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velrim_extract

Extract structured data from a document (PDF or image, passed as document_base64 or as the upload_key of a staged upload) against a JSON Schema you supply. Returns a typed object and, for every field, a state (present, null, or missing), a calibrated confidence score, and an anchor (the source page and bounding box). The confidence is calibrated against published reliability curves (https://velrim.com/reliability), so a 0.9 means the field is right about 90% of the time on that document class — branch on it directly: act on a field at or above your accept threshold, escalate the fields below it and any that are missing. Use each field's anchor to check it against the source page.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hintsNoDiscriminator hints for union schemas.
schemaYesThe JSON Schema (draft 2020-12) the extracted object must conform to (required).
doc_classNoOpaque tag (<=128 chars) echoed back in meta.doc_class.
upload_keyNoThe upload_key of a staged Velrim upload. Mutually exclusive with document_base64.
document_base64NoThe document bytes, base64-encoded. Provide exactly one of document_base64 or upload_key.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
metaNo
errorNo
fieldsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Goes well beyond the annotations (readOnlyHint=false, openWorldHint=true, idempotentHint=false) by disclosing the return contract: a typed object with per-field state, calibrated confidence, and source anchors. The calibration claim is even backed by a reference URL, so the 0.9 semantics are defined rather than asserted.

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?

Three front-loaded sentences: what it does, what it returns, and how to use the return. Every sentence carries distinct information and none restates the name or schema.

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

Completeness5/5

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

An output schema exists, yet the description still supplies the non-obvious behavioral facts an agent needs (confidence calibration, per-field state, anchors). Input modes, required schema, and the escalation workflow are all covered, leaving no material gap for calling it correctly.

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 100%, so the schema already documents all five parameters including the required 'schema' object and the mutually exclusive base64/upload_key pair. The description reinforces the document-input mechanism but adds no syntax or format detail beyond the schema, so baseline 3 applies.

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?

States a specific verb and resource ('Extract structured data from a document') and immediately distinguishes the two supported input modes (PDF/image via document_base64 or upload_key). An agent can tell this apart from the sibling velrim_job_status, which is a polling tool.

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?

Gives concrete operational guidance on when to act versus escalate ('act on a field at or above your accept threshold, escalate the fields below it and any that are missing'), plus the input-mode choice. It never names the sibling or an alternative tool or an explicit when-not, so it falls short of full routing 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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