api

Summarize Document

document.summarize

Read-onlyIdempotent

Summarize document text into a prose summary and key points with citations. Use after document.extract_text or url.extract when you need a condensed understanding of a long document. For single-sentence Q&A, use url.qa instead. For extracting specific fields, use document.extract_structured. Typical workflow: document.extract_text/url.extract → document.summarize. Returns: { summary: string, key_points: string[], summary_cited: { value, confidence, citations[] }, key_points_cited: [{ text, citations[] }], truncated: boolean, strategy: "full"|"truncated"|"chunked" } Example prompts:

"Summarize this financial report and give me the key points."
"What are the main takeaways from this document?"
"Give me a concise summary of this 50-page report."

Input Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes	Document text to summarize. Obtain via document.extract_text or url.extract. Example: "The Q4 2025 financial report shows revenue growth of 23% year-over-year..."
`max_tokens`	No	Input length cap (1 token ≈ 4 chars). Default ~3000 tokens. Truncates input, not output. Example: 4000

Output Schema

TableJSON Schema

Name	Required	Description	Default
`summary`	Yes
`strategy`	Yes
`truncated`	Yes
`key_points`	Yes
`summary_cited`	Yes
`key_points_cited`	Yes

TDQS

A5/5.0

Behavior5/5

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

Annotations declare readOnlyHint=true and idempotentHint=true, indicating a safe read operation. The description adds significant behavioral context: explains the structure of the returned object (summary, key_points, cited versions, truncated flag, strategy), mentions truncation behavior for input length, and outlines the typical workflow. This goes 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?

The description is well-structured and appropriately sized. It includes a purpose statement, usage guidelines, workflow hint, output format specification, and example prompts. Every sentence serves a clear purpose, and the information is front-loaded.

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 tool's complexity (summarization with structured output), the description is complete. It covers input, output format, strategy types, truncation, and usage context. The output schema is embedded in the description, and workflow dependencies are clarified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description enriches both parameters: 'text' is explained with source (other tools) and an example; 'max_tokens' explains default and effect (truncates input, not output) with an example. This adds practical meaning beyond the 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 explicitly states 'Summarize document text into a prose summary and key points with citations.' It differentiates from siblings by specifying alternatives: 'For single-sentence Q&A, use url.qa instead. For extracting specific fields, use document.extract_structured.' The purpose is clear and specific.

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 provides explicit guidance on when to use the tool ('Use after document.extract_text or url.extract') and when not to, with direct references to sibling tools. It also includes example prompts that illustrate typical usage scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All api tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?