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 states the tool 'retrieves' content, indicating a read-only operation, and lists what will be returned. However, it does not clarify the response structure, error behavior, or any prerequisites like authentication. It adds some behavioral context beyond the name, but not enough to fully illuminate side effects or failure modes.

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 begins with the action verb and then lists the retrieved fields in a compact enumerative style. It contains no filler or repetition, and the most important qualifier ('specific vacancy') appears early. It could arguably be split for readability, but it is still efficient 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?

This is a simple one-parameter read tool with no output schema or annotations. The description effectively communicates the tool's purpose and the main content retrieved, but it does not specify the response format (e.g., JSON object vs. plain text) or any error conditions. For a straightforward get-by-ID tool, the missing output details are a notable gap, though not critical given the tool's low 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 100% and the parameter description clearly explains the accepted formats (UUID or slug with an example). The tool description adds no additional semantics about the parameter beyond referring to a 'specific vacancy', so it does not improve on the schema. Baseline 3 applies because the schema fully documents the 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 identifies a specific verb ('Retrieve') and a precise resource ('a specific vacancy'), and then enumerates the distinct content it returns (description markdown, compensation, requirements, ATS source, verified application link). This clearly separates it from siblings like search_jobs or list_companies, which operate at a different scope, so an agent can tell when to use this 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 gives no explicit guidance on when to use this tool versus any of the siblings. It implies the need for a job ID or slug through the parameter, but it does not state conditions such as 'use when you need full details for a known job' or 'do not use for searching'. There are no alternative recommendations 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, the description must carry behavioral disclosure, and it does: it specifies the data source (verified, active postings), the aggregation granularity (25th, median, 75th percentile), and the output style (ASCII distribution visualization). It does not discuss edge cases like missing data or invalid locations, so it does not earn full marks.

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 entire description is one dense, information-rich sentence with no filler. Every element contributes to the tool's purpose, source, output, and presentation style.

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?

There is no output schema, so the description's mention of percentile benchmarks and ASCII visualization meaningfully covers the return format. The parameter schema covers inputs completely. The only gap is absence of behavior for empty or insufficient compensation 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?

Schema description coverage is 100%, so the schema already documents role_title, location, and currency. The description adds no parameter-specific meaning beyond the schema, which is acceptable but not extra credit territory.

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 names a specific action and resource: it aggregates verified compensation into percentile benchmarks. It clearly differentiates this tool from the job-listing, company-listing, and resume-matching siblings by focusing on market-level compensation 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 compensation-market research but never states when to use it versus alternatives or provides any exclusion guidance. The intended use is reasonably inferable from the name and content, but explicit routing is absent.

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 does not state that the operation is read-only, what the response contains, how 'actively recruiting' is determined, or how results are organized despite 'categorized' suggesting some grouping. The description offers purpose but not meaningful 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 a single sentence with no filler and leads with the core discovery purpose. It is appropriately brief, though 'Discover' and 'categorized' are slightly imprecise for an API 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?

With no output schema and no annotations, the description leaves key operational details undocumented, such as the return format, pagination behavior, how open vacancy volume is represented, and whether the result is a flat list or grouped categories. The schema covers inputs, but the overall description is not complete enough for an agent to fully predict the tool's 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 100%, so the schema already documents all parameters and defaults. The description adds some framing by tying 'ATS provider' and 'open vacancy volume' to the tool's purpose, which aligns with ats_provider and min_active_jobs, but it does not add material meaning 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 identifies the resource ('companies') and a specific discovery goal ('actively recruiting tech talent'), and differentiates the tool by naming ATS provider and open vacancy volume as the organizing dimensions. It is not as crisp as 'List companies...' but the intent is clear and separable from the job-oriented sibling 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 implies this tool is for finding companies rather than jobs, which helps with sibling selection, but it gives no explicit when-to-use guidance, no exclusions, and no comparison to alternatives like search_jobs or get_job_details. Usage is inferred 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?

