get_conversion_statusCheck a conversion started by convert_document. Returns a download resource link when complete without exposing the internal job identifier.
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| status | Yes | ||
| fileName | No | ||
| mimeType | No | ||
| progress | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the operation as read-only, idempotent, and non-destructive. The description adds meaningful behavior beyond that: it returns a download link when complete and does not expose the internal job identifier. It does not mention pending or error states, but the annotations and output schema cover the safety profile.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence front-loads the action and resource, then efficiently packs output behavior and a privacy trait with no filler. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter, read-only polling tool with a full output schema and strong annotations, the description covers purpose, origin, and completion behavior. It could be more explicit about where jobId comes from and what the response looks like while the conversion is still running, but those are minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only defines jobId as a non-empty string and has 0% description coverage, so the description must carry meaning. It implies jobId identifies a conversion created by convert_document, but it never explicitly says 'pass the jobId returned by convert_document' or explains the format. This adds moderate value but leaves the source of jobId slightly implicit.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('Check') and a specific resource ('conversion started by convert_document'), clearly distinguishing it from the sibling convert_document tool. It also tells the agent what the result is: a download resource link once the conversion is complete.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly ties the tool to conversions started by convert_document, making the precondition clear: use this after starting a conversion there. It does not list exclusions or alternative tools, but the source-tool reference provides enough context for correct use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.