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Yadukrishnan117

72bpm-leadgen-mcp-server

Save Lead to Pipeline

leadgen_save_lead

Persist qualified leads to a local pipeline store (JSON) for follow-up tracking. Provide company name, category, and optional score, tier, and contact details; receives a saved lead ID for later updates.

Instructions

Persists a qualified lead to the local pipeline store (a JSON file under ./data by default; see README for swapping in a real database for hosted/multi-user deployments).

Args:

  • company_name (string): required

  • domain, region (string, optional)

  • category ('saas' | 'e_mobility' | 'iot' | 'ai_agents'): required

  • score (number 0-100, optional): from leadgen_score_lead

  • tier ('hot' | 'warm' | 'cold', optional): from leadgen_score_lead

  • notes (string, optional)

  • contact_name, contact_title, contact_profile_url (string, optional): from leadgen_find_contact

Returns: the saved lead record including its generated "id", which you'll need for leadgen_update_lead.

Examples:

  • Use when: you've scored a company as hot/warm and want to track it for follow-up

  • Don't use when: you're just browsing candidates (save only once you're tracking it for real)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tierNo
notesNo
scoreNo
domainNo
regionNo
categoryYes
company_nameYes
contact_nameNo
contact_titleNo
contact_profile_urlNo
Behavior5/5

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

Beyond the sparse annotations (readOnlyHint=false), the description discloses storage location (local JSON file), persistence behavior, and that it returns a generated 'id' needed for leadgen_update_lead. This is rich behavioral context not available elsewhere.

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 well-structured with an Args list, Returns note, and Examples section. Every sentence adds value—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?

With 10 parameters, no output schema, and minimal annotations, the description covers all critical aspects: persistence semantics, storage details, return value, and usage context. It is complete enough for an agent to invoke correctly.

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

Parameters5/5

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

Schema coverage is 0%, but description fully compensates by listing each parameter with required/optional status and origin (e.g., 'from leadgen_score_lead', 'from leadgen_find_contact'). This adds meaning beyond the bare types/enums in the schema.

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 purpose: "Persists a qualified lead to the local pipeline store." This uses a specific verb ('persists') and resource ('lead'), and distinguishes it from siblings like leadgen_update_lead (updating existing leads) and leadgen_list_leads (listing).

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?

Explicit guidance is provided: "Use when: you've scored a company as hot/warm and want to track it for follow-up" and "Don't use when: you're just browsing candidates." This clearly states when to use vs. avoid.

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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