Market-level hiring numbers measured this morning across every board we read: employers hiring, open roles, how many went up in the last 24 hours, how many have been open over 90 days and over a year, how many boards have gone quiet, and the most in-demand technologies.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 discloses freshness ('measured this morning'), scope ('across every board we read'), and the exact data points, which strongly implies a read-only aggregate query. It does not explicitly state that it is read-only, nor does it mention output format, caching, or rate considerations, leaving some room for inference.
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, dense sentence with the core purpose front-loaded and the specific metrics delivered via a clear colon-led list. Every element contributes meaningfully, though the sentence is long; the length is justified by the number of metrics covered.
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 no-parameter, no-output-schema aggregate stats tool, the description is largely complete: it names the metrics, the freshness of the data, and the coverage ('every board we read'). It stops short of describing the return format or container, but with zero inputs and enumerated data points this is likely sufficient 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?
The tool has zero parameters, so the baseline is 4. The description adds useful context about the data domain an agent can expect (hiring numbers, board activity, technology demand), even though no parameter-level semantics are applicable because the input schema is empty.
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 purpose: providing market-level hiring numbers across all boards read, and enumerates concrete metrics (open roles, 24h increases, 90-day/1-year openings, quiet boards, in-demand technologies). The 'market-level' scope clearly differentiates it from company- or board-focused siblings like check_company, find_quiet_boards, and 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 usage context is implied through 'market-level' and the aggregate metrics, suggesting it is for broad hiring overviews rather than company- or board-specific lookups. However, there is no explicit statement of when to use this tool versus siblings, nor any exclusion like 'for a specific employer, use check_company instead.'
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.
Each tool answers a clearly distinct question: company-specific verdict, fresh boards, quiet boards, market-level stats, job search, and change tracking. There is no meaningful overlap between tool purposes.
Four tools follow a verb-first pattern (check_company, find_fresh_boards, find_quiet_boards, search_jobs), but hiring_stats is a noun phrase and whats_changed is question-style. The names are readable but not fully predictable.
Six tools is well-scoped for a hiring intelligence server. Each tool covers a distinct aspect of the domain without redundancy or bloat.
The surface covers the core workflows: searching jobs, evaluating a single company, surfacing fresh versus quiet boards, viewing market stats, and monitoring changes. No obvious dead ends or missing critical operations for the stated purpose.