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Yadukrishnan117

72bpm-leadgen-mcp-server

Find Candidate Engineering Contact

leadgen_find_contact
Read-only

Find engineering decision-maker candidates for any company from public search snippets. Get CTO, VP Engineering, Head of Engineering profiles for manual verification.

Instructions

Searches public, search-engine-indexed snippets (not a LinkedIn scrape) for a likely engineering decision-maker at a company — CTO, VP Engineering, Head of Engineering, etc.

IMPORTANT: results are UNVERIFIED candidates parsed from search snippet text. They are a starting point for manual confirmation (open the profile URL yourself, or verify via a mutual connection / company site team page), not a guaranteed accurate contact. Never send outreach based solely on this tool's output without a human sanity-check.

Args:

  • company_name (string): required

  • role_hint (string, optional): a specific title to prioritize, e.g. "Head of Engineering". Defaults to trying CTO, VP Engineering, Head of Engineering, Engineering Lead, Director of Engineering.

Returns: JSON with schema: { "company_name": string, "candidates": [ { "name_guess": string | null, "title_guess": string | null, "profile_url": string | null, "snippet": string, "verified": false // always false — see IMPORTANT note above } ], "count": number }

Examples:

  • Use when: "Who leads engineering at ElectreeFi?" -> company_name="ElectreeFi"

  • Use when: "Find their Head of Data" -> company_name="Acme Corp", role_hint="Head of Data"

  • Don't use when: you need a verified email address (this tool does not find or guess emails)

Error Handling:

  • Returns "No candidates found" if search turns up nothing matching common engineering-leadership titles

  • Returns an error if BRAVE_API_KEY is not configured

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
role_hintNoPreferred title to look for, e.g. 'CTO'. Defaults to trying several common engineering-leadership titles.
company_nameYesCompany name to search for.
Behavior5/5

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

Beyond the annotations (readOnlyHint, openWorldHint, etc.), the description discloses critical behavioral traits: results are UNVERIFIED candidates parsed from snippets, the 'verified' field is always false, and the tool never finds emails. It also explains error handling for missing API keys and empty searches, giving the agent a full picture of caveats and failure modes.

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?

The description is long but well-structured into sections: overview, IMPORTANT note, Args, Returns, Examples, and Error Handling. Every section serves a purpose, and the first sentence establishes the core function. The Returns JSON block is somewhat verbose but compensates for the lack of an output schema, so it earns its place.

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?

Given the tool's moderate complexity (2 params, 1 required) and rich annotations, the description goes beyond the minimum by supplying return format, error cases, default behavior, and usage examples. The lack of an output schema is fully compensated by the inline JSON schema, making the tool's behavior and expected output completely clear.

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 describes both parameters (100% coverage), so the baseline is 3. The description adds value by listing the default titles tried when role_hint is omitted, and by providing concrete example mappings from natural language to parameter values. This goes beyond the schema's generic description but does not fully transform parameter understanding.

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 opens with a specific verb phrase: 'Searches public, search-engine-indexed snippets... for a likely engineering decision-maker at a company.' It clearly identifies the resource (public snippets), the target role (CTO, VP Engineering, etc.), and the output (candidates). It also distinguishes itself from a LinkedIn scrape, setting it apart from common contact-finder tools.

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 explicit 'Use when' examples ('Who leads engineering at ElectreeFi?') and an explicit 'Don't use when' exclusion (need a verified email address). It also advises manual confirmation before outreach. However, it does not name sibling tools as alternatives (e.g., leadgen_enrich_company for verified data), so it falls just short of the top score.

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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