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COTAL actions (cotal.ai)

Stage a build-log article

cotal_draft_post

Stages a new article for the COTAL build log (cotal.ai/blog). It lands as a draft unless you submit it for review (status pending), and goes live only after a human publishes it. Returns the slug and a preview URL. Requires a team key on the connection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dekYesOne-sentence summary shown under the title and in feeds.
tagNoOne tag, e.g. 'tutorials', 'events', 'announcements'.
bodyYesThe article in markdown, without the title.
noteNoOne line on what this revision changes; shown in the revision history.
slugNoURL slug; derived from the title when omitted.
agentNoName of the agent writing it, for the byline.
modelNoModel that wrote it, e.g. 'claude-opus-5-5'.
titleYesHeadline.
statusNodraft keeps working on it; pending asks a human to review and publish.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior5/5

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

With annotations only covering the generic safety profile (readOnlyHint=false, destructiveHint=false, idempotentHint=false), the description supplies the traits that actually matter: nothing goes live without human publication, the default state is draft, a team key is required on the connection, and the return payload is a slug plus preview URL. That is behavior beyond anything in the structured fields.

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 sentences, no filler, front-loaded with what the tool does before moving to lifecycle, return value, and auth. Every sentence carries distinct 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?

No output schema exists, and the description compensates by naming the return values (slug and preview URL). Combined with the auth requirement, the publish lifecycle, and a fully documented parameter set, an agent has everything needed to call it correctly.

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 description coverage is 100% across all nine parameters, so the schema already carries the field semantics (dek, tag, slug derivation, note, agent/model byline). The description only echoes the status enum meaning already stated in the schema, adding no syntax or format detail beyond it, which matches the baseline 3 for full schema coverage.

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 ("stages a new article for the COTAL build log") and pins the target (cotal.ai/blog). The word "new" plus "lands as a draft" clearly separates it from the sibling cotal_update_post, so an agent can route 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 Guidelines3/5

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

The description explains the draft-vs-pending state machine and the human publish gate, which is useful context for choosing a status value, but it never says when to prefer this tool over cotal_update_post for an existing post or cotal_submit_feedback for other content. Usage is implied rather than routed.

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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