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discover_prospects

Read-only

Identify companies with buying signals matching your best customers to find qualified prospects. Searches the web for similar companies without requiring existing pipeline data.

Instructions

Finds real companies that look like your best customers — searches the web for companies showing the same buying signals your winners showed. Takes 15-30 seconds. Works without prior pipeline data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyNameYesYour company name
productDescriptionYesWhat your product does and who it's for. Infer from conversation context if the user has already described their product.
coreCapabilityNoThe single most important thing your product does — the core capability that makes customers buy
industryNoTarget industry to search within
targetMarketNoTarget market segment (e.g., "Series A SaaS companies")

Implementation Reference

  • The schema definition and description for the discover_prospects tool in the static catalog.
    {
      name: 'discover_prospects',
      description: 'Finds real companies that look like your best customers — searches the web for companies showing the same buying signals your winners showed. Takes 15-30 seconds. Works without prior pipeline data.',
      annotations: READ_ONLY,
      inputSchema: {
        type: 'object',
        properties: {
          companyName: {
            type: 'string',
            description: 'Your company name',
          },
          productDescription: {
            type: 'string',
            description: 'What your product does and who it\'s for. Infer from conversation context if the user has already described their product.',
          },
          coreCapability: {
            type: 'string',
            description: 'The single most important thing your product does — the core capability that makes customers buy',
          },
          industry: {
            type: 'string',
            description: 'Target industry to search within',
          },
          targetMarket: {
            type: 'string',
            description: 'Target market segment (e.g., "Series A SaaS companies")',
          },
        },
        required: ['companyName', 'productDescription'],
      },
    },
  • The handler implementation in AndruClient that proxies the tool execution to the backend API via the /api/mcp/tools/call endpoint.
    async callTool(name, args) {
      return this.post('/api/mcp/tools/call', { tool: name, arguments: args });
    }
Behavior4/5

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

Annotations indicate read-only and open-world hints, which the description aligns with by describing a search operation. The description adds valuable behavioral context beyond annotations: it specifies the operation takes '15-30 seconds,' which informs about latency, and clarifies it 'works without prior pipeline data,' indicating no prerequisites. No contradictions with annotations are present.

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, followed by additional context in subsequent sentences. Each sentence adds value: the first defines the tool, the second explains the method and timing, and the third clarifies prerequisites. There is no wasted text, making it efficient and well-structured.

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 (external search with multiple parameters) and the absence of an output schema, the description provides good context on what the tool does and its behavior. However, it does not detail the format or content of the results (e.g., what data is returned about companies), which could be helpful for an agent to understand the output. Annotations cover safety aspects, but more on output expectations would enhance completeness.

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%, providing clear descriptions for all parameters. The description does not add significant meaning beyond the schema, as it focuses on the tool's overall function rather than parameter details. However, it implies that parameters like 'companyName' and 'productDescription' are used to infer buying signals, which slightly enhances understanding, but this is minimal given the comprehensive 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 with specific verbs ('finds', 'searches') and resources ('real companies', 'buying signals'), distinguishing it from sibling tools focused on scoring, classification, or data retrieval rather than external discovery. It explicitly mentions searching the web for companies with specific signals, which is unique among the listed siblings.

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 provides clear context for when to use this tool ('finds real companies that look like your best customers') and mentions it 'works without prior pipeline data,' which implies it's suitable for initial prospecting. However, it does not explicitly state when not to use it or name specific alternatives among the sibling tools, such as when detailed scoring or classification is needed instead.

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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