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Save Talonic Schema

talonic_save_schema

Save a schema definition to your workspace for reuse across multiple document extractions, standardizing extraction for consistent results.

Instructions

STATUS: stable.

Save a schema definition to the user's Talonic workspace so it can be reused across future extractions. Returns the saved schema with its newly assigned id and short_id.

USE WHEN:

  • The user asks to save a schema, store a template, or reuse the schema across docs.

  • You have iterated on a schema with the user and they confirmed it should be saved.

  • The user wants to standardise extraction across many documents of the same type.

DO NOT USE WHEN:

  • The user just wants to extract once with an inline schema (call talonic_extract directly with the schema inline).

  • The user has not confirmed the schema design (avoid creating clutter in their workspace).

DEFINITION FORMATS:

  • JSON Schema (most reliable): { type: "object", properties: { vendor_name: { type: "string" } } }

  • Flat key-type map: { vendor_name: "string", invoice_total: "number" } -- API normalises server-side. If you get a "no fields" error from the API, fall back to JSON Schema.

TIP: After saving, call talonic_extract with schema_id set to the returned id (UUID or SCH- short id) for consistent results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesHuman-readable name for the schema, e.g. 'Standard Invoice'.
definitionYesSchema definition. Most reliable: full JSON Schema {type:'object', properties:{...}}. Also accepted: a flat key-type map {field_name:'string', amount:'number'} which the API normalises.
descriptionNoOptional description of what this schema extracts and when to use it.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesUUID of the newly saved schema.
short_idNoHuman-readable short id (SCH-XXXXXXXX).
nameYes
descriptionNoSchema description, or null when the schema was saved without one. The API explicitly maps the absent case to null (see SchemaResponse in openapi.yaml).
definitionNoFinal schema definition as stored, normalised by the API.
field_countNo
versionNoSchema version (1 for new schemas; increments on update).
created_atNo
updated_atNo
linksNo
Behavior4/5

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

Annotations already indicate read/write and non-destructive nature. Description adds useful behavioral context: status 'stable', return of id and short_id, API normalization behavior, and a fallback hint for error. No contradictions.

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?

The description is well-structured and concise. It starts with status and core purpose, then usage guidelines, definition formats, and a tip. Every sentence adds value with no redundancy.

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?

Given the tool's complexity (3 params, output schema exists), the description is complete. It covers status, purpose, return value, usage guidelines, definition formats with examples, error recovery, and a tip. No gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline 3. Description adds significant extra value beyond schema by detailing definition formats (JSON Schema vs flat map), providing examples, explaining normalization, and offering a fallback tip. This elevates the score.

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?

The description clearly states the action ('save a schema definition'), the resource ('schema to workspace'), and the purpose ('reuse across future extractions'). It distinguishes from siblings like talonic_extract which uses inline schemas, and mentions the return value (new id and short_id).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit 'USE WHEN' and 'DO NOT USE WHEN' sections provide clear context. It specifies when to save (user wants reuse, confirmed schema) and when not to (one-time extraction, user not confirmed), and suggests talonic_extract as alternative.

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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