Extend MCP

List extractors

list_extractors

Read-onlyIdempotent

List the workspace's extractors, newest first (extract group). Fetch a specific one's config with get_extractor. Follow any llmContext guidance included in results.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Page size (default 25).
`sortBy`	No	Default updatedAt.
`sortDir`	No	Default desc.
`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.
`nextPageToken`	No	Opaque cursor from the previous page.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`items`	Yes
`hasMore`	Yes
`llmContext`	No
`nextPageToken`	No

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behavior beyond annotations by specifying 'newest first' and the directive to follow llmContext guidance in results. It could go further by noting pagination behavior, but the provided behavioral details are useful and non-redundant.

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 three short sentences with no filler. The main action is front-loaded, the alternative tool is named in the second sentence, and the llmContext note is a valuable usage detail. Every sentence earns its place.

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?

With read-only/idempotent annotations covering safety, a 100%-coverage schema documenting all parameters, and an output schema available, the description does not need to explain return values or parameter syntax. It adds the essential contextual piece—differentiation from get_extractor—and the non-obvious llmContext behavior. Nothing critical is missing.

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 fully documents all six parameters including defaults and constraints. The description adds a small semantic improvement by interpreting the default sort as 'newest first', but it does not add meaningful parameter-level detail beyond what the schema already provides. Baseline 3 is appropriate.

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 states the exact scope ('List the workspace's extractors'), the ordering ('newest first'), and the resource type. It also distinguishes itself from get_extractor by noting that fetching a specific one's config is a separate operation, making it easy for an agent to pick the right tool among many list_* siblings.

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 names get_extractor as the alternative when a specific extractor's config is needed, giving clear selection guidance. The additional instruction to follow llmContext guidance in results further clarifies how to handle the output, and the reading context makes the typical use case obvious.

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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