Extend MCP

Create an extractor

create_extractor

Create a saved, reusable extractor (extract group). Three starting points, mutually exclusive: config (inline schema — call get_documentation with https://docs.extend.ai/extraction/schema.md BEFORE writing one by hand), cloneExtractorId (copy another extractor's draft config), or generate (Extend writes the schema from 1-5 sample documents plus optional instructions; no docs needed); name alone creates an empty draft. The draft is the only mutable surface — edit it with update_extractor, freeze it with publish_extractor_version, run it with extract_data. Follow any llmContext guidance included in results.

Input Schema

TableJSON Schema

Name	Required	Description
`name`	Yes	Display name for the extractor.
`config`	No	Inline extraction config: { schema?, extractionRules?, baseProcessor?, advancedOptions?, parseConfig? }. Writing schema by hand? ALWAYS call get_documentation with https://docs.extend.ai/extraction/schema.md FIRST and follow the returned dialect — the rules below are only a summary (field-naming best practices: https://docs.extend.ai/extraction/best-practices/field-names-and-prompt-crafting.md). schema is a JSON Schema: root "type": "object"; primitives nullable via a type array (["string","null"]); objects/arrays keep a plain "type" (never a nullable array) and objects always need "properties"; max depth 5; enums include null; no anyOf/oneOf/allOf/patterns. Date/currency/signature fields add "extend:type" alongside a normal type. A currency field is exactly: { "type": "object", "extend:type": "currency", "properties": { "amount": { "type": ["number", "null"] }, "iso_4217_currency_code": { "type": ["string", "null"] } } } — never a bare number. Omit schema for schema-less mode (no docs needed): extractionRules then doubles as schema-generation instructions.
`generate`	No	Auto-generate the schema from sample documents.
`environment`	Yes	"TEST" = the Test (development) environment, "PRODUCTION" = live. Must match a granted target from get_me (an API key pins one environment).
`workspaceId`	Yes	Target workspace (ws_...). Must be a granted workspace — get_me lists the accepted values.
`cloneExtractorId`	No	Existing extractor (ex_...) whose draft config to copy.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes
`name`	Yes
`createdAt`	No
`updatedAt`	No
`draftVersion`	No

TDQS

A4.9/5.0

Behavior5/5

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

Annotations only signal general side-effect hints (not read-only, not idempotent, not destructive); the description adds the key behavioral model: creation always results in a draft, the draft is the only mutable surface, and the three starting points are mutually exclusive. It also introduces the custom llmContext guidance behavior, which is not visible in the schema.

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 dense but tightly written: the first sentence front-loads the three mutually exclusive modes, and later sentences cover the lifecycle and a non-obvious result behavior. Every sentence earns its place for a tool with six parameters and nested objects.

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?

The description captures the creation modes, required prerequisites for hand-written schemas, and the follow-up workflow; with a rich input schema, output schema, and annotations already covering the rest, nothing critical is missing. It even mentions the unusual llmContext instruction behavior that an agent would otherwise miss.

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?

With 100% schema coverage, the schema already documents each parameter, so the baseline is 3. The description adds relational semantics not in the schema: config/cloneExtractorId/generate are mutually exclusive, name alone yields an empty draft, and generate requires no schema docs. This is genuinely useful supplementary meaning, but the description still leans on the schema for individual property details.

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 opens with a specific verb and resource: 'Create a saved, reusable extractor (extract group).' It distinguishes this tool from sibling create_* tools by naming the extractor domain and the three creation modes, so an agent can tell create_extractor from create_classifier or create_splitter without opening schemas.

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?

The description explicitly covers when to use each mode: config with get_documentation first, cloneExtractorId for copying, generate for sample documents, and name alone for an empty draft. It then routes the agent through the lifecycle (update_extractor, publish_extractor_version, extract_data), which prevents misuse and makes the tool's role in the workflow explicit.

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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TDQS

A4/5.0

Disambiguation5/5

Each tool targets a distinct resource+action combination, and the descriptions actively disambiguate potential overlaps (e.g., extract_data vs parse_document, detect_form_fields vs edit_pdf, get_file vs get_file_upload). The consistent verb_noun prefix pattern makes the semantic boundary of every tool immediately recognizable.

Naming Consistency4/5

The dominant verb_noun pattern is highly consistent across all nine domains (list_*, get_*, create_*, update_*, delete_*, run_*, get_*_run, get_*_batch, publish_*_version). Minor deviations exist: deploy_workflow_version vs publish_*_version for the same freeze-a-draft concept, and get_form_detection_run doesn't mirror its detect_form_fields counterpart.

Tool Count2/5

86 tools is a very heavy agent-facing surface, well past the 25+ threshold. The count is inflated by the near-identical 13-tool lifecycle repeated across extract, classify, and split (each with list/get/create/update/publish/runs/batches/versions), and while each tool has a distinct purpose, the sheer volume makes selection harder.

Completeness4/5

Core lifecycles are thoroughly covered: create → update → publish → run (single and batch) → poll → cancel → delete-run → list runs/versions. Notable gaps include no delete tool for extractors, classifiers, splitters, workflows, or evaluation sets, and edit/form-detection runs have no list endpoint (documented workaround: keep run IDs). These are hygenic gaps that don't block primary workflows.

Resources

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