With no annotations provided, the description carries the full burden, and it performs well by disclosing the exact scoring weights (skills 50%, title 25%, remote 15%, salary 10%) — genuinely useful behavioral detail that reveals how inputs influence ranking. However, it doesn't disclose behavior on no-match results, staleness of listings, or what the ranked output structure 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?

A single compact sentence that front-loads the primary purpose and packs the scoring rubric efficiently without fluff. Slightly dense in the middle (the weight list), but every element 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 scoring rubric covers skills, title, remote, and salary, but years_of_experience and currency are params that get no explanation in the description and no output schema compensates. The 'ranked recommendations' return format is never described, leaving a moderate gap for a scoring tool with no annotations and no 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?

Schema coverage is 100%, setting a baseline of 3. The description adds value beyond the schema by explaining how title, remote, and salary parameters contribute proportionally to the score, and by defining the role of skills as the dominant factor. This links parameters to their ranking impact, which the schema alone doesn't convey.

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 (matches), resource (candidate resume skills vs live verified job listings), and the deliverable (ranked recommendations) with explicit scoring weights. The multi-factor scoring rubric makes it clearly distinct from the generic search_jobs sibling, so an agent can tell them apart without opening schemas.

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 guidance on when to choose this tool over search_jobs, which is the most likely confused sibling. The description implies resume-based matching but never states exclusions, prerequisites, or selection criteria — e.g., 'use this when you have extracted skills from a resume; use search_jobs for general keyword queries.'

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 does disclose a meaningful behavior—triggering immediate HTTP prober verification—which helps the agent anticipate a side effect. However, it omits other behavioral context such as authentication needs, idempotency, or what happens after verification.

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 wasted words. The core purpose is front-loaded, and the behavioral note about the HTTP prober is placed immediately after, making it easy to scan.

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 3-parameter report action with a complete schema and no output schema, the description covers the key context: what is being reported and the immediate consequence. It is slightly incomplete because it does not mention usage boundaries or post-call expectations, but the overall package is adequate for correct 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 100%, and each parameter already has a meaningful description, including the reason enum and the job_id_or_slug format. The tool description adds no additional parameter-level meaning, 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 identifies the action ('Report'), the target ('dead link or expired job posting'), and a distinctive side effect ('Triggers immediate HTTP prober verification'). It is easily distinguishable from sibling tools like search_jobs or get_job_details, which are read-only searches.

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 job link is dead or a posting is expired. However, it does not explicitly state when not to use it or mention alternatives, leaving the agent to infer the boundary from sibling names.

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 adds one meaningful fact: results are pre-filtered to 'active, verified' postings, which is genuinely useful and not derivable from the schema. However, it omits pagination behavior (page/limit exist in the schema but response pagination is unmentioned), rate limits, and any note about the response format. Partial coverage of behavior 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?

A single sentence with zero wasted words. The core action and resource are front-loaded, the ATS sources are named compactly, and the filtering capability is stated last. Every clause earns its place; nothing is redundant or padded.

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?

This is a complex tool with 10 parameters, multi-source aggregation, no output schema, and no annotations, yet the description is minimal. It does not explain that results are paginated, what the response structure looks like (critical given there is no output schema), whether salary sorting requires a matching currency, or how filters combine. For a tool of this complexity, the description falls well short of what an agent needs to use it 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% with rich parameter descriptions and concrete examples ('Senior TypeScript Engineer', 'stripe', 'Berlin'). The description itself adds nothing about parameters beyond the phrase 'multi-parameter filtering,' so the baseline of 3 applies since the schema is doing the heavy lifting. No compensation is needed because the schema already documents all parameters well.

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 ('Search') and resource ('active, verified tech job postings'), and names the four aggregated ATS sources (Greenhouse, Lever, Ashby, Workable). This clearly differentiates it from siblings like get_job_details (single-job detail lookup), list_companies, and match_resume_to_jobs (resume-driven matching), so an agent can tell this is the general multi-source job search.

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 offers no guidance on when to use this tool versus its siblings. It never mentions that get_job_details should follow a search result to fetch full details, that list_companies provides the company_slug values, or that match_resume_to_jobs is the alternative when a resume is available. The agent is left to infer routing entirely from the schema and tool names.

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