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Glama

Company Hiring Radar

company-hiring-radar
Read-only

Pull every open role a company is hiring for from its public job board (Greenhouse, Lever, Ashby) and turn it into a buying/expansion signal: role count, which functions are growing (sales, engineering, marketing), remote share and what's new. No login, no scraping, no proxies. — $0.01/call, x402 (USDC on base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsNoHighlight roles whose title, department or function contains any of these words (e.g. `sales`, `marketing`, `growth`). Matched roles are returned separately in `matchedRoles`.
companiesYesOne entry per company. Best form is `provider:token` — e.g. `greenhouse:stripe`, `lever:spotify`, `ashby:ramp`. The token is the company's slug on its job board (the part in the careers URL). A bare token like `stripe` auto-detects the provider.
newWindowDaysNoA role counts as new if it was first published within this many days.
maxConcurrencyNoHow many companies to check in parallel.

TDQS

A4.2/5.0
Behavior4/5

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 known. The description adds valuable behavioral details beyond annotations: 'No login, no scraping, no proxies' and the cost of '$0.01/call', which helps an agent understand operational constraints. 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.

Conciseness5/5

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

The description is two sentences, with the first sentence packed with essential info (what it does, sources, outputs) and the second adding cost and limitations. Every word earns its place, and it is front-loaded with the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

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 must explain return values. It lists key output metrics (role count, growing functions, remote share, what's new) and mentions cost and no-auth requirements. This is sufficient for a moderate-complexity read-only tool, though it could detail response structure more.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description does not add any extra meaning about parameters beyond the schema; it focuses on outputs and benefits. This is adequate as the schema's parameter descriptions are already thorough.

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

Purpose5/5

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

The description clearly states the tool's function: pulling every open role from a company's public job board (Greenhouse, Lever, Ashby) and converting it into hiring signals such as role count, growing functions, remote share, and new roles. This specific verb+resource phrasing distinguishes it from sibling tools like hiring-trend-index or company-lookup.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for use: 'turn it into a buying/expansion signal' indicates this is for assessing company growth via hiring. It does not explicitly name alternatives or when-not-to-use, but the scope is well-defined, so it earns a 4.

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

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TDQS

A3.8/5.0
Disambiguation3/5

Several tools overlap in signal space: hiring-radar, hiring-trend-index, layoff-tracker, and intent-signal-aggregator all touch hiring; funding-alert vs funding-round-tracker, sanctions-screening vs sanctions-update-alert, and rollup tools vs individual checks create boundary ambiguity. However, each has a distinct output format, so descriptions help.

Naming Consistency4/5

Tool names are consistently lowercase with hyphens and descriptive noun phrases (e.g., company-hiring-radar, litigation-check), but pricing_info breaks the pattern with snake_case and a non-descriptive name.

Tool Count3/5

With 20 tools, the server feels heavy and covers a wide range of premium data services, but each tool does target a distinct data source or workflow, so it's borderline rather than excessive.

Completeness4/5

The surface covers company identity, hiring, litigation, sanctions, funding, and patents well, but lacks direct financials, ownership structure, and general news monitoring beyond funding/layoffs, leaving some sales-intelligence gaps.

Resources