitly state that this is a read-only operation (implied by 'List'), nor does it explain filter combination behavior or pagination semantics beyond what the schema provides. It is adequate but not rich.

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

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

Two concise sentences: the first states the action and optional scoping, the second lists return fields. No wasted words, front-loaded with the core 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?

For a simple list tool, the description covers purpose, scoping, and return values. Pagination parameters are already in the schema. It lacks explicit distinction from get_enrollment and filter semantics, but overall is fairly complete given the tool's simplicity.

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 describes limit and offset, but not user_id and course_id. The description adds meaning by saying 'scoped to one course or one user', mapping these parameters to filters. However, it does not clarify whether both can be used together or how they interact. Overall, it partially compensates for the 50% 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 uses a specific verb ('List') plus resource ('course enrollments') and clearly indicates optional scoping by course or user. This distinguishes it from siblings like get_enrollment (single enrollment) and create_enrollment.

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: it's the list endpoint for enrollments, with optional filters. However, it does not explicitly mention alternatives such as get_enrollment for a single enrollment, nor does it state when not to use this tool. The context is clear but lacks 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, the description carries the burden of behavioral disclosure. It specifies the return fields and implies that `upcoming=false` (or default) may return all events including past ones, which is useful. However, it does not disclose default sorting, pagination behavior beyond schema, or whether location/meeting_url are conditionally present. This is moderate transparency.

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 primary purpose, and every sentence adds value. It avoids fluff and includes the essential return fields in a compact, readable format.

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 list tool, the description covers the essential output fields, which is crucial given no output schema. It also gives a practical usage tip for the common calendar UI scenario. However, it omits details like default event range (past+future) and ordering, which are relevant for a list operation. These gaps prevent a 5.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds a recommendation for the `upcoming` parameter ('recommended for calendar UI') that provides context beyond the schema's dry 'If true, only events with start_date in the future.' It does not add new semantics for `limit` or `offset`, so overall it only slightly enhances parameter understanding.

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 opens with 'List community events,' which is a specific verb+resource that clearly states the tool's function. It also enumerates the returned fields (id, title, dates, event_type, location/meeting_url, attendee counts), distinguishing it from single-event retrieval like hivelearn_get_event.

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 contextual guidance for the `upcoming` parameter ('Use upcoming=true for future events only (recommended for calendar UI)'), which helps the agent choose parameter values. However, it does not explicitly mention alternatives such as hivelearn_get_event for retrieving a single event, so it falls short of a full when-to-use versus alternatives guideline.

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

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

With no annotations, the description carries the burden of disclosing behavior. It lists return fields and mentions the authenticated community, implying authentication. It does not explicitly state that the operation is read-only or describe any side effects, pagination behavior, or access restrictions beyond the auth context, so it is adequate but not rich.

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

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

Two sentences, both front-loaded with key information: the action in the first, and use case in the second. No fluff or repetition; every word contributes to understanding the tool's 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?

For a simple list tool with three optional parameters, the description is fairly complete. It specifies the return fields, which is helpful since there is no output schema. It does not mention pagination details, but these are implied by the limit/offset parameters in the schema. The core behavior and output are well-covered.

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 already provides full descriptions for all parameters (100% coverage), so the baseline is 3. The description adds meaningful use-case guidance for the 'role' parameter ('to find admins/owners'), which goes beyond the schema's simple 'Restrict to a single role' and helps the agent understand when to apply it.

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 uses a specific verb ('List') and resource ('members of the authenticated community'), clearly distinguishing it from other member-related tools like get_member or update_member. The scope is immediately clear, and it identifies the action and target unambiguously.

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?

Provides clear context: lists members of the authenticated community. It also gives a specific use case ('Filter by role to find admins/owners'), which helps with role-based queries. However, it does not explicitly mention alternatives or when to prefer this over other list tools, but the resource scope makes it obvious.

