JobYap Job Search

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

Annotations already mark this as read-only and non-destructive. The description adds that the output is markdown including specific fields and top comments with a citable URL, providing useful behavioral expectations beyond the safety flags. It does not contradict any annotation.

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 action and resource, then enumerates the return contents. Every phrase adds value and no words are wasted, 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?

For a simple fetch tool with one parameter, an output schema, and read-only annotations, the description covers the purpose, input provenance, and output composition sufficiently. It gives an agent everything needed to decide when to use it and what to expect, without requiring the output schema to explain return values.

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

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

The sole parameter 'id' is fully documented in the schema as 'A result id returned by search.' The description reiterates this context but adds no new format, constraints, or examples. Since schema coverage is 100%, a baseline 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 the specific verb 'Retrieve' and identifies the resource as 'the full JobYap document for a search result id,' listing the contents (markdown of posting plus comments, citable URL). This clearly distinguishes it from siblings like get_job or get_job_comments, which focus on narrower subsets.

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 states the context: given a search result id, you get the full document. However, it does not explicitly mention when to prefer this over get_job or get_job_comments, nor any exclusions, so it lacks direct comparison to alternatives.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the read-only nature is covered. The description adds valuable behavioral context beyond the annotations: it lists the return fields and reveals that expired jobs still return data with apply_url set to null. This goes beyond the structured metadata.

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 immediately states the tool's purpose and enumerates the returned fields, while the second sentence adds an important edge case. 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?

Even without an output schema, the description provides a thorough account of the return values (markdown description, salary, locations, employment types, apply URL, dates, discussion URL). It also covers the expired-job scenario, making it complete for a single-fetch read tool with one parameter.

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's description of the 'id' parameter is comprehensive—it accepts a numeric ID, a full job URL, or a slug—achieving 100% schema description coverage. The tool description itself does not add new parameter-level information, so 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 opens with the specific verb 'Fetch' and the resource 'one job posting in full,' then enumerates the exact content fields (markdown description, salary ranges, locations, etc.). This clearly distinguishes it from sibling tools like get_job_comments or get_job_stats, which focus on comments or statistics.

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 when to use this tool—when you need the complete details of a single job posting—and highlights that it works for expired jobs, which is a useful contextual cue. However, it does not explicitly name alternative tools or state when not to use this tool, falling short of a full 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?

Annotations already declare readOnlyHint and destructiveHint, and the description adds valuable behavioral context: chronological ordering, parent_id threading, like counts, user-generated content, and that threads persist after job expiration. This goes well beyond the safety metadata.

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, consisting of three focused sentences. Each sentence contributes meaningfully—purpose, data characteristics, and lifecycle behavior—with no 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?

Given the simple one-parameter schema and existing annotations, the description provides sufficient context about the response nature (chronological, threaded, like counts). It lacks details such as pagination or explicit return structure, but that is not essential 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 input schema already fully documents the single parameter 'id' with a clear explanation of accepted formats. The description does not add any additional parameter-level detail, so the baseline score 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 verb 'fetch' and the resource 'community discussion thread for a job', explicitly distinguishing it from sibling tools like get_job and get_job_stats. It also specifies key details (chronological, threaded, like counts) that make the 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 conveys when to use this tool—when you need a job's comment thread—but does not explicitly mention alternatives or exclusionary conditions. This is clear context without being a full when/when-not guide.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, establishing the safety profile. The description adds the specific stats returned, which is helpful, but it does not disclose potential caveats like whether the stats are real-time, cached, or scoped to the current user/workspace. It does not contradict the annotations.

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

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

The description is a single, well-structured sentence that front-loads the core purpose ('Aggregate stats') followed by a concise list of the specific statistics. Every word earns its place, with no redundant or filler 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?

Given the tool has no parameters, a clear read-only annotation, and no output schema, the description specifies exactly which three aggregate values are returned. This is sufficient for an agent to understand what the tool does and when to invoke it. The absence of an output schema is compensated by the explicit enumeration of stats.

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 are no parameter semantics to clarify. The schema fully covers the absence of parameters, and the description adds no parameter-related information, but none is needed. The baseline for 0-param tools is 4.

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 aggregate stats (companies tracked, active job count, newest posting date), which is a specific verb+resource combination. It distinguishes itself from sibling tools like get_job and list_companies by focusing on summary aggregates rather than individual records.

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 any exclusions, prerequisites, or context where a sibling tool (e.g., search_jobs, list_companies) would be more appropriate. The usage context is entirely implied by the tool name and title.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds value by specifying the scope ('every company JobYap tracks') and the output content ('active job count'), which is useful behavioral context beyond the annotations. No contradiction.

