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AntonioBlago

VisiblyAI MCP Server

by AntonioBlago

submit_article_draft

Submit article text as a draft to VisiblyAI for SEO content queries, creating a new query or updating an existing one without queuing or spending credits.

Instructions

Hand your text over to visibly as an article draft (status draft, never queued). Credits: 0.

Needs an API key with the right content:write. Pass a stable idempotency_key when retrying; the response echoes the key used. format: html|markdown. Without query_id a new content query is created; with query_id keyword/country/language/persona and expected_query_revision must match.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes
formatNomarkdown
contentYes
countryNode
keywordYes
languageNode
query_idNo
persona_idNo
project_idYes
idempotency_keyNo
expected_query_revisionNo
expected_article_revisionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.6.1

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the auth scope required (content:write), the cost (Credits: 0), the non-queued draft status, idempotency behavior including that the response echoes the key, and the revision-matching constraint. It omits failure/conflict behavior for expected_article_revision.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads purpose, then states cost, auth, idempotency, format, and the conditional query logic in compact sentences. Dense but every clause carries operational information; 'Hand your text over to visibly' is slightly awkward but functional.

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?

Output schema exists so return values need not be described, and the description covers the highest-risk behaviors (auth, cost, idempotency, conditional query creation) for a 12-param mutation with no annotations. The main remaining gap is the unexplained expected_article_revision concurrency parameter.

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 0% across 12 parameters, so the description must compensate and it partially does: it explains format, idempotency_key, query_id, keyword/country/language/persona, and expected_query_revision semantics. It leaves title, content, project_id, persona_id, and notably expected_article_revision undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('submit... as an article draft') and adds the key scope constraint 'status draft, never queued', which separates it from publish_article and change_article_status. It is clear what the tool does, though it does not explicitly name sibling alternatives.

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?

Provides conditional usage guidance: pass a stable idempotency_key when retrying, and explains the query_id-present vs absent branches ('Without query_id a new content query is created; with query_id ... must match'). It stops short of routing the agent to named alternatives for publishing or optimization.

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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