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

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

No annotations are provided, so the description carries the full burden. It discloses the ordering behavior ('newest first'), the filter capability, and the return fields (id, title, content, category, author, reply counts, timestamps). This goes beyond the schema and gives the agent a clear picture of what to expect. It does not mention error conditions or authentication, but for a read-only list operation, this is sufficient.

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 main action ('List community feed posts, newest first'), followed by a concise usage hint and output specification. No unnecessary words or repetition of schema details.

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 list tool with three parameters, the description is complete: it states the purpose, ordering, filter option, and return fields. The absence of an output schema is compensated by explicitly listing the returned fields. The limit/offset parameters are adequately described in the schema, so no additional explanation 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?

The input schema covers all three parameters with descriptions, so baseline is 3. The description adds value by providing an example value for the category parameter ('announcements') and indicates that the output includes the category field. This helps clarify the semantics of the category filter beyond just 'filter by category slug'.

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 lists community feed posts with a specific ordering ('newest first'). It distinguishes itself from sibling tools like hivelearn_get_post (singular) and other list tools by naming the resource as 'community feed posts'. The verb 'List' is specific and the additional details about filtering and return fields add 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 clear context for when to use the tool: for listing posts. It includes explicit usage guidance for the category parameter with an example ('announcements'). It does not explicitly exclude alternatives (like using hivelearn_get_post for a single post), but the plural 'posts' and the listing nature make the intended use obvious.

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?

The description discloses that results are ordered by sort_order and that each question includes options and the correct answer. Since no annotations are provided, this adds valuable behavioral context beyond the basic 'list' operation, though it doesn't mention pagination or error conditions.

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 concise sentences, front-loaded with the action, and contains no unnecessary information. Every clause adds value: the listing action, the quiz scope, the ordering, and the payload content.

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

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

For a simple list operation with one parameter and no output schema, the description covers the essential return details: questions are ordered and include options and correct answer. It is sufficient for an agent to understand what the tool will return and how it behaves.

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 a single quiz_id parameter with 0% description coverage in the schema. The description refers to 'for a quiz' which gives some context, but it doesn't explicitly name the parameter or explain its required format. The schema pattern provides format details. The description adds minimal compensating 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 identifies the tool as listing all questions for a quiz, with a specific verb and resource. It also distinguishes itself from sibling tools like list_quizzes (which lists quizzes) and get_quiz (which retrieves a quiz), as well as create/update quiz question 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 usage is implied: when you need all questions for a specific quiz. However, there is no explicit guidance on when to use this over alternatives like get_quiz or list_quizzes, and no exclusions or prerequisites 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, the description carries the burden. It mentions optional scoping and the domain constraint that each quiz belongs to a lesson, adding useful context. However, it doesn't clarify whether course_id and lesson_id can be combined or what the output format looks like.

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, front-loaded with the action. The second sentence adds useful domain relationship without fluff. Every word earns its place.

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

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

The tool is simple, but the description doesn't specify behavior when both course_id and lesson_id are provided, and there's no output schema. It's adequate for basic list usage but has gaps around filter interaction and return value details.

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

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

Schema coverage is only 50%; course_id and lesson_id lack descriptions. The description's mention of 'scoped to a course or a specific lesson' gives meaning to these otherwise undocumented parameters, while limit/offset are already described 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 opens with 'List quizzes', a specific verb+resource pair. It clearly distinguishes from sibling tools like hivelearn_get_quiz (singular) and hivelearn_list_quiz_questions (different resource).

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 states the tool lists quizzes and can be optionally scoped to a course or lesson, giving clear context for when to use it. It does not explicitly mention alternatives, but the scoping context implies when this list tool is appropriate.

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 transparency burden. It discloses that only published resources are listed, the sort order, the returned fields, and that the internal storage key is never exposed—a meaningful privacy/security behavior. It could add auth requirements or pagination caveats, but the provided details are substantial.

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?

