Human Pages

search_humans

Search for humans available for hire. Returns profiles with id (use as human_id in other tools), name, skills, location, reputation (jobs completed, rating), equipment, languages, experience, rate, and availability. All filters are optional — combine any or use none to browse. Key filters: skill (e.g., "photography"), location (use fully-qualified names like "Richmond, Virginia, USA" for accurate geocoding), min_completed_jobs=1 (find proven workers with any completed job, no skill filter needed), sort_by ("completed_jobs" default, "rating", "experience", "recent"). Default search radius is 30km. Response includes total count and resolvedLocation. Contact info requires get_human_profile (registered agent needed). Typical workflow: search_humans → get_human_profile → create_job_offer.

Input Schema

TableJSON Schema

Name	Required	Description
`lat`	No	Latitude for radius search (requires lng and radius)
`lng`	No	Longitude for radius search (requires lat and radius)
`field`	No	Filter by field of study (e.g., "Computer Science", "Marketing"). Partial match, case-insensitive.
`skill`	No	Filter by skill tag (e.g., "photography", "driving", "cleaning", "notary")
`degree`	No	Filter by education degree (e.g., "Bachelor", "MBA", "PhD"). Partial match, case-insensitive.
`radius`	No	Search radius in kilometers (default: 30km). Works with both text location and explicit lat/lng coordinates.
`sort_by`	No	Sort results by: "completed_jobs" (humans with platform experience first), "rating" (highest rated first), "experience" (most years of professional experience first), "recent" (most recently active first). Default sorts by completed jobs, then rating, then experience.
`language`	No	Filter by language ISO code (e.g., "en", "es", "zh")
`location`	No	Filter by location. Use fully-qualified names for best results (e.g., "San Francisco, California, USA" not just "San Francisco"). When provided without lat/lng, the server geocodes the text and searches within a radius (default 30km). Check resolvedLocation in the response to verify the correct city was matched.
`max_rate`	No	Maximum hourly rate in USD. Humans who set rates in other currencies are auto-converted to USD for comparison.
`verified`	No	Filter by verification status. Use "humanity" to only return humans who have verified their identity via Gitcoin Passport (score >= 20).
`equipment`	No	Filter by equipment (e.g., "car", "drone", "camera")
`has_photo`	No	Only return humans with an approved profile photo.
`work_mode`	No	Filter by work mode preference (REMOTE, ONSITE, or HYBRID)
`certificate`	No	Filter by certificate name or issuer (e.g., "AWS", "PMP", "Google"). Partial match, case-insensitive.
`institution`	No	Filter by educational institution name (e.g., "MIT", "Oxford"). Partial match, case-insensitive.
`min_vouches`	No	Only return humans vouched for by at least this many other users.
`min_channels`	No	Only return humans with at least this many notification channels active (0-4). Channels: email, telegram, whatsapp, push. Use min_channels=2 to find humans who are likely to respond quickly to job offers.
`payment_type`	No	Filter by accepted payment type (UPFRONT, ESCROW, or UPON_COMPLETION)
`fiat_platform`	No	Filter by fiat payment platform the human accepts (e.g., "WISE", "PAYPAL", "VENMO", "REVOLUT", "CASHAPP", "ZELLE", "MONZO", "N26", "MERCADOPAGO")
`accepts_crypto`	No	Filter to only show humans who have a crypto wallet set up and can accept USDC payments
`available_only`	No	Only return humans who are currently available (default: true)
`min_experience`	No	Minimum years of professional experience
`has_verified_login`	No	Only return humans who have verified their identity via an OAuth provider (Google, LinkedIn, or GitHub). Does not reveal which provider.
`min_completed_jobs`	No	Only return humans who have completed at least this many jobs on the platform. Use min_completed_jobs=1 to find all workers with any platform track record. Works with or without other filters — no skill filter needed.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedMar 19, 2026

addedInput schema / properties / accepts_crypto

Added value: +{
+  "description": "Filter to only show humans who have a crypto wallet set up and can accept USDC payments",
+  "type": "boolean"
+}

Changed16 schema fields changedMar 19, 2026

addedInput schema / properties / certificate

Added value: +{
+  "description": "Filter by certificate name or issuer (e.g., \"AWS\", \"PMP\", \"Google\"). Partial match, case-insensitive.",
+  "type": "string"
+}

addedInput schema / properties / degree

Added value: +{
+  "description": "Filter by education degree (e.g., \"Bachelor\", \"MBA\", \"PhD\"). Partial match, case-insensitive.",
+  "type": "string"
+}

addedInput schema / properties / fiat_platform

Added value: +{
+  "description": "Filter by fiat payment platform the human accepts (e.g., \"WISE\", \"PAYPAL\", \"VENMO\", \"REVOLUT\", \"CASHAPP\", \"ZELLE\", \"MONZO\", \"N26\", \"MERCADOPAGO\")",
+  "type": "string"
+}

