Markdown by mdedit.ai

Accept a review suggestion

accept_suggestion

Destructive

Atomically apply an existing review suggestion to the document and mark it accepted. Use the exact suggestionId and targetId returned by add_suggestion or list_review_threads.

Input Schema

TableJSON Schema

Name	Required	Description
`targetId`	No
`articleId`	No	Internal document identifier when already available. Omit it when the user referred to the document by title.
`commandId`	No
`workspaceId`	No	Internal workspace identifier when already available. Omit it when the user referred to a workspace by name or has only one workspace.
`articleTitle`	No	Human-visible document title. Prefer this over asking the user for an internal document identifier.
`suggestionId`	Yes
`workspaceName`	No	Human-visible workspace name. Prefer this over asking the user for an internal workspace identifier.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`events`	Yes
`applied`	Yes
`articleId`	Yes
`commandId`	Yes
`workspaceId`	Yes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedAug 28, 2026

TDQS

A4.1/5.0

Behavior4/5

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

The description adds behavioral detail beyond the annotations by stating that the operation is atomic and that it marks the suggestion as accepted. Since annotations already flag the operation as destructive and non-idempotent, the added atomicity and state-change context is valuable and non-redundant.

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 sentences with no filler. The core behavior is stated first, followed by the most important data-provenance rule. Every sentence contributes directly to correct invocation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has seven parameters, only one is required, and an output schema exists, the description is mostly adequate. The main gap is that commandId has no explanation anywhere, and the description does not clarify why targetId is mentioned even though it is not a required parameter. Still, the core calling pattern is sufficiently conveyed.

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 57%, and the description adds meaning for suggestionId and targetId by emphasizing they must be exact values returned by prior calls. However, commandId remains undocumented in both the schema and description, leaving an unclear role for a parameter in a destructive, non-idempotent operation.

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 clearly states the specific action: atomically apply an existing review suggestion to the document and mark it accepted. It also distinguishes the operation from related sibling tools by focusing on existing suggestions and the acceptance outcome.

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?

The description provides clear context: this tool is for applying an already-created suggestion, not for creating or rejecting one. It also instructs to use the exact suggestionId and targetId returned by add_suggestion or list_review_threads, giving an agent concrete data provenance guidance.

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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TDQS

A3.6/5.0

Disambiguation4/5

Most tools have clearly distinct purposes across article CRUD, review workflows, publishing, and conversion/export. Minor confusion can arise between convert_document and start_article_export (and their corresponding status checkers), but the descriptions explicitly differentiate arbitrary content conversion from exporting stored documents. The render_article tool's broad contextual wording overlaps conceptually with several actions, though its actual function (opening the reader) is unique.

Naming Consistency5/5

All 22 tool names follow a strict snake_case verb_noun pattern, such as list_articles, accept_suggestion, start_article_export, and unpublish_article. There are no mixed conventions, vague verbs, or camelCase outliers, making the naming highly predictable.

Tool Count3/5

At 22 tools, the server sits above the ideal 3-15 range and feels somewhat heavy for an MCP surface. The inclusion of record_article_app_event, a component-only telemetry tool with no agent-facing value, adds to this heaviness. However, the breadth of the domain—documents, collaboration, publishing, and conversion—largely justifies the count.

Completeness3/5

The tool surface covers article lifecycle, review threads, publishing, export/conversion, and presence well, covering most common workflows. A notable gap is the lack of any delete_article or trash operation, leaving no way to remove documents. Additionally, updates are limited to anchored edits via edit_article, with no direct full-content replacement option.

Resources

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