Three sentences cover purpose, ordering, filters, return fields, and a note about hidden internal keys. Every sentence contributes, with no 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 no output schema, the description covers return fields, ordering, filters, and a security behavior. It could mention explicit pagination behavior or how drafts are handled, but it is sufficiently complete for agent 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 60% (limit, offset, folder_id have descriptions). The description reinforces that folder_id, resource_type, and access_level are filters but doesn't add constraints beyond schema enums. It adds some value but does not fully compensate for the two undocumented 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 action ('List'), the resource type ('published resources'), the scope ('in the authenticated community'), and ordering ('newest first'). It also names the filtering dimensions, which distinguishes it from sibling list tools like list_courses or list_events.

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 usage context: use this to list published resources, optionally filtered by folder_id, resource_type, or access_level. It does not explicitly list alternative tools or when-not conditions, but the scope is unambiguous relative to the sibling set.

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

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

With no annotations, the description carries the full burden. It explicitly indicates a read operation ('Return'), describes the response content (courses with sort_order, is_required, unlock_after_course_id), and adds semantic context by explaining 'unlock_after_course_id' as prerequisite gating. This goes beyond just saying 'list courses' and gives useful behavioral insight.

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?

A single, front-loaded sentence with zero filler. Every word adds value, efficiently conveying purpose, returned fields, and a key concept (prerequisite gating) 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 tool's simplicity (one parameter, no output schema, no annotations), the description adequately covers what the tool does and what it returns. It stops short of detailing return format (e.g., array vs. object) or error behavior, but for a straightforward list tool, this is sufficient.

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

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

Schema description coverage is 0%, so the description must compensate. Though it doesn't explicitly state 'track_id is the track's identifier,' the phrase 'of a track' makes the single parameter's role clear. It adds conceptual meaning about the parameter's use that the bare UUID schema does not provide.

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 uses a specific verb ('Return') and identifies the exact resource ('ordered curriculum of a track') with clarifying details about the included fields (sort_order, is_required, unlock_after_course_id). This clearly distinguishes it from siblings like list_courses (all courses) and get_track (track details).

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 phrase 'ordered curriculum of a track' provides clear context for when to use this tool: whenever you need the courses belonging to a specific track in order. It doesn't explicitly mention alternatives or when-not-to-use, but the track-specific wording makes the use case obvious.

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

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

With no annotations, the description carries the transparency burden. It discloses that only published tracks are listed, the sort order, and the exact fields returned. It does not mention potential side effects or auth, but as a read-only listing operation these are not expected; the provided details are sufficient.

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 concise sentences, front-loaded with the action and resource, and includes only essential details (ordering and return fields). 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?

For a simple list operation with complete schema coverage and no output schema, the description sufficiently covers purpose, scope, ordering, and return fields. It is complete given the low complexity and available metadata.

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% for both parameters (limit and offset each have descriptions and defaults). The tool description adds no parameter-level information, so the baseline score of 3 applies, with no additional value from the 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 a specific verb ('List'), resource ('published learning tracks'), context ('in the community'), ordering ('newest first'), and return fields. It distinguishes from siblings like hivelearn_get_track and hivelearn_list_track_courses by specifying scope and output.

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 gives clear context by defining tracks as 'curated course sequences' and specifying 'published' and 'newest first', which indicates when this tool is appropriate. However, it does not explicitly mention alternatives or exclusion scenarios, leaving some inference to the agent.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It transparently states the side effect (set is_published=true), the validation condition (all lessons have content_url), and the failure behavior (returns error if any lesson is empty), plus a recovery path. It omits details like idempotency or success response, but core behavior is well-covered.

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

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

The description is extremely concise: two sentences, front-loaded with the main action, and the second sentence adds a concrete error-handling instruction. Every word earns its place with no 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 one-parameter tool with no output schema, the description covers the action, validation, error condition, and diagnostic fallback. It does not mention the success response format, but that is not critical for this simple publish operation. Overall, the description provides sufficient context for effective tool selection and 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 schema has one required 'id' field with 0% description coverage. The description does not explicitly elaborate on the parameter, but the phrase 'Publish a course' makes it clear that 'id' refers to the course identifier. With only one parameter and a straightforward schema, this is adequate but does not add extra meaning beyond inference.

