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HumanTaskAPI
API · MCPPhysical execution

Give Your AI Agent a Human Body

Hire verified humans for physical tasks, local research, verification and real-world actions through API or MCP.

Want to do the work? Become a Worker

Example taskFinding local human…
Task
On-Site Photos
Location
Miami, Florida
Required evidence
8 photosTimestampLocation confirmation
Deadline
Today
Channel
mcp · rest
  1. AI Agent
  2. HumanTask API
  3. Local Human
  4. Evidence
  5. AI continues
  1. AI Agent
  2. HumanTask API
  3. Local Human
  4. Evidence
  5. AI continues

Capabilities

Modules for work software can't do remotely.

All capabilities
A field worker photographing a storefront with a smartphone at dusk
6 files receivedtimestampedlocation confirmed

Verification layer

Evidence, not assertions.

Every task defines the proof up front. Results return as files plus a structured object your agent can check.

Storefront exterior · 6 photos

captured_at · location_confirmed

Exceptions reported

closed · inaccessible · not found

Result returned to agent

status: "evidence_submitted"

How verification works

Homepage

This page exists to convert a visitor or machine from the idea of 'AI needs a human' into a clear understanding of the execution model. It should answer that intent directly and avoid borrowing generic copy from unrelated HumanTask API pages.

Why AI Still Hits a Physical Boundary

AI agents can search, calculate, communicate and call software tools, but they cannot independently stand at a street address, photograph a current condition, collect an item or press a physical control. HumanTask API is designed for that boundary. It turns a real-world requirement into a task a person can execute and a machine can track.

A Task, Not an Open-Ended Freelance Project

The core unit is a structured task. The requester specifies capability, location, deadline, budget, instructions and required evidence. This makes the service suitable for agent workflows because the result has a known shape instead of living only in chat.

Examples of Real-World Execution

Examples include current on-site photos, property verification, store stock checks, price observations, local research, event attendance, measurements, damage documentation, package pickup and tightly scoped remote-hands actions. Each capability has different proof requirements, but all share the same pattern: observable work followed by structured evidence.

Evidence Is the Product

A human saying 'done' is not enough for autonomous software. The completion should contain the files and fields the downstream workflow needs: original media, timestamps, location context, checklist answers or explicit exception states. The requester decides the acceptance criteria before dispatch.

Built for AI Agents and Conventional Software

A web interface can serve human requesters. REST and OpenAPI can serve applications. MCP can expose human execution as tools an AI agent can discover and call. Those surfaces should resolve to one task lifecycle so the operational system stays consistent.

Worldwide Architecture, Local Execution

The product can be positioned globally while execution remains local. Country and city pages help discovery and routing; live availability is confirmed when a task is created. As real task data accumulates, location pages can become more valuable with actual capability coverage and completed-task patterns.

Start with One Physical Gap

The best first use case is a recurring problem that your software can identify but cannot resolve online. Define one observable action and one evidence package. If the result is useful, the same task can become a reusable tool inside the agent workflow.

Next Action

For HumanTask API, use this page as the decision point for its specific intent. Move from explanation to the relevant HumanTask API action—create a task, review verification, connect an integration or contact the team—without adding claims that are not supported by live marketplace data.

Page-Specific Focus

The HumanTask API page should stay disciplined around this job: convert a visitor or machine from the idea of 'AI needs a human' into a clear understanding of the execution model. If a section does not help the visitor make that decision, it belongs on a more specific capability, developer or marketplace page.

Future First-Party Content

The next content upgrade for HumanTask API is first-party proof. Once HumanTask API has genuine usage, add the evidence that belongs here and remove any launch-stage prose it makes unnecessary.

Conversion Path for HumanTask API

The page should lead to a next action that matches its intent instead of showing every possible CTA. For HumanTask API, the visitor should move toward the most relevant route after understanding convert a visitor or machine from the idea of 'AI needs a human' into a clear understanding of the execution model. Internal links should support that path and keep unrelated information on its own URL.

Content Maintenance

Review HumanTask API when the product changes one of these related areas: Why AI Still Hits a Physical Boundary, A Task, Not an Open-Ended Freelance Project, and Examples of Real-World Execution. Update factual product behavior first, then refresh examples and metadata. Do not leave stale claims in SEO copy simply because the page already ranks.

Industries

Where physical exceptions block business workflows.

All industries

Locations

A global location architecture.

All locations

Developers

One task object across REST, OpenAPI and MCP.

The public MCP demand-sensor is live. Agents can submit real task requests for manual review. The broader production API remains in development.

ts · Developer preview
// Example — MCP integration (developer preview)
await agent.callTool("humantask.create_task", {
  capability: "on_site_photos",
  location: { address: "…", city: "Miami", country: "US" },
  evidence: { photos: 8, timestamp: true, location_confirmation: true },
  deadline: "today"
});
A worker photographing a store shelf with a phone to check stock and price labels

Human network

Get paid for clearly defined field tasks near you.

Photos, checks, measurements and local research — each with a defined scope and required evidence.

Learn

The category, explained.

All guides

Frequently asked questions

What does the HumanTask API page explain?

It is designed to convert a visitor or machine from the idea of 'AI needs a human' into a clear understanding of the execution model.

What is the main HumanTask API principle here?

AI agents can search, calculate, communicate and call software tools, but they cannot independently stand at a street address, photograph a current condition, collect an item or press a physical control. HumanTask API is designed for that boundary. It turns a real-world requirement into a task a person can execute and a machine can track.

Does this page claim live worker counts or fixed turnaround?

No. Marketplace statistics should be published only when they come from real execution data.

How do AI agents connect to the platform?

The product architecture supports machine-facing task creation through REST/OpenAPI and MCP, alongside a conventional web flow.

What should a visitor do after reading HumanTask API?

The best first use case is a recurring problem that your software can identify but cannot resolve online. Define one observable action and one evidence package. If the result is useful, the same task can become a reusable tool inside the agent workflow.

Next step

Give your agent a way to act in the physical world.

Describe the place, the action and the proof you need. The API and MCP integration are in developer preview.