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Find people on LinkedIn by filters

linkedin_leads

Build LinkedIn and Sales Navigator lead lists from job title, location, and company filters, then get verified work emails for outreach.

Instructions

Build a list of people from LinkedIn and Sales Navigator audiences using filters, and get their verified work email addresses. This takes filters, not a LinkedIn URL: job titles, locations, company domains, keywords, industry, company size and revenue range. Use it for requests like "CMOs at SaaS companies in California" or "heads of engineering at companies using Stripe". Costs $9 per 1,000. If you get far fewer results than requested, set include_unverified to true: that single filter is the most common reason a list comes back short. Returns a job id: poll job_status, then download.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fundingNoFunding stages, e.g. ["seed","series_a"].
keywordsNoCompany keywords to include.
list_nameNoName for the export file.
locationsNoRegions or countries from Evascrape's list, lowercase. US states end in ", us" (e.g. "california, us"), other regions in ", <country>" (e.g. "england, united kingdom"), or a bare country name ("germany"). Unknown values are rejected.
seniorityNoSeniority levels, e.g. ["c_suite","director","founder"].
industriesNoIndustries to include, lowercase.
job_titlesNoComma separated job titles to include. Example: "CMO, VP Marketing".
max_revenueNoMaximum company revenue band, e.g. "50M".
min_revenueNoMinimum company revenue band, e.g. "1M".
company_sizesNoHeadcount bands, e.g. ["51-100","101-200"].
total_recordsYesMaximum leads. Multiple of 100, minimum 100, maximum 50000.
company_domainsNoComma separated company domains. Domains, not names: "stripe.com", not "Stripe".
exclude_keywordsNoCompany keywords to exclude.
exclude_locationsNoRegions or countries to exclude.
email_verificationNoExtra real-time verification for $5 per 1,000. Default false.
exclude_job_titlesNoComma separated job titles to exclude.
include_unverifiedNoInclude people whose email is not yet verified. Greatly increases volume. Default false.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.6/5.0
Behavior5/5

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

Goes well beyond the annotations: discloses pricing ($9 per 1,000, $5 extra for email_verification), the asynchronous job pattern (returns a job id, poll job_status, then download), and a diagnostic tip about the most common cause of short lists. The annotations only mark it non-readonly/open-world/non-idempotent, so this description carries and adds the behavioral weight.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose, then input-shape clarification, examples, cost, a troubleshooting hint, and the return workflow. Every sentence earns its place, though the filter enumeration partly duplicates the schema and could be trimmed.

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

Completeness5/5

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

For a 17-parameter, no-output-schema, async tool, the description closes all the gaps an agent needs: what it returns (a job id and the poll-then-download follow-up), the cost model, and the key tuning knob. Nothing essential to calling it correctly is missing.

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 description coverage is 100%, so the baseline is 3. The description adds genuine meaning beyond the schema by grouping the filter families and, crucially, explaining the practical consequence of include_unverified ('the most common reason a list comes back short') rather than merely restating its default.

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?

States a specific verb and resource (build a list of people from LinkedIn/Sales Navigator) plus the output (verified work emails), and explicitly distinguishes the input style: 'This takes filters, not a LinkedIn URL.' An agent can tell it apart from google_maps_leads and apollo_export without opening any schema.

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?

Provides concrete triggering examples ('CMOs at SaaS companies in California') and a conditional instruction for shortfalls ('If you get far fewer results than requested, set include_unverified to true'), which is real when-to-use guidance. It stops short of naming when to prefer the sibling audience tools (apollo_export, google_maps_leads) over this one, so routing between the three remains an inference.

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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