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: publishing a course by setting is_published=true after validating that every lesson has a content_url. This distinguishes it from create/update tools and specifies its unique role in the course lifecycle.

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 when to use this tool (when lessons are ready to be published) and explicitly directs the user to hivelearn_get_course_structure on error, providing a diagnostic alternative. However, it does not explicitly state prerequisites or situations where this tool should be avoided.

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

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

No annotations are provided, so the description carries the full burden. It discloses important behavioral traits: atomicity and the requirement that all lessons be present exactly once. However, it does not mention return values, error behavior on validation failure, or any side effects, which would improve transparency 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?

Two sentences, front-loaded with the primary action, with no redundant words. Every clause adds value: atomicity, full order, and the exact-once constraint.

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 two-parameter tool with no output schema, the description covers the core purpose, parameter semantics, and a key behavioral constraint. It could mention the requirement that the module exists or what happens if validation fails, but the description is largely complete for the tool's complexity.

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

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

Schema coverage is 0%, so the description must compensate. It effectively explains lesson_ids as 'the desired final order' and stresses that every lesson must appear exactly once, adding meaning beyond the raw UUID array type. module_id is self-evident from the context and tool name.

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

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

The description states a specific verb ('Set') and resource ('full lesson order within a module'), clearly distinguishing this from sibling tools like update_module. It also adds scope by specifying 'one atomic call' and the exact-once requirement for lessons.

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 clearly tells when to use this tool: to set the full lesson order for a module. It provides a key usage constraint (every lesson must appear exactly once) but does not explicitly mention alternatives or when not to use it, though the unique name and sibling context make this less critical.

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 adds valuable behavioral context by disclosing the consequence of setting status to 'revoked' (verify endpoint rejects). However, it does not mention other effects like reversibility, permissions, or the behavior for 'active' or 'expired' statuses, leaving some gaps.

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

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

The description is a single, focused sentence that immediately states the action and then adds a critical behavioral hint. It is free of fluff and perfectly front-loaded.

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 tool with only two parameters and no output schema or annotations, the description covers the essential purpose and a key behavioral consequence. It omits some potential details (e.g., return values or error handling), but given the low complexity, the description is largely sufficient.

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

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

Schema description coverage is 0%, so the description must compensate for parameter meaning. It explains the 'revoked' status specifically, which adds semantic value beyond the bare enum. However, it does not elaborate on 'id' or the other statuses ('active', 'expired'), relying on the schema's self-explanatory names.

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 the resource 'certificate status', specifying exactly what the tool does. It is unambiguous and distinguishable from sibling tools like verify_certificate or create_certificate.

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 contextual guidance by explaining that 'revoked' should be used to invalidate and that the verify endpoint will reject such certificates. While it doesn't explicitly mention alternatives or when-not-to-use scenarios, the tool's purpose is niche enough that this context is sufficient.

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 disclosing behavioral traits. It does add a useful warning that setting is_published=true bypasses validation, which is important behavioral context. However, it does not describe other side effects, permissions required, whether edits are reversible, or what the response contains. For a mutation tool, this is only partially transparent.

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, consisting of two sentences. It front-loads the core purpose ('Edit course metadata') and then provides a critical usage warning. Every word earns its place, with no filler 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 the tool's complexity (14 parameters, no output schema, no annotations), the description is insufficient. It explains the publishing caveat but leaves out essential operational details such as which fields can be updated, how partial updates work, return values, or required permissions. A minimal description for a 14-parameter mutation tool should provide more 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 0%, meaning none of the 14 parameters are explained in the schema. The description does not compensate; it only mentions is_published in the context of a usage warning, not to explain the parameter's meaning. It adds no semantic value to any parameter, making this a significant 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 function: 'Edit course metadata.' The verb 'edit' and resource 'course metadata' are specific and unambiguous. It also distinguishes itself from the sibling hivelearn_publish_course by explicitly warning against using this tool for publishing.

