mindee_get_jobCheck the processing status of an enqueued document by job id. Status is Waiting | Processing | Processed | Failed. When Processed, the response carries a result_url; pass that inference id to mindee_get_inference. V2 API: GET /v2/jobs/{job_id}.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | Job id returned by mindee_extract_document (when it times out) or an enqueue call. |
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 establish readOnlyHint=true and openWorldHint=true. The description adds significant behavioral context: it enumerates the possible statuses (Waiting, Processing, Processed, Failed) and explains that a Processed response includes a result_url, which is a key behavioral detail not present in the annotations.
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 concise and well-structured: a one-sentence purpose, a list of status values, a note on the result_url, and the API endpoint. Every sentence adds value and there is zero redundant information.
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 simple one-parameter status tool with no output schema, the description covers the essential workflow: statuses, the result_url on Processed, and the next tool to use. It does not specify error handling or response structure for non-Processed statuses, but given the read-only nature and single parameter, it is sufficiently complete.
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 description covers job_id fully, explaining it is returned by mindee_extract_document or an enqueue call. The tool description only restates 'by job id' and includes it in the URL pattern, adding no extra parameter semantics beyond what the schema already provides.
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 clearly states the tool's function: 'Check the processing status of an enqueued document by job id.' It distinguishes itself from siblings by explaining the relationship to mindee_get_inference, which is used after processing completes, making the purpose distinct.
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?
The description gives context on when to use this tool (to poll status after enqueue) and explicitly names the alternative for the next step (mindee_get_inference). It does not explicitly list exclusions, but the workflow guidance is clear enough for an agent to select appropriately.
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.
Each tool has a clearly distinct purpose: extracting documents, fetching inference results, checking job status, and listing available models. There is no overlap or ambiguity between them.
All tool names follow a consistent pattern: the 'mindee_' prefix followed by verb_noun (extract_document, get_inference, get_job, list_models). This makes the set predictable and easy to navigate.
With only 4 tools, the server is tightly scoped to the essential Mindee operations: submit, poll, retrieve, and explore models. Each tool earns its place, and the count is ideal for this purpose.
The tool set covers the full document extraction lifecycle: listing models, enqueuing extraction (with built-in polling), checking job status for long-running requests, and fetching the final inference result. No critical operation is missing.