get_statsCurrent AI Dev Jobs index statistics: total active jobs, companies hiring, median salary, new jobs today / this week, and the top 10 trending tags across jobs posted in the last 7 days. This is free index data and does not create a payment handoff.
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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 explicitly states that there is no payment handoff, which is a relevant side-effect disclosure for a stats tool. However, it does not mention whether the operation is read-only, return format, freshness, or rate limits. The single safety statement adds some value but leaves room for more 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 extremely concise: two sentences, front-loaded with the main content, and no wasted words. The second sentence adds a crucial behavioral note about payment handoff, making every sentence earn 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?
Given no parameters and no output schema, the description stands as the primary source of information about what the tool returns. It lists specific data points, making the tool's functionality clear. It could be improved by explicitly stating the tool's aggregate/read-only nature, but for a zero-param tool, the description is sufficiently complete.
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 burden. The description adds no parameter-specific semantics, but none are needed. Per the rubric, a zero-parameter tool receives a baseline of 4, and the description's enumeration of output fields is sufficient given no parameters exist.
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 what the tool returns: aggregate index statistics such as total active jobs, companies hiring, median salary, new jobs, and trending tags. While it lacks an explicit verb like 'retrieves' or 'returns', the resource and scope are specific enough to distinguish it from sibling tools like get_salary_data or get_trending_companies.
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 by listing the available content, but it does not explicitly state when to use this tool versus siblings like get_salary_data or get_trending_companies. The note about being 'free index data' and not creating a payment handoff gives a useful context signal but does not provide clear alternative selection guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clear, distinct purposes (e.g., search_jobs vs get_job vs get_similar_jobs). The main ambiguity is between match_jobs and analyze_application_readiness, both of which assess candidate-job fit, though one ranks multiple jobs and the other evaluates readiness for a specific role. The application flow steps are well-separated.
All tool names consistently follow the snake_case verb_noun pattern (e.g., get_company, list_companies, apply_to_job). Even longer names like analyze_application_readiness and compile_job_specific_resume adhere to this convention, with no mixed casing or inconsistent verb styles.
With 20 tools, the server is on the heavier side for a job board, though the breadth of features (search, company info, salary, application, interview tracking, and product sales) partially justifies the count. Some redundancy exists (e.g., get_trending_companies vs list_companies, get_stats vs get_salary_data), making the set feel slightly bloated.
The toolset covers core job search and application workflows, but there is no way to list or track submitted applications, view application status, or withdraw an application. Post-application features are limited to interview outcomes, leaving obvious lifecycle gaps for a candidate-facing job platform.