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get_persona_profile

Read-only

Generate detailed buyer personas for enterprise sales by analyzing job titles, industries, and product context to reveal motivations, objections, and effective messaging strategies.

Instructions

Look up who you're actually talking to before the call — what they care about at 7 AM, why they'll say no, and exactly how to open. Returns persona details including MBTI distribution, empathy map, and messaging angles.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesJob title of the person you're meeting (e.g., "VP Engineering", "CTO", "Head of Sales")
industryNoTheir industry
companySizeNoTheir company size range
productDescriptionNoA brief description of what the user's product does and who it's for. Infer this from the conversation if the user has already described their product. If the user hasn't mentioned their product yet, ask them: "What does your product do, and who do you sell to?" before calling this tool.
verticalNoThe industry the user sells into (e.g., "fintech", "healthcare", "defense"). Infer from conversation context — the user's product description, company name, or the companies they're asking about. If unclear, ask.
targetRoleNoThe buyer role being evaluated (e.g., "CFO", "CTO", "VP Sales"). Infer from context — often explicit in the user's question. If not mentioned, default to the most senior relevant role for their vertical.

Implementation Reference

  • src/catalog.js:74-88 (registration)
    Registration of 'get_persona_profile' tool in the catalog.
    {
      name: 'get_persona_profile',
      description: 'Look up who you\'re actually talking to before the call — what they care about at 7 AM, why they\'ll say no, and exactly how to open. Returns persona details including MBTI distribution, empathy map, and messaging angles.',
      annotations: READ_ONLY,
      inputSchema: {
        type: 'object',
        properties: {
          title: { type: 'string', description: 'Job title of the person you\'re meeting (e.g., "VP Engineering", "CTO", "Head of Sales")' },
          industry: { type: 'string', description: 'Their industry' },
          companySize: { type: 'string', description: 'Their company size range' },
          ...COLD_START_PARAMS,
        },
        required: ['title'],
      },
    },
Behavior4/5

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

The annotations indicate read-only and open-world operations, which the description doesn't contradict. The description adds valuable behavioral context about what information is returned (persona details with specific components like MBTI distribution and empathy map) and the timing context (before a call). However, it doesn't mention potential limitations like data availability or freshness.

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 perfectly front-loaded with the core purpose in the first clause, followed by specific return details. Every sentence earns its place by providing essential context about timing, use case, and output format without any wasted words.

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

Completeness4/5

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

For a read-only tool with comprehensive parameter documentation and no output schema, the description provides good context about what information is returned and when to use it. It could be more complete by mentioning what happens when persona data isn't available or providing more detail about the return format structure.

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

Parameters3/5

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

With 100% schema description coverage, the schema already documents all 6 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. The baseline score of 3 reflects adequate coverage through the schema alone.

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 with specific verbs ('look up who you're actually talking to') and resources ('persona details including MBTI distribution, empathy map, and messaging angles'). It distinguishes from siblings by focusing on pre-call persona analysis rather than scoring, classification, or other functions listed in the sibling tools.

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 explicitly states when to use this tool ('before the call') and provides clear context about its purpose ('what they care about at 7 AM, why they'll say no, and exactly how to open'). While it doesn't name specific alternatives, the timing guidance and focus on persona details provide strong usage context.

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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