Extend MCP

Update a workflow's draft

update_workflow

DestructiveIdempotent

Update a workflow's mutable draft (workflows group): rename it and/or replace the entire draft step graph (hand-editing steps? call get_documentation with https://docs.extend.ai/workflows/configuring-workflows.md first). Deployed versions are immutable and unaffected — runs pinned to them keep working. Follow any llmContext guidance included in results.

Input Schema

TableJSON Schema

Name	Required	Description
`id`	Yes	Workflow ID (workflow_...).
`name`	No	New display name.
`steps`	No	Replaces the whole DRAFT step graph. Step graph (max 100 steps), TRIGGER → PARSE first. Every step needs { type, name }; "name" is REQUIRED and is what other steps route to. Route via next, an ARRAY of objects: linear steps (TRIGGER/PARSE/EXTRACT) use next: [{ step: "<target name>" }]; CLASSIFY/SPLIT branch with next: [{ step, classificationId }] (classificationId = a classification id from the config, not its type). TRIGGER routes to exactly one PARSE. Types: TRIGGER, PARSE, EXTRACT, CLASSIFY, SPLIT, MERGE_EXTRACT, CONDITIONAL, CONDITIONAL_EXTRACT, EXTERNAL_DATA_VALIDATION, WEBHOOK_RESPONSE, RULE_VALIDATION, VALIDATION, ROUTER, HUMAN_REVIEW, COLLECT, FILE_CONVERSION. EXTRACT/CLASSIFY/SPLIT need a config with exactly one of a saved ref or inline config (EXTRACT: config.extractor {id,version} or config.extractorConfig with REQUIRED schema; CLASSIFY: config.classifier {id,version} or config.classifierConfig); next is only allowed once config is set. Classifier/splitter refs can't be "latest" — use semver or "draft". The rules here are a summary — before authoring a step graph by hand, call get_documentation with https://docs.extend.ai/workflows/configuring-workflows.md and follow it.
`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.

Output Schema

TableJSON Schema

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

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already flag mutation and destructiveness, so the description adds value by stating that deployed versions are immutable, runs pinned to them keep working, and only the draft is affected. The instruction to follow llmContext guidance in results is an extra behavioral disclosure beyond the structured fields.

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 concise, information-dense sentences front-load the action and scope, then add a documentation pointer and a safety-relevant immutability note. There is no filler and every sentence earns its place.

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

Completeness4/5

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

For a complex tool with a detailed schema and an output schema, the description is nearly complete: it explains the update scope, the destructive boundary, and where to get authoritative step-graph documentation. It could be slightly stronger by explicitly naming sibling tools for creating or deploying workflows, but that is not a significant gap.

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 baseline is 3. The description maps the high-level actions 'rename' and 'replace step graph' to parameters, but it adds no syntax or format details beyond what the schema already provides. The schema itself carries the detailed step-graph rules.

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: 'Update a workflow's mutable draft' and then enumerates exactly what can change: rename it and/or replace the entire draft step graph. This clearly distinguishes the tool from siblings like create_workflow, get_workflow, and deploy_workflow_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?

It clearly positions the tool for editing an existing draft and explicitly advises calling get_documentation before hand-authoring a step graph. It does not explicitly contrast with create_workflow or deploy_workflow_version, but the draft-versus-deployed immutability statement gives strong contextual 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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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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