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 concise sentence that front-loads the core purpose and adds a practical usage note. Every word earns its place, with no wasted 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 read-only list tool with no parameters and no output schema, the description is sufficient. It specifies what is listed and the output includes active job count, and it explains how to use the results. It could mention ordering or limitations, but these are not essential for this level of 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?

The tool has zero parameters, so there is nothing for the description to explain about parameters. Per the rubric, 0 params is a baseline 4. The description does not need to add 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 clearly states it lists every company JobYap tracks with its active job count, using the specific verb 'List' and resource 'companies'. It distinguishes from siblings like search_jobs and search_locations by focusing on companies rather than jobs or locations.

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 advises using the exact returned company names as filters for search_jobs, providing practical usage context. It implies when this tool is useful (to get company names for filtering) but does not explicitly delineate when to use this vs. alternatives, or provide 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?

Beyond annotations (readOnlyHint=true, destructiveHint=false), the description adds meaningful behavioral details: it matches job titles and falls back to 'significant keywords' when the full phrase is low-yield. It also discloses that results include citable URLs intended for fetch. This enriches the agent's understanding of how the tool behaves without contradicting any annotations.

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, each earning its place: the purpose, the matching behavior, and the alternative tool. It is front-loaded with the primary action and contains 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?

With one parameter, a single required field, no nested objects, and an output schema plus annotations, the description covers the key aspects: what it searches, how the query works, what it returns, and when to use a sibling instead. Nothing essential is missing 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?

The schema provides only the type and length constraints for 'query' with 0% description coverage. The description compensates fully by explaining that the query is natural-language, matches job titles, and falls back to keywords. This gives the agent a clear model of how the parameter affects the search.

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: 'Search JobYap job postings by natural-language query.' It also specifies the matching strategy (job titles, fallback to keywords) and output (result ids, titles, citable URLs). It distinguishes itself from sibling search_jobs by noting the structural filtering alternative, 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 explicitly tells when to use this tool versus the structured sibling: 'For structured filtering (location, company, remote, freshness) prefer search_jobs.' This provides a clear alternative condition, and the natural-language query description implies the intended use case. The mention of 'for use with fetch' also guides the follow-up workflow, offering actionable 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?

Beyond the annotations (readOnlyHint, destructiveHint), the description discloses critical behavior: query matches titles only (case-insensitive substring), unmarked work_mode means 'unstated not onsite', results are active listings with newest-first default, and pagination requires passing next_cursor with all other arguments identical. No contradiction with annotations.

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 dense but organized: purpose first, then filtering nuances, then sort, then pagination. No wasted words, though it is longer than average. Each sentence earns its place for a 10-parameter 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 complexity (10 params, no output schema), the description covers the most important behavioral quirks: matching rules, identifier sources, work_mode semantics, sorting, and pagination. It also references sibling tools for lookups. The remaining params (has_comments, include_total, posted_within) are self-explanatory from schema. This is complete for invocation purposes.

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 that schema descriptions don't fully convey: query semantics, company/location source constraints, work_mode nuance, sort meaning, and cursor usage. It compensates for missing schema descriptions on work_mode and page_size, and enriches the other 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 opens with a specific verb and resource: 'Search JobYap's aggregated job postings with structured filters.' This clearly distinguishes it from siblings like get_job (single job) or search (likely generic), while also implying a structured search vs. free-form.

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 clear context: companies must come from list_companies and locations from search_locations, which implicitly tells the agent to call those tools first. It also advises trying synonyms or shorter tokens when results are thin. However, it doesn't explicitly state when *not* to use this tool versus the 'search' 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?

The description adds behavioral context beyond the annotations: it resolves to specific identifier formats and only returns places present in current job data. This is useful and does not contradict the read-only hint.

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 that pack essential information: purpose, examples, output format, and a limitation. It is well-structured and 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 2-parameter tool with no output schema, the description covers the key aspects: input, output identifier format, and data scope. It could add default/limit behavior, but overall it is sufficiently complete for invocation.

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

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

The description clarifies the meaning of 'query' (free-text place name) and gives examples, complementing the schema. However, the 'limit' parameter is not mentioned in the description, and with 50% schema coverage, the description only partially compensates for the missing 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 states the tool's purpose: resolving free-text place names to location identifiers accepted by search_jobs. It provides concrete examples and explicitly distinguishes from siblings by naming search_jobs.

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 (before search_jobs, to get location identifiers) and notes the limitation that only places in current job data are returned. However, it does not explicitly contrast with alternatives or state when not to use it.

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