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 a when-not-to-use guideline: 'Use hivelearn_publish_course instead of setting is_published=true directly — publish runs validation.' This names the alternative tool and the specific condition under which to avoid this tool. It clearly conveys that this tool is for metadata edits, not publication actions.

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

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

With no annotations, the description carries the full burden. It usefully discloses that cancellation preserves history and that null max_attendees lifts the cap. But it omits other important behaviors such as partial vs full update semantics, permission requirements, or response format, which are critical 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?

Two concise sentences with no filler. The most important usage notes are front-loaded, making the description 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?

For a high-complexity mutation tool with 15 parameters, no annotations, and no output schema, this description is far too brief. It doesn't cover common edit scenarios, validation rules, or what the tool returns, leaving the agent under-informed.

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, and only two of fifteen parameters (is_cancelled and max_attendees) receive semantic explanation. The remaining parameters like description_json, description_format, and timezone are not clarified, leaving a large 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 uses a specific verb-resource pair ('Edit an event') that clearly distinguishes it from create, get, and list tools. It is not tautological and immediately conveys the tool's 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 context is clear: this tool modifies existing events. It also gives conditional guidance for cancellation and lifting attendee caps. However, it doesn't explicitly contrast with create_event or other edit tools, so it lacks 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 full responsibility for behavioral disclosure. It only states the action (edit metadata/move between modules) without explaining whether updates are partial or full, whether moving to a module has side effects (e.g., reordering), any permission requirements, or what the response contains. This is a mutation tool with zero annotation coverage; the description adds minimal behavioral context beyond the raw 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?

Two sentences, first states purpose, second gives a targeted alternative. No filler or redundant phrases.

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?

With 13 parameters, no output schema, and no annotations, this description is too terse to be fully operational. It lacks details on return values, update semantics, permission requirements, and the specific metadata fields involved. The pointer to the content tool is useful, but it doesn't compensate for the missing 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 coverage is 0%, and the description must compensate for 13 undocumented parameters, but it only hints at module_id via 'move it between modules' and groups everything else under 'metadata.' It does not explain the meaning of content_type, content_json, is_preview, sort_order, etc., leaving the agent to guess the role of most fields.

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?

Description opens with "Edit lesson metadata or move it between modules," a specific verb+resource statement. It explicitly distinguishes from sibling hivelearn_update_lesson_content by scoping content body edits to that tool.

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 directly tells the agent when to use this tool vs the content-specific sibling: 'To edit ONLY the content body use hivelearn_update_lesson_content (narrower scope, safer).' This provides explicit use case differentiation. It doesn't discuss alternatives like create_lesson or reorder_module_lessons, but the main conflict is resolved.

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

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

With no annotations, the description carries the burden and discloses key behaviors: it replaces content fields, preserves title/sort_order/module assignment, and is described as safe for bulk writes. However, it does not mention potential side effects, permissions, or response behavior, leaving some gaps beyond the stated scope.

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, front-loaded with the core action, with no redundant phrasing. Every sentence adds value: the first states scope, the second adds contrast and exclusions.

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 an 8-parameter tool with no schema descriptions, output schema, or annotations, the description provides useful scope and exclusions but is incomplete. It ignores two parameters (content_json, content_format) and does not mention return values or error conditions, leaving important operational details uncovered.

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 explicitly maps many schema params (content_url, content_type, description, duration_seconds, thumbnail_url) but omits content_json and content_format entirely, which are present in the schema. This partial coverage adds some meaning but leaves two parameters 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 tool's function: 'Replace just the content of a lesson' with specific fields (URL, content_type, optional description/duration/thumbnail). It explicitly differentiates from the sibling 'update_lesson' by being narrower, which establishes a distinct purpose.

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?

