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Job Posting to Hiring Manager Email

job-posting-to-hiring-manager-email

Turn a Greenhouse or Lever vacancy into the named hiring owner behind it, with a provider-verified work address from your own BYOK enrichment key. You pay $0.02 only for a verified hiring contact; ambiguous company matches, unsupported boards and empty results are free. — $0.02/call, x402 (USDC on base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobsYesOne to 100 vacancies. Give either a direct ATS posting URL, or a company domain plus the exact job title.
providerNameNoWhich provider your key belongs to. Only meaningful together with a provider API key.
maxConcurrencyNoVacancies processed concurrently only in local non-monetized runs. On-platform source work and paid delivery are sequential, and Lever pacing stays at one request per second regardless of this value.
providerApiKeyNoYour own key, used on your own quota. It is never stored, never logged, never reused across runs and never redistributed. Without it, no owner lookup is attempted and the vacancy returns free.

TDQS

A4/5.0
Behavior4/5

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

The description discloses key behavioral traits beyond the annotations: it uses the caller's own BYOK key, charges $0.02 per verified contact via USDC on base, and only bills for verified results (ambiguous/empty are free). It also implies external data provider calls. This adds context that the boolean hint annotations do not capture, such as cost and verification semantics.

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 front-loading the action and resource, and the second sentence adding pricing and fallback behavior. Every phrase serves a purpose—there is no fluff, repetition, or tangential information.

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?

Given the tool's moderate complexity (4 parameters, no output schema) and sparse annotations, the description provides a solid overview of purpose, pricing, and edge cases. It does not fully describe the output structure, but the title and first sentence imply the output (hiring manager name + email). The schema fills in parameter details, making this reasonably complete for selection and invocation.

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?

The input schema already provides 100% description coverage for all 4 parameters, including details like 'Direct posting link on a supported board' and 'Your own key, used on your own quota.' The main description adds high-level context about Greenhouse/Lever and BYOK, but it does not significantly extend the meaning beyond what the schema already explains, meeting the baseline for high schema coverage.

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: 'Turn a Greenhouse or Lever vacancy into the named hiring owner behind it, with a provider-verified work address.' This is a specific verb ('turn') with a specific resource ('vacancy') and output ('hiring owner' + 'verified work address'). It distinguishes itself from sibling tools like domain-to-verified-work-email by focusing on job posting URLs and ATS boards.

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

Usage Guidelines3/5

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

The description implies usage context—it's for Greenhouse or Lever vacancies with a BYOK enrichment key, and notes that unsupported boards yield free empty results. However, it does not explicitly state when to use this tool versus alternatives, nor provides clear exclusions beyond the board-type limitation. The guidance is mostly implied rather than explicit.

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

A3.9/5.0
Disambiguation4/5

Each tool targets a distinct lead-intelligence function: scraping careers pages, finding booking gaps, verifying published emails, identifying hiring managers, cleaning lists, and detecting permits. The two vertical gap finders (clinic and restaurant) are structurally similar but clearly separated by domain in both names and descriptions, avoiding real confusion.

Naming Consistency3/5

Most names follow a lowercase hyphenated descriptive pattern (e.g., 'careers-page-scraper', 'lead-list-cleaner'), but 'pricing_info' breaks the pattern by using an underscore. The naming is readable and mostly consistent, yet it lacks a uniform verb_noun convention across the set.

Tool Count5/5

With 8 tools, the server is well-scoped for a lead-intelligence bundle, covering the lead lifecycle from discovery to enrichment to cleaning. This falls comfortably within the ideal 3-15 range, and no tool feels redundant or out of place.

Completeness4/5

The set covers lead discovery (careers, permits, gap finders), enrichment (domain-to-email, hiring-manager-email), and processing (lead-list-cleaner), forming a coherent workflow. Minor gaps exist—like a general web-search lead finder or a tool to update lead statuses—but they don't break the core pipeline.

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