mindee_get_inferenceFetch the structured result of a completed inference by id, returned as a compact map of extracted fields (plus raw_text if it was requested). V2 API: GET /v2/inferences/{inference_id}.
| Name | Required | Description | Default |
|---|---|---|---|
| raw | No | Return the full unshaped API response instead of the compact fields map. Default false. | |
| inference_id | Yes | Inference id (from a Processed job's result_url, or from mindee_extract_document). |
Changes observed during successful MCP inspections.
Input schema / $schemaPrevious value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=true), and the description adds real value on top: the return shape (compact fields map plus raw_text when requested) and the underlying V2 endpoint. It stops short of mentioning auth needs, rate limits, or error behavior for non-completed inferences.
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?
Two tight sentences, front-loaded with the action and return shape, with the API reference appended as supporting detail. No filler.
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 usefully sketches what comes back (extracted fields map, optional raw_text) and where the id originates. It could say more about behavior on unfinished/invalid inferences, but for a simple read tool this is close to 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?
Schema description coverage is 100%, so both parameters are already documented, including the raw flag and where the id comes from. The description only alludes to 'raw_text if it was requested' and adds no syntax or format detail beyond the schema baseline.
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 and resource ('Fetch the structured result of a completed inference by id') and even names the return shape, which cleanly separates it from mindee_extract_document (initiates extraction) and mindee_get_job (polls status).
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?
Implies usage via 'completed inference' and the parameter note pointing to mindee_extract_document, but never explicitly states when to call this versus mindee_get_job or what to do if the job is still pending. Usage is inferable rather than stated.
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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