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AutomateLab Content Distribution MCP

Retract a published post (best-effort)

post_unpublish
Destructive

Best-effort delete of a published post on the target platform. Side effects: makes an external HTTP DELETE or update request; DEV.to sets published=false (soft delete); platforms without a delete API return success=false without error. Non-idempotent — calling on an already-deleted URL may return a platform 404. Use post_unpublish to retract a live post; use post_status first to obtain the live_url; use post_publish to re-publish after an unpublish.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channelYesChannel slug the post was published to, e.g. 'devto', 'hashnode', 'reddit:ClaudeAI'.
live_urlYesURL of the live published post to retract, e.g. 'https://dev.to/user/post-slug'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorYesPlatform error message; null on success.
successYesWhether the retract succeeded on the platform side.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare destructive, non-idempotent, and open-world, but the description adds substantial context beyond them: it describes the actual side effect (external HTTP DELETE or update request), platform-specific behavior (DEV.to soft-deletes via published=false; platforms lacking a delete API return success=false without error), and the non-idempotent 404 case. This is exactly the behavioral detail structured fields cannot convey.

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?

Front-loaded with the core action, then side effects, then usage routing. Every clause carries distinct information — no filler, no repetition of the title or schema.

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?

An output schema exists, so return-value explanation is unnecessary; the description instead covers the risk profile an agent needs (soft delete, silent success=false, 404 on repeat). Complete for a destructive cross-platform 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 100% with clear per-parameter descriptions (channel slug, live_url format), so the schema already carries parameter meaning. The description references live_url via post_status but adds no syntax or format detail beyond the schema, making the baseline 3 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?

States a specific verb and resource ('Best-effort delete of a published post on the target platform') and immediately frames the scope as retracting a live post. An agent can distinguish it from post_publish, post_status, and post_schedule without opening any 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?

Explicitly routes the agent: use this to retract a live post, use post_status first to obtain the live_url, and use post_publish to re-publish afterward. It names both the prerequisite and the reverse operation, leaving nothing to inference.

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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