Extend MCP

Run a batch of parse runs

run_parse_batch

Submit up to 1,000 documents as one batch of parse runs (parse group); no saved processor needed — an optional inline config applies to every run. Returns a batchId immediately; poll aggregate status with get_parse_batch and fetch individual results with list_parse_runs filtered by batchId. Follow any llmContext guidance included in results.

Input Schema

TableJSON Schema

Name	Required	Description
`config`	No	Inline parse config applied to every run (same shape as parse_document's config).
`inputs`	Yes	1-1000 documents.
`priority`	No	Queue priority (1-100).
`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
`status`	No
`batchId`	Yes
`runCount`	No
`llmContext`	No

TDQS

A4.5/5.0

Behavior5/5

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

The description goes beyond the annotations by disclosing asynchronous behavior: it returns a batchId immediately rather than blocking, and requires polling get_parse_batch or fetching results via list_parse_runs. It also notes the inline config applies uniformly and mentions following llmContext guidance in results, which helps agents understand expected behavior.

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 deliver the core action, the async follow-up pattern, and a notable behavioral instruction. The description is front-loaded with the key capability and limit, with no filler or 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 output schema exists and the input schema is already very detailed, the description completes the picture by explaining the batch lifecycle and how results are retrieved. Nothing critical is missing for an agent to call and consume this tool correctly.

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?

The input schema already provides 100% parameter coverage with rich descriptions, including the file id/url/text union, environment pinned via get_me, workspace grants, and maxItems. The description adds little beyond the existing schema details, so the baseline of 3 applies.

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 a specific action: submit up to 1,000 documents as a batch of parse runs. It also adds differentiating detail — no saved processor needed and optional inline config — so the tool is distinct from a single parse or other batch workflows.

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 usage context: batching multiple documents, returning a batchId immediately, and using get_parse_batch or list_parse_runs for follow-up. It does not explicitly contrast with parse_document or other run_*_batch siblings, but the context is strong enough to guide selection.

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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