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callable

by io.github.CCSTT-dev

submit_task

Submit an account verification task to a real human operator. Use this when your agent is blocked on something only a real person can complete — a KYC check, a platform sign-in or 2FA prompt, a CAPTCHA, an identity confirmation, or any verification step that requires a human. The task is charged automatically via Stripe ($39). A vetted operator claims it, performs the verification on real hardware, and returns the result (with optional screenshot) to your callback_url or via get_task_status. Typical completion: 30 minutes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
task_typeYesaccount_verification — a real human verifies an account, KYC check, sign-in, or CAPTCHA on real hardware and returns structured proof ($39).
callback_urlNoWebhook URL to receive a POST request with the task result when completed. Recommended for async agent workflows. If omitted, poll get_task_status instead.
instructionsYesStructured input for account_verification. Required fields: platform (e.g. 'gmail', 'stripe'), url (where to perform the verification), credentials (login info or how to obtain the code), what_to_verify (what the operator must confirm). Optional: screenshot_required (boolean, default true), additional_notes.
deadline_minutesYesTime in minutes the operator has to complete the verification after claiming it. Minimum 20. Recommended: 30.
payment_credentialNoStripe Link Agent Wallet Single-use Payment Token (SPT) for fully autonomous agent payments with no human intervention required. Omit if the developer account has a saved card on file.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYesCurrent task status
messageNoConfirmation or error message
task_idYesUnique identifier for the submitted task
estimated_completionNoEstimated completion time based on deadline_minutes

TDQS

A4.1/5.0
Behavior4/5

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

Beyond annotations (readOnlyHint=false etc.), description discloses automatic Stripe charge ($39), human operator workflow, async result delivery via callback or get_task_status, and typical 30-minute completion. This adds meaningful side-effect and latency context.

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?

Starts with a one-sentence purpose, followed by when-to-use list and process details. It is compact and information-dense, though slightly longer than strictly necessary.

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?

Combined with a rich schema and output schema, the description covers trigger conditions, cost, execution flow, delivery method, and timing. No critical gaps for an agent deciding to submit a human-verification task.

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?

Input schema has 100% parameter description coverage, including nested `instructions` and `payment_credential`. The description only adds the overall cost and result-delivery flow, not new per-parameter meaning, so 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?

Description opens with a specific verb+resource ('Submit ... account verification task to a real human operator') and enumerates exact use cases (KYC, sign-in, CAPTCHA). This clearly distinguishes it from siblings get_task_status and review_task.

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?

Explicitly states when to use ('when your agent is blocked on something only a real person can complete') and gives concrete examples. It does not provide explicit exclusions or contrast with review_task, but the guidance is unambiguous.

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

A4.5/5.0
Disambiguation5/5

Each tool serves a distinct stage in the task lifecycle: submit creates a task, get_task_status polls it, and review acts on the pending_review outcome. There is no overlap or ambiguity between them.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (submit_task, get_task_status, review_task) with clear, specific verbs and the same domain object. This is a model of naming consistency.

Tool Count5/5

Three tools is the ideal number for this server's narrow purpose—submitting, monitoring, and reviewing human tasks. Each tool is necessary and there are no redundant or missing tools.

Completeness5/5

The toolset fully covers the workflow: task creation, status polling, and final approval/dispute resolution. The documented auto-approval mechanism fills any edge case, leaving no obvious functional gaps.

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