PeopleSearch.im

Search people

people_search

Read-onlyIdempotent

Search a global professional database in plain English (for example 'Heads of Marketing at Series B SaaS in New York') and get a sample of matching people plus the true total match count. Results are masked previews (a masked name, role, industry, company size and location) and each one carries an opaque token. Searching is FREE and spends no credits: use it to validate that the right people exist before you pay. To get a person's real name, LinkedIn and verified email, pass their token to reveal_profile, or use find_people to unlock a batch in one call.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	How many masked previews to return (1-25, default 10). This does not limit the reported total.
`query`	Yes	Who you are looking for, in plain English. Include role, seniority, industry, company stage/size, and location as needed.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 6, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Beyond the readOnlyHint true annotation, the description discloses that results are masked previews, that each result carries an opaque token, and that searching is FREE and spends no credits. This gives the agent actionable behavioral knowledge beyond the annotations.

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 compact and well-structured: it opens with the core action, then explains output format, cost behavior, and follow-up alternatives. Every sentence earns its place with no repetition.

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?

There is no output schema, so the description compensates by describing exactly what is returned: masked previews, the token, and the true total match count. Cost, use case, and related follow-up tools are all covered, making the definition complete for an agent.

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%, so the schema already documents query and limit well. The description adds value by giving a concrete plain-English example and explaining that results are masked previews, but the schema already carries the core parameter semantics.

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 states a specific verb and resource: 'Search a global professional database' and returns matching people plus a total count. It clearly distinguishes the masked-preview, validation-oriented nature of this tool from siblings like reveal_profile and find_people.

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?

The description explicitly says to use this tool to 'validate that the right people exist before you pay.' It also names the alternatives: pass a token to reveal_profile for one person, or use find_people to unlock a batch.

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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TDQS

A4.2/5.0

Disambiguation4/5

Most tools have distinct purposes, but pairs like company_search/lookup_company and find_linkedin_profile/lookup_linkedin_profile could cause selection errors. The free-search versus paid-unlock flow (people_search/find_people/reveal_profile) is well-differentiated by detailed descriptions.

Naming Consistency3/5

Naming mixes verb_noun patterns (find_people, fetch_email) with noun_verb patterns (company_search, people_search), and uses overlapping verbs like find, lookup, search, and fetch. The pattern is readable but not consistently predictable.

Tool Count5/5

With 12 tools, the set is well-scoped for a people search and email enrichment service. Each tool covers a distinct operation, from free search to paid profile unlock, email verification, and credit checking, without unnecessary bloat.

Completeness4/5

The toolset covers the core lifecycle: free search, paid profile unlock, email fetch, verification, and reverse lookup. Minor gaps exist, such as not being able to fetch an email later for a profile unlocked via reveal_profile unless include_email is set initially, but agents can work around these.

Resources

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