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OnStartups

Agent.ai MCP Server

by OnStartups

prospect_research_research_prospect

Researches a prospect using Perplexity Sonar Pro to create a structured Intelligence Brief covering snapshot, role, career, activity, priorities, outreach angles, communication style, and ICP fit.

Instructions

Researches a prospect via Perplexity Sonar Pro and produces a structured Prospect Intelligence Brief with 8 sections: Snapshot, Role, Career, Activity, Priorities, Outreach Angles, Communication Style, and ICP Fit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
prospect_nameYesFull name of the person being researched.
company_websiteYesThe domain of the company the prospect works at (e.g. hubspot.com).
linkedin_urlNoProspect's LinkedIn profile URL. Significantly enriches output.
additional_contextNoAnything else you know about this person (e.g. 'Met at SaaStr', 'Referred by Jane').
seller_websiteNoYour own company website for mutual context analysis.
seller_productNoWhat you sell or offer. Enables seller-specific outreach angles.
seller_icpNoWho your ideal customer is. Enables the ICP Fit Signals section.
company_researchNoCompany research output from a prior Company Research agent run, if available.
output_variable_nameYesVariable name to store the research output.prospect_research
Behavior2/5

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

No annotations provided, so description must disclose behavior. It mentions using Perplexity Sonar Pro and output sections, but lacks information on data sources, limitations, or side effects, leaving gaps.

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?

Single well-structured sentence contains essential action and output summary with no redundant words, achieving high efficiency.

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

Completeness3/5

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

No output schema or annotations; description outlines output sections but lacks format details. For a tool with 9 parameters and structured output, it is adequate but not thorough.

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 coverage is 100%, baseline 3. The description adds context by listing output sections (e.g., ICP Fit), which clarifies the purpose of seller_icp and seller_product parameters, but doesn't enrich parameter semantics beyond 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 that the tool researches a prospect and produces a structured intelligence brief with 8 specific sections, making its purpose distinct from sibling tools like prospect_research_find_prospects or contact_research.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives like contact_research or comprehensive_contact_intelligence; the context of use is implied but not contrasted.

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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