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classify_opportunity

Read-only

Analyze sales deals to determine fit, identify risks, and recommend actions using multiple scoring engines for comprehensive assessment.

Instructions

Run a full read on a deal in one call — fit score, persona match, risk flags, disqualification check, and a verdict: pursue, pause, or walk away. Combines multiple scoring engines for a comprehensive assessment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyNameYesCompany name
contactTitleNoPrimary contact job title
industryNoIndustry
employeeCountNoNumber of employees
revenueNoRevenue range
geographyNoLocation
dealValueNoEstimated deal value
dealStageNoCurrent deal stage
techStackNoTechnologies used
painPointsNoKnown pain points
triggerEventsNoTrigger events
championIdentifiedNoHas a champion been identified?
competitorInvolvedNoKnown competitor in the deal
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:166-186 (registration)
    Definition of the 'classify_opportunity' tool in the static catalog.
    {
      name: 'classify_opportunity',
      description: 'Run a full read on a deal in one call — fit score, persona match, risk flags, disqualification check, and a verdict: pursue, pause, or walk away. Combines multiple scoring engines for a comprehensive assessment.',
      annotations: READ_ONLY,
      inputSchema: {
        type: 'object',
        properties: {
          companyName: { type: 'string', description: 'Company name' },
          contactTitle: { type: 'string', description: 'Primary contact job title' },
          industry: { type: 'string', description: 'Industry' },
          employeeCount: { type: 'number', description: 'Number of employees' },
          revenue: { type: 'string', description: 'Revenue range' },
          geography: { type: 'string', description: 'Location' },
          dealValue: { type: 'number', description: 'Estimated deal value' },
          dealStage: { type: 'string', description: 'Current deal stage' },
          techStack: { type: 'array', items: { type: 'string' }, description: 'Technologies used' },
          painPoints: { type: 'array', items: { type: 'string' }, description: 'Known pain points' },
          triggerEvents: { type: 'array', items: { type: 'string' }, description: 'Trigger events' },
          championIdentified: { type: 'boolean', description: 'Has a champion been identified?' },
          competitorInvolved: { type: 'string', description: 'Known competitor in the deal' },
          ...COLD_START_PARAMS,
  • The MCP RequestHandler that proxies tool calls (including classify_opportunity) to the Andru backend API.
    server.setRequestHandler(
      CallToolRequestSchema,
      async (request) => {
        if (!client) {
          return {
            content: [{ type: 'text', text: JSON.stringify({ error: 'ANDRU_API_KEY not configured. Tool execution requires an API key.' }) }],
            isError: true,
          };
        }
        const { name, arguments: args } = request.params;
        try {
          return await client.callTool(name, args || {});
        } catch (error) {
          return {
            content: [{
              type: 'text',
              text: JSON.stringify({ error: error.message }),
            }],
            isError: true,
          };
        }
      }
    );
  • The AndruClient method that makes the actual HTTP POST request to the backend for the tool execution.
    async callTool(name, args) {
      return this.post('/api/mcp/tools/call', { tool: name, arguments: args });
    }
Behavior3/5

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

Annotations indicate readOnlyHint=true and openWorldHint=true, which the description does not contradict. The description adds behavioral context by mentioning it 'combines multiple scoring engines' and outputs a verdict, which goes beyond annotations. However, it lacks details on rate limits, error handling, or performance characteristics that would be useful for an agent.

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 front-loaded with the core purpose in the first sentence and efficiently lists outputs without redundancy. Every sentence adds value by clarifying scope and methodology, making it appropriately sized and well-structured for quick comprehension.

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?

Given the tool's complexity (16 parameters, no output schema) and annotations covering safety, the description adequately explains what the tool does and its comprehensive nature. However, without an output schema, it could benefit from more detail on the return format (e.g., structure of the verdict) to be fully complete.

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?

Schema description coverage is 100%, so the schema fully documents all 16 parameters. The description does not add any parameter-specific details beyond what the schema provides, such as explaining relationships between parameters or usage tips. This meets the baseline for high schema coverage.

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 ('run a full read on a deal') and resources ('deal'), listing the comprehensive outputs (fit score, persona match, risk flags, disqualification check, verdict). It effectively distinguishes from siblings by emphasizing it 'combines multiple scoring engines for a comprehensive assessment,' unlike more focused sibling tools like 'get_icp_fit_score' or 'get_disqualification_signals'.

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 implies usage context by stating it provides a 'full read' and 'comprehensive assessment,' suggesting it should be used for holistic deal evaluation rather than piecemeal analysis. However, it does not explicitly state when not to use it or name specific alternatives among the sibling tools, which prevents a perfect 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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