get_status
Check if your document is ready.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
Check if your document is ready.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds minimal context ('if your document is ready') but doesn't explain what 'ready' means or discuss response behavior. It's consistent with annotations, so not contradictory.
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?
The description is extremely short, which is concise, but it's under-specification rather than effective conciseness. It lacks essential details and doesn't earn its place as a useful description for an AI agent.
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?
With no output schema, the description should explain what a status response contains (e.g., states like processing/ready/error). It provides no information about return values, polling behavior, or how to interpret results, leaving the tool incomplete for practical use.
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 input schema includes job_id with no description, and schema description coverage is 0%. The description fails to explain the purpose or format of job_id, leaving the agent without any semantic guidance for this required parameter.
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?
The description states a clear verb ('check') and resource ('your document'), but ambiguously refers to 'document' rather than the job identified by job_id. It gives a general sense of status checking but doesn't explicitly say 'job status' or distinguish from sibling tools.
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?
No guidance is provided on when to use this tool versus alternatives, when to poll for status, or what conditions warrant calling it. The description doesn't mention any context for usage, such as after submitting a job.
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 are clearly separated by resource (document vs deck) and lifecycle stage (create, preview, status, download). generate_deck and get_research_deck both produce decks and could be confused, and preview_template overlaps with the preview step inside fill_template, but descriptions mostly clarify intent.
All tool names follow a consistent snake_case verb_noun pattern (create_, list_, get_, save_, download_). The verbs are simple and each noun clearly identifies the target resource, making the set predictable.
11 tools is well-scoped for a document and deck generation server. Each tool covers a distinct step in the workflow without excessive fragmentation.
The document lifecycle (template -> fill -> preview -> status -> download) and deck workflow (templates -> generate/research -> save) are well covered. Minor gaps include no template update/delete and no way to list or retrieve saved decks, but agents can complete core tasks.