Provides explicit usage context: 'Narrower than update_lesson' signals when to use this instead of the general update tool, and 'safe for bulk agent writes' adds an appropriate use case. The exclusion 'Does NOT change title, sort_order, or module assignment' clarifies what the tool won't do, guiding 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 burden of behavioral disclosure. It explains accepted role values and the semantics of is_banned, but does not disclose potential side effects, permissions needed, or error behavior. This is adequate but not fully transparent.

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

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

The description is three concise sentences, each adding essential information without redundancy. It is front-loaded with the verb and resource, making it immediately clear what the tool does.

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 three-parameter mutation tool with no output schema, the description covers all parameters, constraints, and an important exclusion. It does not detail return values or error conditions, but these are not necessarily required given the tool's simplicity.

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

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

Schema coverage is only 33% (only id has a description), so the description compensates well by explaining the role enum values and the meaning of is_banned. It also clarifies the requirement that at least one field beyond id must be provided, which is not evident from the schema alone.

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 function with a specific verb ('Change') and resource ('a member's role or ban status'). It distinguishes itself from sibling update tools by specifying the exact member attributes it modifies.

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 use, including the constraint that owner role cannot be assigned via the API and that at least one field is required. It does not explicitly name alternative tools, but the purpose and constraints sufficiently guide usage.

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 disclosing behavioral traits, but it only states the actions without mentioning side effects, partial vs full updates, or auth requirements. It does not explain how sort_order changes affect other modules or whether the update is idempotent.

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 primary purpose, and includes a helpful pointer to an alternative tool. Every word earns its place with no 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 simple mutation tool with no output schema, the description provides enough to attempt invocation: required id, optional title/sort_order. However, it omits behavioral details like return format, error conditions, and effects on module ordering, making it minimally viable but not 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 meaning by mapping 'rename' to the title parameter and 'reorder' to the sort_order parameter, which helps beyond the raw schema. However, it does not explain the id parameter semantics or clarify sort_order behavior (e.g., 0-based, conflicts) since schema coverage is 0%.

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 renames or reorders a module, using specific verbs and resource. It also distinguishes itself from the sibling tool hivelearn_reorder_module_lessons by explicitly directing lesson reordering elsewhere.

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 an explicit alternative for lesson reordering ('To reorder lessons within a module use hivelearn_reorder_module_lessons'), which clarifies when not to use this tool. It does not mention prerequisites or other alternatives, but the scoping is clear.

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 must carry the full burden. It discloses the partial update behavior and explains is_pinned semantics, which is valuable. However, it does not mention permissions, reversibility, or what happens to unspecified fields, leaving gaps 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?

Two short sentences, no filler, and each sentence provides actionable guidance. The description is appropriately sized and front-loaded with the primary action.

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?

With no output schema and no annotations, the description leaves many important aspects unaddressed: parameter formats, the relationship between content and content_json, and the effects of the update operation. It covers only one parameter's semantics and the basic edit intent, so it is not complete for a tool with six parameters and nested objects.

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%, and the description only elaborates on is_pinned. The fields content, category, content_json, content_format, and even id are not explained. The 'only include fields you want to change' hint gives a clue that all properties are optional fields to update, but this is insufficient for six parameters with nested objects.

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 uses a specific verb and resource ('Edit an existing post'), clearly distinguishing this tool from create_post and get_post siblings. The scope is unambiguous, and the pinned behavior adds further specificity.

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 statement 'Only include fields you want to change' gives clear partial-update guidance and clarifies how to use the tool correctly. However, it does not explicitly mention when to use this over other update tools, but the tool name makes that obvious enough.

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

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

No annotations are present, so the description carries the full behavioral burden. It discloses one constraint (lesson moves are not supported) but omits mutation semantics, whether updates are partial or full replacement, permission requirements, or side effects.

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, front-loaded with the action, and the second sentence adds a useful exception rather than filler. Every sentence earns its place 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 7-parameter mutation tool with no annotations and no output schema, the description is too thin. It provides only a single constraint and no context about return values, update behavior, or parameter details, leaving the agent under-informed.

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% and the description only says 'quiz settings' without naming any of the seven editable fields. Parameter meanings are left entirely to bare schema property names, offering essentially no added semantic 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?

