api

Add Document to Collection

collection.add_document

Idempotent

Add an evidence bundle to a collection and trigger async vector indexing. Use after collection.create to populate a collection with documents. Once indexed, documents become searchable via collection.search and collection.ask. Indexing is async — poll job.status with the returned job_id until status is "complete". Also returns a signed action receipt (rcpt_...) binding this add call to the bundle manifest — list with receipt.list, verify with receipt.verify. PREREQUISITE: Bundle must have status "complete" (check with bundle.get). Collection must be owned by your API key. Returns: { collection_id, bundle_id, job_id (poll for indexing completion), receipt: ActionReceipt|null } Example prompts:

"Add my contract bundle ev_550e8400 to the Q4 Contracts collection."
"Put this evidence bundle into my Due Diligence Docs collection for search."
"Add document [bundle_id] to collection [col_id] with a title."

Input Schema

TableJSON Schema

Name	Required	Description
`title`	No	Optional display title for the document in this collection. Example: "Q4 2025 Financial Report"
`bundle_id`	Yes	Evidence bundle ID (ev_...) to add. Bundle must have status "complete". Example: "ev_550e8400-e29b-41d4-a716-446655440000"
`collection_id`	Yes	Collection ID (col_...) returned by collection.create. Example: "col_550e8400-e29b-41d4-a716-446655440000"

Output Schema

TableJSON Schema

Name	Required	Description	Default
`job_id`	Yes
`receipt`	Yes
`bundle_id`	Yes
`collection_id`	Yes

TDQS

A4.6/5.0

Behavior5/5

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

Discloses async indexing, need to poll job.status, and return of a receipt. Annotations already indicate idempotent and non-destructive, but description adds behavioral context beyond annotations, such as the prerequisite checks and return structure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with front-loaded purpose, then step-by-step, prerequisites, returns, and examples. Slightly verbose with example prompts, but all information earns its place. No wasted words.

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?

Covers all necessary context: flow (async indexing), prerequisites, returns (including receipt), and examples. Given output schema and annotations, description is complete for this moderately complex tool.

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 coverage is 100% with descriptions for each parameter. Description does not add significant semantic meaning beyond what schema provides, but it does give usage context (e.g., 'Use after collection.create'). 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 clearly states the verb 'Add' and the resource 'evidence bundle to a collection', and distinguishes from siblings like collection.create (creates collection) and collection.search/ask (query). It specifies the action and its scope.

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?

Explicitly says 'Use after collection.create' and provides prerequisites: bundle must have status 'complete', collection owned by API key. Includes instructions on polling job.status and gives example prompts, offering clear guidance on when and how to use.

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

A4.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose, with no overlap. The category prefixes (account, bundle, collection, document, job, receipt, url) and specific action names (get, notarize, verify, create, list, etc.) ensure that an agent can unambiguously select the correct tool for any task.

Naming Consistency5/5

All tools follow a consistent category.action or category.action_noun pattern using snake_case (e.g., bundle.get, collection.add_document, url.translate). No mixed conventions or irregular names, making the pattern predictable and easy to learn.

Tool Count4/5

With 22 tools, the set is somewhat large but each tool addresses a distinct need within a broad domain (evidence management, document AI, collections, URL processing, job tracking, receipts). The count is slightly above the typical well-scoped range but still reasonable given the scope.

Completeness2/5

The tool surface has notable gaps: there is no tool to create or delete an evidence bundle, nor to update collections or bundles. The core workflow of creating a bundle from a document is missing, and the lifecycle is incomplete, which would likely cause agent failures in typical use cases.

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