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company_profile

A company profile from its domain, read live from its own site: what it sells and to whom, pricing model and plans from its pricing page, open roles from its public Greenhouse/Lever/Ashby board, tech stack, socials, security.txt contact channels (role mailboxes and company pages only), and the domain's registrar, age, email provider, SaaS vendors and SPF/DMARC posture. Each AI-read field names its source page. Price: $0.05 (70% off when served from cache)

Input Schema

NameRequiredDescriptionDefault
cacheNoServe a cached copy when one exists (cached responses are 70% off)
domainYesThe company's website domain, e.g. "vercel.com". A full URL is accepted and reduced to its hostname.
cacheTtlNoHow long to cache this response, in seconds

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries full burden and does well on provenance: it discloses live reading, per-field source pages, the security.txt restriction to role mailboxes/company pages, and an explicit $0.05 price with 70% cache discount. It says nothing about rate limits, failure modes when a site blocks fetching, or partial-result behavior, which matters for a multi-source live scrape.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

A single dense sentence plus a pricing line; every clause enumerates a distinct returned field, so little is wasted. The core purpose is front-loaded, though the field list is long enough that the price information trails at the end.

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 describe return values, and it comprehensively enumerates the fields plus notes that each AI-read field names its source page. Missing only failure/partial-result behavior for a live multi-source scrape.

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 coverage is 100%, so domain, cache and cacheTtl are already fully documented, and the description reinforces only the cache discount. The description adds essentially no syntax or semantics beyond the schema, which sets the baseline at 3.

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

Purpose4/5

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

States a specific resource (company profile from a domain) and enumerates exactly what is returned, so an agent can tell it apart from generic fetch tools. It does not explicitly name the closest siblings (domain_intel, detect_tech, project_due_diligence), which overlap on registrar/tech/SPF data.

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 scope ('read live from its own site') implies when it is appropriate, and the cache/price note hints at a cost tradeoff, but there is no explicit when-to-use guidance or exclusion of alternatives. An agent must infer that this is the aggregate profile call rather than a targeted lookup.

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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