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Extend MCP

Update an extractor's draft

update_extractor
DestructiveIdempotent

Update an extractor's mutable draft (extract group): rename it and/or replace the draft config (hand-editing the schema? call get_documentation with https://docs.extend.ai/extraction/schema.md first). Published versions are immutable and unaffected — runs pinned to them keep working. Follow any llmContext guidance included in results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesExtractor ID (ex_...).
nameNoNew display name.
configNoReplaces the whole DRAFT config. Same shape and rules as config on create_extractor / extract_data.
environmentYes"TEST" = the Test (development) environment, "PRODUCTION" = live. Must match a granted target from get_me (an API key pins one environment).
workspaceIdYesTarget workspace (ws_...). Must be a granted workspace — get_me lists the accepted values.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
nameYes
createdAtNo
updatedAtNo
draftVersionNo

TDQS

A4.5/5.0
Behavior4/5

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

The description adds meaningful behavioral nuance beyond annotations: it clarifies that only the draft is mutable, published versions stay intact, and runs pinned to published versions keep working. It also warns about the config replacement semantics and mentions following llmContext guidance. This goes beyond the raw destructiveHint/idempotentHint flags.

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 sentences with no filler. The core purpose is front-loaded, and each subsequent sentence adds a distinct piece of useful context: immutability, schema documentation reference, and llmContext follow-up behavior.

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 covers the essential behavioral contract: what changes, what doesn't change, what to consult before hand-editing schema, and how to handle result guidance. With an output schema present and annotations carrying safety signals, nothing critical is missing for an agent to call this tool correctly.

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 the baseline is 3. The description adds extra value by clarifying that config replaces the whole draft config and by referencing create_extractor for shape/rules, plus noting the 'rename and/or replace' relationship between name and config. This improves on the schema's already-present property descriptions.

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 ('Update'), the specific resource ('extractor's mutable draft'), and the two concrete operations ('rename it and/or replace the draft config'). This distinguishes it from related tools like create_extractor or publish_extractor_version.

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?

The description gives clear context on what can be updated and explicitly notes published versions are immutable and unaffected, steering the agent away from expecting published changes. It also points to get_documentation when hand-editing schema, providing useful alternative guidance. It doesn't explicitly enumerate all sibling alternatives, but the context is strong.

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.

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