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workflows

workflows
Read-only

Durable workflows for agents (Cloudflare Workflows): up to 25 steps of HTTP calls (automatic retries), sleeps of up to 30 days, and human approval gates, with a callback when done. Survives restarts; pass the previous response into the next step with {{prev}}. $0.005 + $0.001 per step; status/approve/reject $0.002. Costs $0.03 USDC per call (x402).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stepsNo[{type: http, url, method, body} | {type: sleep, seconds} | {type: approval, timeout_hours, notify_url}]
tokenNo
actionNocreate (default), status, approve, reject, cancel
workflow_idNo
callback_urlNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.1/5.0
Behavior1/5

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

The description describes an executing, state-changing system — HTTP calls with retries, sleeps up to 30 days, approval gates, create/approve/reject/cancel actions — while annotations declare readOnlyHint=true. An agent relying on the annotation would wrongly treat this as a safe non-mutating read, making the description and annotations inconsistent.

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?

Three dense sentences, front-loaded with the core capability (durable workflows, step limits, sleeps, approvals, callback) before mechanics and pricing. Every clause carries information; the pricing figures are crammed but not wasteful.

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?

For a complex orchestration tool with no output schema and 40% parameter coverage, the description covers limits, retries, persistence, templating, callback and cost well. It still omits what create returns (workflow_id?), how status polling works, and any auth/payment prerequisite detail, but the essentials for calling it are present.

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?

Schema description coverage is only 40% (token, workflow_id, callback_url are bare), so the description has to carry weight. It adds genuinely new information — {{prev}} templating to pass a prior response into the next step, step-type semantics, and per-action pricing — but it never explains what token (x402 payment) or workflow_id are for, leaving meaningful gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource and capability set ('Durable workflows for agents (Cloudflare Workflows)' with HTTP steps, sleeps, approval gates, callbacks) — far more than a tautology. It does not explicitly differentiate itself from near-neighbors like schedule, queue, tasks, or cron_explain, so an agent must infer the distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied through the capability inventory: multi-step, long-running, restart-surviving jobs with human-in-the-loop gates. However, there is no explicit 'use this when… / use X instead' guidance, and the five actions (create/status/approve/reject/cancel) are only hinted at via the pricing sentence rather than laid out as a workflow lifecycle.

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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