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Peopledatalabs

by io.github.pipeworx-io
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Pdl Person Enrich

pdl_person_enrich
Read-onlyIdempotent

Enrich a person from their email / LinkedIn / name+company / phone using People Data Labs. Returns job title, company, emails, phone numbers, location, and skills. Provide at least one identifier. Example: pdl_person_enrich({ email: "sean@peopledatalabs.com", _apiKey: "your-key" })

Input Schema

NameRequiredDescriptionDefault
nameNoFull name of the person, e.g. "Sean Thorne" (best combined with `company`)
emailNoEmail address of the person, e.g. "sean@peopledatalabs.com"
phoneNoPhone number of the person, e.g. "+14155551234"
_apiKeyYesYour People Data Labs API key (peopledatalabs.com; free tier ~100/mo).
companyNoCompany name or domain the person works at, e.g. "People Data Labs" or "peopledatalabs.com"
profileNoLinkedIn profile URL, e.g. "https://www.linkedin.com/in/seanthorne"
min_likelihoodNoMinimum match likelihood, integer 1-10. PDL only returns a person if confidence meets this floor.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-peopledatalabs-api-key",
+    "email": "sean@peopledatalabs.com"
+  },
+  {
+    "_apiKey": "your-peopledatalabs-api-key",
+    "company": "peopledatalabs.com",
+    "min_likelihood": 8,
+    "name": "Sean Thorne"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive behavior. The description adds useful context about what fields are returned (job title, company, emails, etc.) and the constraint 'Provide at least one identifier', which goes beyond the annotations. It does not describe potential error cases or rate limits, but that is acceptable given the strong annotation coverage.

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 two sentences plus an example, with no wasted words. It front-loads the main purpose and immediately follows with a concrete usage example, making it efficient and clear.

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 100% schema coverage, annotations, and no output schema, the description adequately explains the tool's purpose, inputs, and return fields. It lacks details on error handling or match quality, but for a straightforward enrichment API this is sufficient. It falls just short of 5 because it doesn't mention what happens when no match is found.

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 100% (all 7 parameters documented). The description adds key nuance beyond the schema by indicating that email, LinkedIn, name+company, and phone are valid identifiers, and implies that name alone or company alone may not suffice. It also provides an example using _apiKey, which reinforces the parameter usage.

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 'Enrich a person from their email / LinkedIn / name+company / phone' using a specific verb and resource, and lists the return fields (job title, company, emails, phone numbers, location, skills). This distinguishes it from the sibling tool pdl_company_enrich by focusing on 'person' data.

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?

It provides clear context on when to use the tool (to enrich a person from one or more identifiers) and gives input options. However, it does not explicitly mention when not to use it or point to alternatives like pdl_company_enrich, so it misses a 5.

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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