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Yadukrishnan117

72bpm-leadgen-mcp-server

Discover Candidate Companies

leadgen_discover_companies
Read-only

Discover companies matching technical ICP signals across SaaS, e-mobility, IoT, or AI agents. Returns de-duplicated, signal-ranked candidates from public web searches.

Instructions

Searches the public web for companies showing technical ICP signals for one of 72BPM's four practice areas, and returns a de-duplicated, signal-ranked candidate list.

This tool does NOT scrape LinkedIn or any social platform directly. It queries a real search engine (Brave Search) the same way a human researcher would, and reads back only the public snippet text already indexed. Use leadgen_enrich_company next to go deeper on any promising candidate.

Args:

  • category ('saas' | 'e_mobility' | 'iot' | 'ai_agents'): which practice area to prospect for

  • region (string, optional): geographic focus, e.g. "India", "UAE"

  • extra_keywords (string[], optional): up to 5 extra terms to narrow the search

  • max_results (number): 1-20, default 8

Returns: JSON with schema: { "category": string, "category_label": string, "queries_used": string[], "candidates": [ { "name": string, // best-guess company/page name from search result title "domain": string | null, // extracted domain, use with leadgen_enrich_company "matched_signals": string[],// ICP phrases found in the search snippet itself "sources": [{ "title": string, "url": string, "snippet": string }] } ], "count": number }

Examples:

  • Use when: "Find e-mobility companies in India working on payment sync" -> category="e_mobility", region="India", extra_keywords=["payment sync"]

  • Use when: "Who's building multi-agent systems in the UAE" -> category="ai_agents", region="UAE"

  • Don't use when: you already have a company name and just want its tech profile (use leadgen_enrich_company instead)

Error Handling:

  • Returns an error message if BRAVE_API_KEY is not configured, with a link to get one

  • Returns "No candidates found" with the queries tried if the search turned up nothing usable

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
regionNoOptional region/country/city to focus the search, e.g. 'India', 'UAE', 'Dubai'.
categoryYesWhich 72BPM practice area to prospect for: 'saas', 'e_mobility', 'iot', or 'ai_agents'.
max_resultsNoMaximum candidate companies to return (1-20, default 8).
extra_keywordsNoOptional extra technical or business keywords to narrow the search further.
Behavior5/5

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

Beyond the annotations (read-only, open-world, non-destructive), the description discloses the mechanism ('queries a real search engine (Brave Search)... reads back only the public snippet text'), explicitly states it does NOT scrape LinkedIn, and explains error handling (missing BRAVE_API_KEY, no candidates found). This adds significant behavioral context.

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?

Although the description is longer than average, it is tightly structured with clear sections: purpose, args, returns, examples, error handling. The content is front-loaded with the core purpose, and every section adds necessary information without filler.

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?

For a tool with 4 parameters, no output schema in structured fields, and external dependencies, the description is remarkably complete. It covers purpose, mechanism, parameter semantics, full return schema, examples, error handling, and the recommended next tool. This gives an agent everything needed to invoke it correctly.

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?

Schema coverage is 100%, so the baseline is 3. The description adds value by providing concrete examples that map natural language to parameter values ('Find e-mobility companies in India...' → category, region, extra_keywords) and explains how the returned 'domain' integrates with leadgen_enrich_company. This goes beyond the schema's property descriptions.

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 ('Searches') and clearly states the resource (public web) and the output (de-duplicated, signal-ranked candidate list for one of four practice areas). It distinguishes itself from sibling tools by explicitly directing users to leadgen_enrich_company for deeper enrichment and providing a 'Don't use when' example.

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 concrete 'Use when' examples with natural-language-to-parameter mappings and a clear negative case ('Don't use when: you already have a company name...'). It also recommends the next step (leadgen_enrich_company), giving explicit guidance on when to use this tool vs alternatives.

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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