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COTAL (cotal.ai)

Revise a staged article

cotal_update_post
Idempotent

Improves an article staged with cotal_draft_post. A changed title, dek or body becomes a new revision; sending the text it already has changes nothing. Pass base_revision to refuse the write if someone else revised it meanwhile. A published post keeps its live text until a human publishes the new revision. Requires a team key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dekNo
tagNo
bodyNoThe full new markdown body (not a diff).
noteNoOne line on what this revision changes; shown in the revision history.
slugYesThe post slug, e.g. 'mesh-in-production'.
titleNo
statusNopending submits it for human review.
restoreNoMake an earlier revision current again instead of sending new text.
new_slugNoRename the post's slug.
base_revisionNoThe revision you edited; the write is refused (409) if the post moved past it.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior5/5

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

Adds substantial behavior beyond the annotations: no-op-on-identical-text semantics (consistent with idempotentHint=true), 409 refusal when base_revision is stale, the rule that published posts keep their live text until a human publishes the revision, and the team-key auth requirement. That is exactly the non-obvious context an agent needs before calling.

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?

Five short sentences, front-loaded with what the tool does, then the revision semantics, then concurrency, then publish behavior, then the auth requirement. No sentence is filler.

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 10-parameter mutation tool with no output schema, it covers the important behavioral surface: idempotency, concurrency conflict, publish/revise split, and permission requirements. It leaves the restore and new_slug (rename) semantics unexplained, though those are partly covered by the schema descriptions.

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 70%, so most parameters already carry descriptions; the description reinforces base_revision's conflict role and clarifies that title/dek/body changes create a revision. It adds nothing for dek, tag, title, or the non-obvious restore/new_slug behaviors, so it neither fully compensates nor falls short of the schema 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 and resource ('Improves an article staged with cotal_draft_post') and anchors itself to the sibling that creates the artifact, so an agent can place it correctly relative to cotal_draft_post and the read tools 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 Guidelines4/5

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

Gives real usage conditions: it operates on posts already staged via cotal_draft_post, it no-ops when the text is unchanged, and it names base_revision as the guard for concurrent edits. It stops short of explicitly naming when to use a different sibling for a given need, but the scope is clear.

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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