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Create a draft

create_draft
Destructive

Step two: generate an application draft for a call. It uses one draft from the monthly allowance, so call it only after the user agreed to what prepare_draft returned. It never applies: the user reviews the draft and applies at the official source.

Input Schema

NameRequiredDescriptionDefault
idYesThe call's id, as returned by search_open_calls or list_my_matches, for example ted-728925-2026.
confirmationTokenYesThe token prepare_draft returned for this call. It works once, after the user agreed.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / confirmationToken / description
      Added value: +"The token prepare_draft returned for this call. It works once, after the user agreed."
    • addedInput schema / properties / id / description
      Added value: +"The call's id, as returned by search_open_calls or list_my_matches, for example ted-728925-2026."
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Adds real context beyond the annotations: it consumes one unit from a monthly allowance and deliberately stops short of submission, which explains why destructiveHint=true (quota consumption) despite nothing being applied. It does not say whether a consumed draft can be reclaimed or what the returned draft contains, leaving a modest gap.

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?

Three short sentences, front-loaded with the workflow position and the gating condition, with zero redundant restatement of the tool name. Every clause carries a distinct constraint an agent needs.

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 two-parameter mutation with annotations but no output schema, the description covers the sequencing, the precondition, the quota cost, and the non-applying scope. The only shortfall is that the shape of the returned draft is implied rather than stated.

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 100% and both parameters are well documented in the schema, including the provenance of id and the single-use nature of confirmationToken. The description adds workflow framing but no additional syntax or format detail, so baseline 3 applies.

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 and resource ('generate an application draft for a call') and situates it in a workflow as 'Step two', explicitly paired with prepare_draft as the preceding step. An agent can distinguish this from prepare_draft and from listing siblings like list_my_drafts without opening a schema.

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

Usage Guidelines5/5

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

Gives an explicit precondition ('call it only after the user agreed to what prepare_draft returned'), a cost constraint ('uses one draft from the monthly allowance'), and a boundary ('it never applies: the user reviews the draft and applies at the official source'). This is close to complete when/where 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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