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Search available human capabilities

meku_search_humans
Read-onlyIdempotent

Search saved-profile aggregate counts for public and listed MEKU humans marked online, requiring every requested skill on the same human. Only the first 500 saved profiles are checked; counts_complete=false means partial counts, not a complete total. Countries list only profiles matching the requested skills and location. Location filters use exact trim/case-normalized self-reported data, not verified live location or promised availability. Empty skills adds no skill filter. Exact-filter zero does not prove nobody is available. Never returns names, profiles or contact details.

Input Schema

NameRequiredDescriptionDefault
regionNoOptional exact saved-profile region filter; missing or blank stored regions do not match.
skillsNoOptional skills: all must match on the same human, case-insensitively (max 10); empty means no skill filter.
countryNoOptional country filter.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent/openWorld annotations, it discloses the 500-profile scan ceiling, the meaning of the counts_complete flag, the self-reported and unverified nature of location data, and a hard PII exclusion. These are exactly the behavioral constraints an agent cannot infer from the schema.

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?

Each sentence carries a distinct constraint and the most important scope statement is front-loaded. It is dense run-on prose in places (the countries sentence is ambiguous about whether countries are listed or filtered), but no sentence is filler.

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?

With no output schema, the description does the work of naming the returned concepts (counts, counts_complete, countries) and the exclusion of PII. It still leaves the exact response shape and how to act on counts_complete unstated, so it stops just short of fully self-contained.

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 baseline is 3, and the description adds real meaning: exact trim/case-normalized matching on self-reported region/country, blank stored values not matching, and the AND-across-skills requirement. It goes past the terse 'Optional country filter' schema text.

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 first sentence names a specific verb (Search) and a precise resource (saved-profile aggregate counts for public and listed MEKU humans marked online), and states the AND-semantics of skills. An agent knows exactly what it gets back (counts) and what it never gets back (names, profiles, contacts).

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 conveys when results are meaningful: empty skills means no skill filter, an exact-filter zero does not prove nobody is available, and counts_complete=false means partial data. There is no sibling tool to route against, so no alternatives are needed, but it never states a positive 'use this instead of X' scenario.

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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