Description uses a specific verb 'Edit' and resource 'quiz settings,' making the tool's purpose clear and distinguishing it from sibling tools like update_quiz_question. It doesn't enumerate which settings are editable, but the action and target are unambiguous.

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 one explicit when-not: cannot move a quiz to a different lesson, and offers an alternative ('create a new one instead'). It does not explicitly contrast with other update tools, but the general edit context is implied.

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 does reveal an important constraint about the interdependence of question_type, options, and correct_answer. However, it does not mention whether updates are partial or full, how validation errors are handled, or what the response format is, leaving significant behavioral unknowns.

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 concise sentences with no filler. The key action ('Edit a question') is front-loaded, and the extra note about question_type is directly relevant. Every sentence earns its place.

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

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

Given 8 parameters, no output schema, and no annotations, the description is notably incomplete. It addresses only one behavioral relationship and omits essential context such as required fields, return value, error handling, and the effect of updating individual fields. A typical agent would have many unanswered questions when invoking this 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?

The input schema has 0% description coverage, so the description must compensate. It adds meaningful semantics by explaining that changing question_type requires resupplying options and/or correct_answer, which clarifies those parameter relationships. But it does not explain id, points, sort_order, explanation, or question_text, leaving most parameters underdocumented.

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 opens with 'Edit a question,' which is a clear verb+resource pairing. While 'question' is somewhat generic, the tool name and sibling context (e.g., create_quiz_question, update_quiz) make it clear this targets quiz questions, distinguishing it from other update 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 a key usage hint ('Changing question_type will require resupplying options or correct_answer...'), which implies when to use this tool along with a special caution. However, it does not explicitly state when to choose this over alternatives like create_quiz_question or list_quiz_questions, nor does it mention any 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, the description carries the burden and does disclose partial-update behavior and the meaning of two flags. However, it omits other behavioral traits such as required permissions, idempotency, whether omitted fields remain unchanged, and error/return 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 two sentences, front-loaded with the primary verb and object, and every sentence adds functional guidance. No filler 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 14 parameters, zero annotations, and no output schema, the description covers only partial updates and two flag behaviors. It lacks information on return values, authorization, validation rules, and the full set of editable fields, making it incomplete for a tool of this complexity.

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

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

Schema description coverage is 0%, so the description must compensate, but it only explains semantics for is_featured and is_published out of 14 parameters. The remaining parameters (title, price, tags, access_level, etc.) rely on names, types, and enums from the schema, which is insufficient for selecting and providing correct values.

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 opens with 'Edit a resource,' a specific verb+resource statement that clearly identifies the tool's function. It does not explicitly distinguish it from sibling update_* tools, but the resource focus plus sibling create/get/list_resource tools provide sufficient context.

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 usage context: 'Only include fields you want to change' signals a partial-update pattern, and the guidance on is_featured and is_published gives concrete instructions. However, it does not explicitly mention when not to use this tool or name alternatives like create_resource.

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

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

With no annotations, the description carries the burden. It discloses partial-update semantics and a publishing trigger, which is useful. However, it does not mention what happens when is_published=false, whether changes are reversible, or any error/validation behavior, leaving gaps in behavioral transparency.

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 every sentence adds value. It is appropriately sized and free of 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?

The tool has 8 parameters, no output schema, and no annotations. The description covers the essentials of usage but lacks information on return values, error handling, and unpublishing behavior. It is minimally viable but not comprehensive for the tool's complexity.

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

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

Schema description coverage is 0%, so the description must compensate. It only explains one parameter (is_published) and gives a general rule that applies to all optional fields, but it does not clarify the meaning of id, title, description, difficulty_level, etc. This is insufficient for an 8-parameter tool with no 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 begins with 'Edit a learning track,' which clearly states the verb (edit) and resource (learning track), distinguishing it from create, get, and list operations. It also mentions a specific behavioral aspect (is_published) that ties to the update purpose.

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