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Identify a person

pdl_person_identify
Read-only

Given broad, possibly-ambiguous attributes about a person, return up to 20 strongly-related profiles ranked by match score. Sits between enrich (1:1) and search (open-ended) — ideal when you can't uniquely resolve someone. API: GET /person/identify.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lidNoLinkedIn numerical ID.
nameNoFull name, e.g. 'Sean Thorne'.
emailNoEmail address, or an array of email addresses.
phoneNoPhone number (E.164 or local).
pdl_idNoPDL persistent ID for a person record.
prettyNoPretty-print the JSON response.
regionNoState / region, e.g. 'california'.
schoolNoSchool name or website the person attended.
companyNoCompany name, website, or PDL company id the person works at.
countryNoCountry name, e.g. 'united states'.
profileNoSocial profile URL(s), e.g. a LinkedIn URL. String or array.
localityNoCity / locality, e.g. 'san francisco'.
locationNoFree-text location, e.g. 'San Francisco, CA, USA'.
last_nameNoLast / family name.
titlecaseNoTitlecase the returned data (default false = lowercase).
birth_dateNoBirth date (yyyy or yyyy-mm-dd).
first_nameNoFirst / given name.
postal_codeNoPostal / ZIP code (assumed US if no country).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Beyond the readOnlyHint annotation, the description discloses that it returns up to 20 profiles ranked by match score and gives the REST endpoint. It does not describe error or no-match behavior, but for a read-only identify endpoint the disclosed limits and ranking are useful added context.

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?

Three sentences front-load the core behavior, then the positioning statement, then the API route. Every sentence earns its place and there is no redundant restating of the tool title.

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 no output schema and a broad parameter set, the description covers return count, ranking, and when to choose it. It stops short of saying whether at least one attribute parameter is required to call the API, which is a minor gap given 0 required params are declared.

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?

The input schema already describes all 18 parameters at 100% coverage, so the description does not need to repeat them. The phrase 'broad, possibly-ambiguous attributes' adds a little framing, but it does not add per-parameter meaning beyond the 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 names a specific action — take broad person attributes and return up to 20 ranked profiles — and immediately contrasts with enrich (1:1) and search (open-ended). This is enough for an agent to tell pdl_person_identify apart from its siblings without opening schemas.

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?

It explicitly says the tool sits between enrich and search and is 'ideal when you can't uniquely resolve someone', which tells the agent the selection condition versus the obvious alternatives. This is the strongest guidance possible for a sibling-disambiguation problem.

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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