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DocImprint

by com.docimprint.api

Search Collection

collection.search
Read-onlyIdempotent

Semantic (vector) search across documents in a collection. Returns ranked text chunks with relevance scores. Free — no credits consumed. Use when you need raw matching chunks from a collection. For a synthesized cited answer from the same context, use collection.ask instead. PREREQUISITE: Collection must be populated via collection.add_document and async indexing must complete (poll job.status) before results appear. Returns: { results: [{ bundle_id, chunk_id, text, score: number (0–1), title? }] } Example prompts:

  • "Search my Q4 Contracts collection for mentions of liability cap."

  • "Find the clause about data retention in my due diligence docs."

  • "Search for revenue numbers across my quarterly reports."

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax chunks to return (default 10, max 50). Example: 5
queryYesNatural language search query. Example: "What were the revenue numbers for Q4?"
collection_idYesCollection ID (col_...) returned by collection.create. Example: "col_550e8400-e29b-41d4-a716-446655440000"

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsYes

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint. Description adds 'Free — no credits consumed' and detailed return format, providing useful behavioral context beyond annotations.

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?

Highly efficient: single paragraph covering purpose, alternative, cost, prerequisites, return format, and examples. No wasted sentences.

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?

Output schema is implied by detailed return structure. Covers all needed aspects: parameters, prerequisites, examples, and distinguishes from sibling. Complete for tool complexity.

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%, baseline 3. Description adds example prompts and explains limit (max 50), query as natural language, and collection_id format, adding value beyond schema.

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 tool performs semantic (vector) search across documents in a collection and returns ranked text chunks, distinguishing it from the sibling tool collection.ask.

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 states when to use (raw matching chunks) and when not (use collection.ask for synthesized answer). Also mentions prerequisite of populating collection and waiting for indexing.

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

A4.5/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, grouped by domain (account, bundle, collection, document, job, receipt, url). Descriptions and naming make it easy to differentiate between similar tools like url.extract vs document.extract_text or collection.search vs collection.ask.

Naming Consistency5/5

All tools follow the same prefix.group_action pattern in snake_case (e.g., account.quota, bundle.get, collection.create). No mixing of conventions, making the API predictable and easy to navigate.

Tool Count5/5

With 22 tools, the server covers a comprehensive set of operations for document and evidence management. Each tool serves a specific purpose, and the count feels well-scoped without being bloated or sparse.

Completeness3/5

The tool surface lacks explicit create and delete operations for bundles and collections. Bundles appear to be created externally, and there is no tool to remove a bundle or collection. This is a notable gap given the server's stated purpose.

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