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

No annotations are provided, so the description carries the full burden. It mentions the metrics returned but doesn't disclose behavior like default date ranges (though the schema covers defaults), whether data is read-only, or any rate limits. The description adds some value by listing metrics but doesn't cover behavioral traits like aggregation granularity or data freshness.

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

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

The description is a single sentence that front-loads the main purpose and lists key metrics. It's concise and structured well, though it could be slightly more explicit about usage context.

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 read-only analytics tool with two optional parameters and no output schema, the description is adequate but not complete. It doesn't explain what 'period-over-period comparisons' means in terms of output format, nor does it clarify how this differs from get_dashboard_stats. The lack of annotations and output schema means the description should carry more weight.

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

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

Schema description coverage is 100%, so the schema already documents both parameters (startDate, endDate) with defaults. The description doesn't add meaning beyond what the schema provides, but the baseline of 3 applies since the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: retrieving an analytics overview with specific metrics (application trends, interview rates, offer rates, period-over-period comparisons). The verb 'get' and resource 'analytics overview' are specific. It doesn't explicitly differentiate from siblings like get_dashboard_stats, but the metrics listed make it distinguishable.

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 analytics queries but doesn't explicitly state when to use this tool versus get_dashboard_stats or list_applications. The context is clear enough for an agent to infer it's for high-level analytics, but no explicit exclusions or alternatives 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 behavioral disclosure burden. It communicates a read-style 'Get' operation and the returned content categories, but does not state whether AI analysis is precomputed or generated on demand, nor any permissions or error behavior. This is acceptable but not richly 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?

One sentence, front-loaded with the main action and resource, then specific content categories. There is no filler or redundancy; every phrase adds 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 one-parameter get-by-ID tool with no output schema, the description covers the main response areas (AI analysis, tailored resume, metadata). It is sufficient for an agent to know what it will get back, though it leaves minor ambiguity about exact response structure or error cases.

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% and the single 'id' parameter is already documented as an Application ID (UUID). The description only reinforces the 'specific application' notion, adding no new format or syntax detail. Baseline 3 is appropriate because the schema handles parameter meaning.

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 ('Get full details for a specific job application') and enumerates what is included: AI analysis, tailored resume, and metadata. This clearly distinguishes it from the sibling list_applications (list vs. single record) and get_resume (single component vs. full 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 description conveys clear usage context: call when you need details about one specific application by ID, rather than a list or dashboard stats. It does not explicitly name alternatives or exclusions, but the 'specific' qualifier gives sufficient routing guidance for this simple tool.

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 should disclose behavioral traits. It clarifies the tool is read-only (get) and provides a summary, but it doesn't mention whether responses are aggregated across all users or scoped to a context, or whether the metrics are computed in real-time. The description 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?

The description is a single, clear sentence, front-loading the verb and resource, then listing the specific metrics. No wasted words, and it is appropriately structured for a parameter-less tool.

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 has no parameters, no output schema, and no annotations, the description does a good job of explaining what it returns. It lacks a note on the response format (e.g., JSON structure) and whether it supports date filtering, but for a simple stats snapshot it 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?

The tool has 0 parameters, so the description doesn't need to explain parameters. It does explain that it returns aggregate statistics, adding meaning beyond the empty schema, which fully covers the 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 it retrieves dashboard statistics and enumerates the specific metrics (total applications, active applications, interviews, offers, AI match score), using a specific verb 'Get'. It distinguishes itself from siblings like list_applications (which likely returns detailed records) by focusing on aggregate stats, though it doesn't explicitly name a sibling.

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 a quick overview of key metrics, distinct from detailed tooling, but it does not explicitly state when to prefer this over get_analytics_overview or list_applications. It lacks exclusions or alternative 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 of behavioral disclosure. It states the output format (JSON Resume) and content scope, which is useful. However, it doesn't disclose whether this is a read-only operation, whether it returns the full resume or a subset, or any caching/versioning behavior. For a simple getter with no annotations, 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 sentences, no wasted words. The format (JSON Resume) is front-loaded, followed by a compact list of content areas. 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?

For a parameterless getter with no output schema, the description is reasonably complete: it names the format and the content areas. It could mention whether the resume is the user's own vs. a candidate's, but the phrase 'the user's base resume' already implies ownership. The lack of an output schema means the description's content list partially compensates, though an agent might still wonder about exact field names.

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 parameter semantics burden on the description. The description adds value by explaining what the returned data contains, which is the closest analog to parameter semantics for a parameterless tool. Baseline 4 for 0 params 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's purpose: retrieving the user's base resume in JSON Resume format, and enumerates the content areas (work history, education, skills, projects, contact info). This is a specific verb+resource combination that distinguishes it from the sibling tools, which all concern applications, dashboard stats, or analytics.

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 is for fetching the user's own base resume, which is distinct from the application/analytics-focused siblings. It doesn't explicitly state when not to use it or name alternatives, but the context signals (0 params, no siblings with similar names) make the usage context clear 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?

With no annotations, the description carries the full behavioral disclosure burden. It usefully discloses pagination and the return fields, but does not mention default limits, ordering, or potential side effects—though it is a list operation and thus implicitly read-only.

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

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

Two sentences with no filler: the first gives the action and resource, the second the return behavior. Everything present earns its place, and the key scoping phrase is 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 list tool with three optional parameters and no output schema, this is reasonably complete: purpose, pagination, and return fields are covered. It could mention sorting or default page size, but the schema documents the parameters adequately.

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 of 3 applies. The description does not add parameter-level insights beyond what the schema already provides; at most it complements the status filter by listing return 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?

The description states a specific verb and resource ('List job applications from Ascend') and names the returned fields, making it clearly a list operation. It distinguishes itself from siblings like get_application, which implies fetching a single application, and get_dashboard_stats/get_analytics_overview, which are analytics-oriented.

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 this is the tool for listing applications and notes pagination, placing it in a clear usage context. It does not explicitly state when not to use it or name alternatives, but the sibling names make those alternatives inferable.

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