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Yadukrishnan117

72bpm-leadgen-mcp-server

Enrich Company Technical Profile

leadgen_enrich_company
Read-only

Crawl a company's public website, LinkedIn snippet, and job postings to build a technical profile and identify ICP signal matches across all four practice areas.

Instructions

Builds a technical profile for a specific company by crawling its own public website (homepage, /careers, /about, /blog, /engineering) and cross-referencing an indexed LinkedIn snippet plus public job postings — all via legitimate, ToS-compliant sources (no LinkedIn scraping).

Returns which ICP signal phrases were found, for ALL FOUR practice areas at once, so you can see if a company is a multi-category fit (e.g. an IoT hardware company also hiring for LLM-based ops automation).

Args:

  • company_name (string): required

  • domain (string, optional): website domain; enrichment is much richer with it, but LinkedIn/job-posting signal still works without it

Returns: JSON with schema: { "company_name": string, "domain": string | null, "crawled_pages": string[], // URLs successfully crawled "crawl_errors": string[], // pages skipped/failed, with reason "linkedin_snippet": { "title": string, "url": string, "snippet": string } | null, "job_posting_hits": [{ "title": string, "url": string, "snippet": string }], "signals_by_category": { "": { "matched": [{ "phrase": string, "weight": number, "found_in": string[] }], "missing": string[] } } }

Examples:

  • Use when: "What's ElectreeFi's tech stack look like?" -> company_name="ElectreeFi", domain="electreefi.com"

  • Use when: you have a candidate from leadgen_discover_companies and want the full picture before scoring

  • Don't use when: you just want a single category's numeric fit score (use leadgen_score_lead — it's cheaper and more direct)

Error Handling:

  • If domain is unreachable, crawl_errors will explain why but the tool still returns LinkedIn/job-posting signal

  • Returns an error if BRAVE_API_KEY is missing and no domain was provided (nothing to enrich from)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNoCompany's website domain, e.g. 'electreefi.com'. Omit if unknown — LinkedIn/job data will still be gathered.
company_nameYesCompany name, e.g. 'ElectreeFi'.
Behavior5/5

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

Beyond the annotations (readOnlyHint=true, etc.), the description adds substantial behavioral context: legitimate/ToS-compliant sources, partial results when domain is unreachable, error conditions for BRAVE_API_KEY, and the multi-category coverage. It transparently documents limitations and edge cases.

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 long but well-structured with sections (overview, Args, Returns, Examples, Error Handling). Every section contributes necessary information, and the return JSON schema is essential given no output schema. The first sentence front-loads the core purpose, and the length is justified by the tool's complexity.

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

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description fully equips the agent to use the tool: it explains what enrichment covers, provides a detailed return schema, error handling, and explicit examples. Despite no output schema in annotations, the description carries the burden and fulfills it completely.

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

Parameters4/5

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

The schema already provides complete descriptions for both parameters (100% coverage). The description adds value by explaining the impact of the optional domain parameter ('enrichment is much richer with it') and showing example values in context. This goes beyond the schema's basic type/length constraints.

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: 'Builds a technical profile for a specific company by crawling its own public website... and cross-referencing an indexed LinkedIn snippet plus public job postings.' It uses specific verbs and resources, and distinguishes from siblings by explicitly naming leadgen_score_lead as an alternative for single-category scoring.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance with 'Use when' examples and a 'Don't use when' section that names the alternative tool (leadgen_score_lead). It also clarifies when to include the optional domain parameter, making the decision framework complete.

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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