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dyngai

apollo-mcp

by dyngai

apollo_enrich_person

Enrich person data by email, LinkedIn URL, or name+domain/organization. Returns a compact summary or full raw payload.

Instructions

Enrich a person by email, linkedin_url, or name+domain/name+organization_name using Apollo match. Returns a compact summary by default (~25 fields trimmed to the most useful ones). Pass full=true to return the raw Apollo payload — those can exceed 100KB and may overflow MCP token limits, so only use full=true when you need the complete employment_history / current_technologies / org block.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fullNoReturn the raw Apollo response. Defaults to false (compact).
nameNo
emailNo
domainNo
companyNoDeprecated alias for organization_name; kept for compatibility.
linkedin_urlNo
organization_nameNo
reveal_phone_numberNoReveal phone numbers (default: false)
reveal_personal_emailsNoReveal personal emails (default: false)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description carries full burden. It discloses that full=true returns raw Apollo payload which may exceed token limits, but does not mention whether the tool is read-only or has side effects. The safety profile (destructive or auth) is absent.

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?

Two sentences that are front-loaded with purpose and then detail on the key behavioral nuance. Every sentence adds value without redundancy.

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 has 9 parameters and no annotations, the description covers the essential identifiers and output options. It mentions compact vs raw payload and token risks. Missing details on required identifiers or return schema, but the output schema exists.

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

Parameters4/5

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

Schema coverage is 44%, but the description adds value by explaining the identifier combinations (email, linkedin_url, name+domain/name+organization_name) and the full parameter's effect. It also notes the 'company' parameter is deprecated. However, some parameters like reveal_phone_number are only in 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 explicitly states the tool enriches a person using identifiers like email, linkedin_url, or name+domain/name+organization_name. Among siblings like apollo_get_person, this tool is distinct for its enrichment capability with a compact or full output mode.

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 guidance on when to use full=true (only when needed for complete data) versus default compact mode, warning about token limits. However, it does not directly compare to sibling tools like apollo_get_person or apollo_search_people.

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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