addedInput schema / properties / field

Added value: +{
+  "description": "Filter by field of study (e.g., \"Computer Science\", \"Marketing\"). Partial match, case-insensitive.",
+  "type": "string"
+}

addedInput schema / properties / has_photo

Added value: +{
+  "description": "Only return humans with an approved profile photo.",
+  "type": "boolean"
+}

addedInput schema / properties / has_verified_login

Added value: +{
+  "description": "Only return humans who have verified their identity via an OAuth provider (Google, LinkedIn, or GitHub). Does not reveal which provider.",
+  "type": "boolean"
+}

addedInput schema / properties / institution

Added value: +{
+  "description": "Filter by educational institution name (e.g., \"MIT\", \"Oxford\"). Partial match, case-insensitive.",
+  "type": "string"
+}

changedInput schema / properties / location / description

Previous value: -"Filter by location name or neighborhood (partial match, e.g., \"San Francisco\" or \"Mission District\")"New value: +"Filter by location. Use fully-qualified names for best results (e.g., \"San Francisco, California, USA\" not just \"San Francisco\"). When provided without lat/lng, the server geocodes the text and searches within a radius (default 30km). Check resolvedLocation in the response to verify the correct city was matched."

addedInput schema / properties / min_channels

Added value: +{
+  "description": "Only return humans with at least this many notification channels active (0-4). Channels: email, telegram, whatsapp, push. Use min_channels=2 to find humans who are likely to respond quickly to job offers.",
+  "type": "number"
+}

addedInput schema / properties / min_completed_jobs

Added value: +{
+  "description": "Only return humans who have completed at least this many jobs on the platform. Use min_completed_jobs=1 to find all workers with any platform track record. Works with or without other filters — no skill filter needed.",
+  "type": "number"
+}

addedInput schema / properties / min_experience

Added value: +{
+  "description": "Minimum years of professional experience",
+  "type": "number"
+}

addedInput schema / properties / min_vouches

Added value: +{
+  "description": "Only return humans vouched for by at least this many other users.",
+  "type": "number"
+}

addedInput schema / properties / payment_type

Added value: +{
+  "description": "Filter by accepted payment type (UPFRONT, ESCROW, or UPON_COMPLETION)",
+  "enum": [
+    "UPFRONT",
+    "ESCROW",
+    "UPON_COMPLETION"
+  ],
+  "type": "string"
+}

changedInput schema / properties / radius / description

Previous value: -"Search radius in kilometers (requires lat and lng)"New value: +"Search radius in kilometers (default: 30km). Works with both text location and explicit lat/lng coordinates."

changedInput schema / properties / skill / description

Previous value: -"Filter by skill tag (e.g., \"photography\", \"driving\", \"notary\")"New value: +"Filter by skill tag (e.g., \"photography\", \"driving\", \"cleaning\", \"notary\")"

addedInput schema / properties / sort_by

Added value: +{
+  "description": "Sort results by: \"completed_jobs\" (humans with platform experience first), \"rating\" (highest rated first), \"experience\" (most years of professional experience first), \"recent\" (most recently active first). Default sorts by completed jobs, then rating, then experience.",
+  "enum": [
+    "completed_jobs",
+    "rating",
+    "experience",
+    "recent"
+  ],
+  "type": "string"
+}

First observedFeb 25, 2026

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and reveals multiple behavioral details: default radius 30km, response includes totalCount and resolvedLocation, contact info requires get_human_profile (registered agent needed), and geocoding behavior for location. It also mentions currency conversion for max_rate, making the tool's behavior more predictable.

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 a single dense paragraph but logically structured: purpose, returns, key filters, defaults, response extras, contact info, workflow. Every sentence adds actionable value without fluff, making it highly scannable.

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 search tool with 25 parameters and no output schema, the description covers the essential behavioral contract: what fields are returned, how filters combine, defaults, the workflow with related tools, and the requirement for get_human_profile to access contact info. It also explains the resolvedLocation verification, making it practically complete for invocation decisions.

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?

The input schema already has 100% field coverage with descriptions, so the baseline is 3. The description adds strategic guidance above the schema, such as using min_completed_jobs=1 to find proven workers without a skill filter, fully-qualified location names for accurate geocoding, and default sort behavior. This elevates it to 4.

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 it searches for humans available for hire and lists the exact fields returned (id, name, skills, location, reputation, etc.). It also differentiates itself by providing the typical workflow link to get_human_profile and create_job_offer, making it distinct from the many get_* sibling tools.

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 explicit usage tips like 'All filters are optional — combine any or use none to browse' and highlights key filters (skill, location, min_completed_jobs) with examples. The typical workflow 'search_humans → get_human_profile → create_job_offer' indicates when this tool is the appropriate entry point, though it doesn't enumerate exclusions vs. alternatives.

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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search_humans

Input Schema

Schema Changelog

TDQS

Glama MCP Gateway

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