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 return fields (title, body, posted date, attachments) and implies a read-only operation via 'Get', but it does not explicitly state permissions, side-effect-free behavior, or other traits like sorting or filters.

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 and return values, and contains no filler. Every sentence adds value, 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?

The tool is simple (one parameter), an output schema exists, and the description covers the core function, return values, and use cases. Minor details like sorting or limits are not mentioned, but they are not essential given the tool's simplicity and the presence of an output schema.

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

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

The schema has 100% coverage for the single parameter courseId, including a description and example. The tool description adds no additional parameter meaning, so the baseline of 3 applies.

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

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

The description explicitly states 'Get announcements/news posted by instructors for a course' and lists return fields, providing a specific verb, resource, and scope. This clearly distinguishes it from sibling tools like get_assignments and get_grades.

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 concrete example queries ('Any new announcements?', 'What did the professor post?') that indicate clear usage context. It does not explicitly mention when not to use the tool or name alternatives, but the examples effectively guide 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?

No annotations are provided, so the description carries the full burden. It discloses the returned data types and the user-centric nature ('your submission status', 'your feedback'), which is meaningful behavioral context. It does not explicitly state side effects, but as a read-only getter, the verb 'get' and the listed outputs make the behavior clear enough.

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 the second sentence provides concrete usage examples. Every word contributes value, with no redundancy or unnecessary detail.

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, output schema present), the description fully covers the purpose, the returned fields, and typical use cases. The output schema handles the return format details, and the description adequately positions the tool among siblings.

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 100% coverage with a detailed description for courseId, explaining it comes from list_courses and providing an example. The tool description adds no additional parameter meaning beyond the schema, so the baseline of 3 applies.

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

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

The description clearly states the tool gets assignments (dropbox folders) for a course and enumerates the specific information returned (due date, instructions, attached problem sheets, submission status, submitted files, feedback/score). This specific verb+resource+scope distinguishes it from sibling tools like get_grades or get_upcoming.

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 example user questions such as 'Have I submitted Assignment 5?' and 'What feedback did I get?' which clearly indicate when to use this tool. However, it does not explicitly mention alternatives or when not to use it, so it falls short of 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 of behavioral disclosure. It states the output format ('nested table of contents') but does not mention any edge behaviors such as whether only published content is included, pagination, or access requirements. The description is adequate but not rich; however, the presence of an output schema lessens the need to detail return 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 three sentences, front-loaded with the main purpose, and uses space efficiently by including usage examples and a pointer to a sibling tool. Every sentence adds value, with no filler 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?

The tool is simple (one parameter, no nested objects) and has an output schema, so return values are covered elsewhere. The description provides strong usage context and links to get_topic_file. The only gap is not explicitly distinguishing from get_course_outline, which might also relate to course content, but this is a minor omission given the overall clarity.

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 only parameter (courseId) is fully described in the schema as the 'course org unit (ou) ID from list_courses'. The description adds no additional parameter details beyond referencing 'a course'. This matches the baseline of 3 for well-covered schemas.

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: 'List the content modules and materials (lectures, slides, files, links) for a course as a nested table of contents.' This clearly distinguishes it from siblings by focusing on content modules and materials, not announcements, grades, or assignments. It also names the complementary tool get_topic_file, further differentiating functions.

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?

Explicit usage guidance is provided: 'Use this as the primary source for course activity questions like...' followed by concrete examples. It also states what to do after using this tool ('After identifying relevant topics, call get_topic_file to inspect PDFs or PowerPoints'). This tells the agent exactly when and how to use the tool versus alternatives.

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

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

No annotations exist, so the description carries the disclosure burden. It conveys read-only behavior ('Fetch... return its full text'), the content structure ('week-by-week schedule'), and a fallback behavior when no outline link exists. It does not mention permissions or error conditions, but for a fetch tool the essential behavior is 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 a bit lengthy but front-loaded with the core purpose. The examples and workflow instructions earn their place; however, the block of quoted questions could be condensed without losing meaning. Overall, it is concise enough for the value it provides.

