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by io.github.linglistack
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Count people

count_people
Read-only

Count how many LinkedIn people match a filter set — FREE, instant, and exact. Use this BEFORE search_people to size a market, and fan out SEVERAL angles in parallel (different title variants, with/without a location, each industry) then union the ones that land — counting never costs credits, so go broad. Filters: keywords, titles, locations, industries, companies (by name) or companyIds (exact LinkedIn ids from find_companies / list_funding_signals), functions, seniorities, companyHeadcounts, company revenue band (companyMinRevenueMillions / companyMaxRevenueMillions), plus changedJobs / postedOnLinkedIn buying signals. With a revenue band, LinkedIn searches people at the best-matching companies in that band (up to 100) — when revenueScope.complete is false, the count covers that sample, not every company in the band; say so. A zero from one filter is a claim about that filter, not the market — try another angle before concluding it's empty.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titlesNoJob titles, e.g. ["Chief Technology Officer","VP of Engineering"]. Matched as OR'd keywords — pass variants to go broad.
keywordsNoFree-text keywords searched across all profile fields (title, headline, about, company).
companiesNoCurrent companies by name, e.g. ["Google","Stripe"]. Each name is looked up and the top match used — when you already have LinkedIn company ids (find_companies / list_funding_signals return companyId), pass companyIds instead.
functionsNoJob functions/departments, e.g. ["Engineering","Sales","Marketing"].
locationsNoPerson locations in plain English, e.g. ["United States","London"]. Resolved to LinkedIn regions automatically.
companyIdsNoCurrent companies by LinkedIn company id — the companyId values find_companies / list_funding_signals return (up to 100). Exact: no name lookup, so no same-name mix-ups. Combine with seniorities and changedJobs / postedOnLinkedIn to get the decision-makers at signal companies who just moved / are posting.
industriesNoCompany industries in plain English, e.g. ["Software","Financial Services"].
changedJobsNoOnly people who recently changed jobs (buying signal).
senioritiesNoSeniority levels, e.g. ["CXO","VP","Director","Manager"] (owner/partner, cxo, vice_president, director, experienced_manager, entry_level_manager, strategic, senior, entry_level, in_training).
revenueCurrencyNoISO currency of the revenue band, e.g. "USD" (default), "EUR", "GBP".
postedOnLinkedInNoOnly people who recently posted on LinkedIn (active/reachable signal).
companyHeadcountsNoCompany size bands. Valid: self-employed, 1-10, 11-50, 51-200, 201-500, 501-1000, 1001-5000, 5001-10000, 10001+.
companyMaxRevenueMillionsNoOnly people at companies with at most this annual revenue, in millions (e.g. 100 = up to $100M). Same brackets as companyMinRevenueMillions.
companyMinRevenueMillionsNoOnly people at companies with at least this annual revenue, in millions (e.g. 10 = $10M+) — use it to target companies that can afford the user's price. LinkedIn uses fixed brackets (0, 0.2, 1, 2.5, 5, 10, 20, 50, 100, 500, 1000); other values widen to the nearest one. The companies are matched on the same industries / companyHeadcounts / locations (as HQ location). Revenue is LinkedIn's estimate — companies without one are left out.

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?

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false, so safety and openness are covered. The description adds important behavioral context beyond annotations: the count is free and doesn't cost credits, and it explains the revenue-band sampling limitation (up to 100 companies, revenueScope.complete false means a sample) plus the zero-result caveat. This is valuable operational transparency.

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?

Well-structured and front-loaded with the core verb and benefit, followed by usage strategy and filter list. The second half is dense and slightly long, but every sentence provides actionable information. Minor verbosity in the filter enumeration.

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?

Given 14 parameters and no output schema, the description covers the essential context: what it returns (a count), when to use it, how to combine filters, the sampling caveat, and the zero-result interpretation. An agent has enough to call it correctly and interpret the result.

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 description coverage is 100%, so each parameter is already documented in the schema. The description enumerates the filter categories (keywords, titles, locations, etc.) and adds some naming/flow context (use companyIds from find_companies), which is minor since the schema already carries the detailed semantics. Baseline 3 is appropriate.

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 precise verb+resource+scope: 'Count how many LinkedIn people match a filter set'. It explicitly distinguishes itself from search_people by noting it should be used BEFORE it, and describes the exact nature of the count (free, instant, exact).

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?

Gives explicit when-to-use guidance ('Use this BEFORE search_people to size a market'), prescribes a workflow (fan out several angles, union the ones that land), and explains why ('counting never costs credits, so go broad'). It also warns about a common pitfall (a zero from one filter is not an empty market, try another angle).

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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