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Regenerate a draft's text

regenerate_draft
Destructive

Re-draft one draft's story text (optionally steering with an angle), resetting it to pending and invalidating its cached render. Capped per tier: Free 1/day per draft, Pro 5/day.

Input Schema

NameRequiredDescriptionDefault
angleNo
handleNoInstagram handle (with or without @). Omit for your default account.
contextYesExplain why you are calling this tool and how it fits into the business owner's goal for their Instagram presence. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include personal data, credentials, or the text of a draft. Example (20 words): "Reviewing the pending drafts before approving today's story, checking the caption matches the photo the owner expects."
draft_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true and readOnlyHint=false, so safety is partially covered. The description adds genuine value beyond that: it discloses that the draft is reset to pending, its cached render is invalidated, and the per-tier rate limits (1/day Free, 5/day Pro). It does not say whether the prior text is recoverable or what happens when the cap is exceeded.

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?

Two tightly packed sentences with zero filler; the core operation and its side effects lead, and the rate limits close the sentence as supporting constraint. Every clause earns its place.

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 mutation tool with instructive annotations and no output schema, the description covers the key behavioral facts an agent needs (text replaced, status reset, cache invalidated, caps). It stops short of stating recovery behavior, cap-exceeded handling, or the handle parameter's role.

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 50%; the context and handle parameters are documented in the schema, while angle and draft_id are not. The description clarifies that angle is optional steering and hints at draft_id's role, but adds no semantics for handle and no detail on how angle values map to output. Only partial compensation for the coverage gap.

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?

The description states a specific verb+resource: re-drafting an existing draft's story text, optionally with an angle. The phrase 'one draft's story text' and the reset-to-pending side effect clearly distinguish this from generate_draft (new) and update_draft (field edits).

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?

It implies usage through the tier cap ('Free 1/day per draft, Pro 5/day'), which tells the agent this is a rate-limited operation, but never states when to prefer this over update_draft or generate_draft, nor any exclusions. Usage context is inferable but not explicit.

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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