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 only one parameter and an output schema present, the description need not detail return values. It thoroughly covers when to use, how to chain with sibling tools, and a fallback case, making it fully complete for its intended 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 100%, with courseId already described as 'The course org unit (ou) ID from list_courses, e.g. 123456'. The description adds no extra parameter context beyond the schema, so a baseline of 3 is appropriate.

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

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

The description clearly states it 'Fetch the official syllabus and return its full text,' using a specific verb and resource. It also distinguishes itself from siblings by framing its role in chronological orientation and referencing get_content and get_topic_file as follow-ups.

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 lists when to use the tool ('Use to answer...', 'WHENEVER user asks about content with chronological language') and provides alternatives ('If no outline link exists, check get_content'). It also instructs to call get_content & get_topic_file afterward, making the intended workflow very 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?

With no annotations provided, the description carries the full burden. It discloses supported file types, the rendering behavior, and the configured Drive folder restriction. However, it doesn't mention what happens with unsupported file types, required permissions, or any 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?

The description is two concise sentences, front-loaded with the main action and supported file types. 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?

With full parameter documentation in the schema and an output schema present, the description is largely complete. It clearly references the workflow with search_drive_files and covers access restrictions. It could mention error cases or unsupported file behavior, but overall it's well-rounded.

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 descriptions fully cover both parameters (fileId and pages) with examples and a default. The tool description adds no additional parameter semantics, but with 100% schema coverage, the baseline of 3 is appropriate.

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

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

The description clearly states the tool downloads and inspects files returned by search_drive_files, rendering PDFs, PowerPoints, Google Docs, and Slides as page images. This specific verb+resource combination distinguishes it from sibling tools like search_drive_files.

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 references search_drive_files as the source of the fileId, establishing its role in the workflow. It doesn't name alternative tools or exclusions, but the context is clear enough for an agent to know when this 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 full burden of behavioral disclosure. It transparently states the tool returns grade items with displayed grade, points, weight, and feedback, which conveys its read-only, non-destructive nature for a get-type tool. It does not mention side effects or auth, but none are expected for a read operation.

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

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

The description is two sentences long, front-loaded with the core purpose, and efficiently includes example use cases. Every sentence 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?

The tool is simple with one well-documented parameter and an output schema, so the description adequately covers the necessary context. It fully answers what the tool does and when to use it, and the output schema handles 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?

The schema has 100% coverage for the single parameter courseId, with a clear description including source (list_courses) and an example. The tool description adds no additional parameter information, so it meets the baseline for high schema coverage but does not exceed 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 the specific verb 'Get' and clearly identifies the resource 'your grades for a course', listing the return fields. It is distinct from sibling tools like list_courses and get_assignments, so it unambiguously differentiates its 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 explicit usage context by giving example questions ('What are my grades?', 'How did I do on the midterm?') that signal when to invoke the tool. It does not explicitly name alternatives or when-not-to-use, but the context is clear enough given the sibling list.

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 page-to-slide mapping, the ability to fetch specific pages via the pages parameter, and a 30-per-call cap with a note in the response. This is substantial behavioral context, though it doesn't mention auth requirements or error handling, so a slight deduction is warranted.

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 with front-loaded purpose, a precise usage example, and no filler. Every sentence contributes value. The structure is clean and easily 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?

Given the presence of an output schema, the description adequately covers purpose, usage, and behavior. It explains the relationship to get_content, page selection, and the default cap. It lacks explicit exclusions (e.g., 'not for non-lecture files'), but the examples and context make the tool's scope clear enough.

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 baseline is 3. The description restates parameter origins (topicId from get_content, courseId from list_courses) and provides examples, but these largely mirror the existing schema descriptions. It adds minimal new semantic meaning beyond reinforcing the page-number convention.

