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Yadukrishnan117

72bpm-leadgen-mcp-server

Score Lead Fit

leadgen_score_lead
Read-only

Score a company's fit for a specific practice area (SaaS, IoT, etc.) with a 0-100 ICP score, tier, and confidence based on public-source signals to prioritize leads.

Instructions

Computes a weighted 0-100 ICP fit score for one company against one 72BPM practice area, with a tier (hot/warm/cold) and a confidence level based on how many independent public sources corroborated the signal.

Internally this reuses the same public-source gathering as leadgen_enrich_company (and shares its short-lived cache, so calling enrich then score on the same company won't double the network calls).

Args:

  • company_name (string): required

  • domain (string, optional): website domain — scoring is more reliable with it

  • category ('saas' | 'e_mobility' | 'iot' | 'ai_agents'): required, which practice area to score against

Returns: JSON with schema: { "company_name": string, "category": string, "category_label": string, "score": number, // 0-100 "tier": "hot" | "warm" | "cold", "confidence": "low" | "medium" | "high", // based on # of independent sources with matches "matched_signals": [{ "phrase": string, "weight": number, "found_in": string[] }], "missing_high_value_signals": string[], // top signals NOT found — good discovery-call questions "rationale": string }

Tier thresholds: score >= 55 = hot, >= 30 = warm, else cold. Confidence: 3+ independent sources = high, 2 = medium, 0-1 = low. Treat "low confidence + hot tier" as promising but unverified — worth a human look before outreach.

Examples:

  • Use when: "Is Acme Corp a good IoT lead?" -> company_name="Acme Corp", domain="acme.com", category="iot"

  • Don't use when: you want the full multi-category breakdown (use leadgen_enrich_company instead)

Error Handling:

  • Returns an error if BRAVE_API_KEY is missing and domain gathers nothing

  • Returns score=0, tier="cold", confidence="low" (not an error) if the company has no public signal at all — that's a valid, informative result

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNoCompany's website domain, if known.
categoryYesWhich practice area to score fit against: 'saas', 'e_mobility', 'iot', or 'ai_agents'.
company_nameYesCompany name.
Behavior5/5

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

Even with readOnlyHint=true, the description adds rich behavioral context: it shares a cache with leadgen_enrich_company, explains tier and confidence thresholds, defines the low-confidence+hot-tier caveat, and details error handling (including the score=0/cold/low no-signal case). No contradictions with annotations.

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 clear sections (Args, Returns, thresholds, Examples, Error Handling). Every sentence adds operational or decision-relevant detail, and the first sentence front-loads the core purpose. No filler or redundancy.

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 tool is moderately complex (3 params, no output schema in the tool definition, error cases, thresholds). The description compensates by providing a full return schema, threshold values, confidence semantics, cache-sharing behavior, and error handling. Contextually complete for an AI agent to invoke 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 schema already documents all parameters. The description adds value by specifying that domain makes scoring 'more reliable,' clarifying that category is a practice area, and providing a concrete example mapping natural language to parameter values. This goes beyond the schema's basic 'Company name' / 'Which practice area' 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 and resource: 'Computes a weighted 0-100 ICP fit score for one company against one 72BPM practice area.' It clearly distinguishes this from sibling tools by explaining the single-category scoring focus and pointing to leadgen_enrich_company for multi-category breakdowns.

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 'Use when' and 'Don't use when' examples with a concrete query and a named alternative: 'Don't use when: you want the full multi-category breakdown (use leadgen_enrich_company instead).' This is model-level guidance for when to select this tool versus a sibling.

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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