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 does disclose a key behavioral trait: results are shown only 'when visible to the token,' which is useful context about access scoping. However, it does not describe the return format, whether grades are aggregated or per-assignment, or any rate-limit/auth implications.

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, compact sentence that front-loads the resource ('grade/score metadata') and adds the key scoping condition. Every word earns its place; 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?

For a parameterless read tool, the description is mostly sufficient: an agent knows what it returns and the visibility caveat. However, with no output schema and no annotations, the agent is left guessing about the response shape (e.g., per-course objects, numeric scores, letter grades), which is a meaningful gap for a tool whose entire purpose is returning data.

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 there is no schema to compensate for. The description adds meaning by specifying the scope ('by enrolled course') and the visibility condition, which is the only semantic content an agent needs for a parameterless call.

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 resource ('grade/score metadata by enrolled course') and a condition ('when visible to the token'), which clearly distinguishes it from sibling tools like list_courses or list_assignments. It lacks a strong verb like 'retrieve' or 'list', but the noun phrase is specific enough to convey 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 a read-only lookup of grade metadata, and the sibling names (list_courses, list_assignments, upcoming, overdue) suggest it is the grade-specific counterpart. However, it does not explicitly state when to prefer this tool over alternatives or mention any exclusions, leaving the agent to infer usage context.

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

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

No annotations are provided, so the description carries the full behavioral burden. It discloses the key trait — this is a read operation ('Read') — but says nothing about what 'assignment metadata' includes, how the bucket filter behaves, pagination, or authorization requirements. The core safety profile is clear, but other behavioral details are absent.

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 the core action first and no filler. It is efficient, though the compressed jargon 'Canvas assignment bucket' trades clarity for brevity, and 'numeric' adds marginal value given course_id is typed as a string in the schema.

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 2-parameter tool with no output schema and no annotations, the description defines the basic call adequately. But it leaves bucket semantics undefined, doesn't characterize the return contents beyond 'metadata,' and fails to position the tool against upcoming/overdue, which may be overlapping views. Serviceable with clear gaps.

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

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

Schema coverage is 0%, so the description must compensate. It adds real meaning for course_id (identifies the course being read, expected to be numeric). But bucket is glossed as 'Canvas assignment bucket,' which largely restates the parameter name without explaining accepted values or filtering behavior — a significant gap given the schema provides no 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?

States a specific verb ('Read'), resource ('assignment metadata'), and scope ('one numeric course ID'), which distinguishes it from siblings like list_courses (course list) and upcoming/overdue (time-filtered views). However, 'optional Canvas assignment bucket' is cryptic — an agent cannot tell what a bucket is or what values it takes, so part of the intended behavior remains unclear.

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 'Read one numeric course ID's assignment metadata' implies when to use it — when you need assignments for a single course — and the sibling tool names provide some contrast. But the description never explicitly says when not to use it or names alternatives such as upcoming/overdue for time-filtered assignment views, so the guidance is implied rather than stated.

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

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

No annotations are provided, so the description carries the behavioral burden. It signals a read-only listing action and clarifies scope with 'including unstarred courses', but it does not mention authentication, pagination, or response shape. For a simple list tool this is acceptable, but there are still unstated behavioral 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 a single, focused sentence with no filler. It front-loads the action and resource, then adds scope clarification. Every part of the 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?

For a zero-parameter list command with no output schema, the description is largely self-sufficient. It identifies the resource and scope. However, it could be more complete by noting whether pagination or authentication details apply, though these are not likely to block correct invocation 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 tool has zero parameterschers, so the baseline is 4. The description does not need to explain parameters; it adds useful context about what the returned list contains, which is sufficient given the empty input 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 uses a specific verb ('List'), names the exact resource ('courses'), and defines scope ('active, pending, and favorite') with the clarification that unstarred courses are included. This differentiates it clearly from sibling tools like list_assignments or 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 implies the tool should be used when an agent needs the full set of courses, but it does not explicitly state when to choose it over alternatives or mention sibling tools. The context is clear enough for a zero-parameter listing tool, but the guidance is only 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?

There are no annotations, so the description carries the full burden. It reveals the scope ('across all enrolled courses') but does not clarify expected behavior such as whether it aggregates by course, whether it is read-only, or what the return structure looks like. For a simple lookup tool this is partially acceptable, but the absence of any behavioral detail 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 a single sentence with no wasted words, and the most important qualifiers ('unsubmitted', 'past-due', 'across all enrolled courses') are front-loaded. It is appropriately concise, though adding a verb would make it slightly more polished.

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 conveys the essential information needed to decide whether to invoke it: the result is unsubmitted past-due assignments across all enrolled courses. It is simple enough that additional details like return format are not critical, though an explicit action verb 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?

The tool has zero parameters, and the schema is fully covered by that absence. The description adds useful context by indicating the operation uses the implicit set of enrolled courses and requires no user-supplied arguments. This meets the baseline for a parameterless tool.

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: unsubmitted past-due assignments across all enrolled courses, which differentiates it from siblings like upcoming and list_assignments. It lacks an explicit verb such as 'list' or 'get', but the noun phrase and tool name make the intended action reasonably clear.

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

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

No explicit guidance is given about when to use this tool versus alternatives. The scope wording implies it is for overdue work rather than upcoming or all assignments, but the description does not state that or mention the sibling tools.

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

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

No annotations exist, so the description carries the full burden of explaining behavior. It states the filtering criteria but does not disclose whether results include due dates, how assignments are organized, whether only the count or full details are returned, or how the days parameter affects the range.

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

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

The description is extremely concise and communicates the core selection criteria in a single sentence. It is front-loaded and easy to scan, though it omits parameter and return-format details that could have been included without much length.

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 annotationsasi and no output schema, the description must be self-sufficientam. It fails to explain the 'days' parameter, result format, default behavior, or how this tool compares to 'list_assignments' and 'overdue'. A simple listing tool still needs at least parameter and return-value 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?

There is one parameter, 'days', with 0% schema description coverage. The description mentions a 1–31 day range but never explicitly maps this to the 'days' parameter or explains how changing it alters results. The parameter semantics are therefore largely left to 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 identifies a specific resource—unsubmitted assignments—and a clear time window (next 1–31 days) across enrolled courses. It is distinguishable from siblings such as overdue and list_assignments, though it lacks an explicit verb like 'list' or 'get'.

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 (for future-due unsubmitted assignments) but provides no explicit guidance on when to choose it over list_assignments, overdue, or grades. It does not name alternatives or exclusion conditions.

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