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 downloads a lecture file (PDF/PowerPoint) from course content and returns each page/slide as an image, including diagrams. It distinguishes itself from siblings like get_content (list content) and get_drive_file (Drive files) by specifying 'from course content' and the topicId linkage.

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?

Explicit guidance is given: 'Use after get_content' with concrete example queries like 'Summarize lesson 2' and 'What is the diagram on slide 4?'. This establishes both prerequisite and use cases, effectively guiding the agent when to select this tool over alternatives.

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

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

With no annotations, the description carries the full burden. It discloses the scope of results (calendar events, assignments, quizzes, exams) and an important limitation (excludes lecture/tutorial materials), adding context beyond the name. It doesn't mention ordering or read-only behavior, but for a retrieval tool this is a reasonable level of 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 followed by usage examples and alternatives. It is concise, structured, and contains no 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 has only two parameters, full schema descriptions, and an output schema. The description covers the primary use cases, exclusions, and a complementary sibling tool. It is complete for an agent 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?

Schema description coverage is 100%, so the baseline is 3. The description does not add parameter-specific details beyond the schema, though it implicitly references courseId via 'for a course'. The schema already documents parameters effectively.

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

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

The description uses the specific verb 'Get' and clearly identifies the resource: 'upcoming calendar events and due dates (assignments, quizzes, exams) for a course'. It also explicitly distinguishes itself from siblings by stating it does not list lecture/tutorial materials and references get_content.

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

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

The description provides concrete example questions ('What is due this week?', 'When is the next deadline?') that indicate when to use the tool. It also gives an explicit exclusion ('does not list lecture/tutorial materials') and suggests an alternative tool ('pair it with get_content'), offering complete 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?

No annotations provided, but the description discloses the key behavioral aspect: returns names and ou IDs used as courseId by other tools. This gives enough transparency for a simple read-only list operation, though it omits potential details like ordering or filtering 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?

Two sentences, front-loaded with action and resource, no redundant text. Each clause adds value: what it returns and how the output is used by other tools.

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 parameterless list tool with an output schema, the description is complete. It covers purpose, return content, integration with sibling tools, and the question it answers. No gaps are apparent.

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 tool has zero parameters, so the description carries no param burden. Baseline 4 applies; no additional parameter explanation 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?

Clear verb 'List' with specific resource 'courses you are enrolled in on Waterloo LEARN'. It explicitly states what is returned (course names and ou IDs) and differentiates from sibling content-focused tools by being the course-level listing.

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 an explicit usage example: 'Use to answer: "What courses am I taking?"'. It implies typical use as a starting point since other tools consume the returned courseId, though it doesn't explicitly name alternatives or exclusions.

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

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

With no annotations provided, the description carries the full burden. It discloses key behaviors: recursive search, use of a configured folder, and no-query fallback. It does not mention auth or rate limits, but for a search operation these are not critical, and the description adds context beyond what the name/schema imply.

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 main purpose front-loaded, examples integrated naturally, and a clear pointer to the next step. Every sentence contributes value—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 2-parameter search tool with an output schema, the description covers all essential aspects: what it searches, how to query, default behavior, and how to inspect results. The output schema provides return structure, so the description need not elaborate further.

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% (both parameters have descriptions), so the baseline is 3. The description enriches the 'query' parameter meaning by providing examples and noting the no-query behavior, making the parameter's purpose and usage clearer than 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 uses a specific verb ('recursively search') and clearly identifies the resource ('configured Google Drive folder') and scope ('past quizzes, tests, midterms, exams, practice material, and solutions'). It distinguishes from the sibling tool get_drive_file by explicitly directing users to it for inspecting results.

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 concrete usage examples ('CS 135 quiz', 'MATH 136 midterm') and explains behavior with no query ('returns assessment-like files'). It tells users to use get_drive_file for inspection, but does not explicitly contrast with other alternatives or describe scenarios where this tool should not be used.

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