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CommonTape

by com.commontape
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cancel

Cancel your own held, waiting (pending_approval) or booked job and release the slot. Optional reason: customer_changed_plans, customer_booked_elsewhere, booked_in_error, customer_other. The answer carries cancellation (by, at, reason, fee null, policy, capacity_released): CommonTape takes no cancellation fee. Already cancelled answers ALREADY_CANCELLED (with recovery when the business called it off).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "transaction_id": "<transaction_id>",
      "idempotency_key": "<idempotency_key>"
    },
    "name": "cancel"
  }
}

Input Schema

NameRequiredDescriptionDefault
reasonNo
transaction_idYestransaction_id from quote or hold
idempotency_keyYesUnique per write request (a UUID). Retrying with the same key returns the original result.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does well: it discloses no cancellation fee, slot/capacity release, idempotent write semantics, and the ALREADY_CANCELLED terminal state with recovery behavior. It stops short of covering auth/permission requirements or failure modes like a non-existent transaction.

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?

The purpose and scope are front-loaded in the first sentence, followed by the reason enum and return-shape details. It is dense and slightly run-on in the trailing clauses but every sentence carries information.

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?

There is no output schema, yet the description enumerates the returned cancellation fields (by, at, reason, fee, policy, capacity_released) and the already-cancelled outcome. Combined with the state coverage, an agent has everything needed to invoke and interpret this tool.

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 coverage is 67% and the reason enum values are already fully enumerated in the schema, so the description's reason list is redundant. It does not add format or constraint detail for transaction_id or idempotency_key beyond the schema, so this lands at the baseline.

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?

States a specific verb (cancel) plus the exact resource states it applies to (held, waiting/pending_approval, booked) and adds the scope qualifier 'your own' and the side effect 'release the slot'. This clearly separates it from siblings like modify or book.

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 establishes the condition under which cancellation is valid (own held/waiting/booked job) and lists the allowed reasons, giving a clear context for use. It does not name an alternative such as modify for rescheduling, so there is no explicit when-not guidance.

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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