smry Product

Read an article with smry

get_article

Read-only

Fetch clean, source-grounded text for a public article or YouTube URL, budgeted to a token limit and addressable by paragraph anchor. Returns an outline, the requested window, and a next_cursor when more remains. Prefer search_article when you have a specific question rather than needing the whole text.

Input Schema

TableJSON Schema

Name	Required	Description
`url`	Yes	The public http or https article or YouTube URL to read.
`cursor`	No	Paragraph anchor to resume from. Pass the next_cursor of a previous call to continue reading.
`max_tokens`	No	Approximate token budget for the returned text. Defaults to 4000, which covers a typical article whole. Raise it for long documents, lower it when context is tight.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes
`title`	Yes
`author`	Yes
`outline`	Yes
`language`	Yes
`cache_hit`	Yes
`publisher`	Yes
`to_anchor`	Yes
`reader_url`	Yes
`source_url`	Yes
`from_anchor`	Yes
`next_cursor`	Yes
`published_at`	Yes
`responded_at`	Yes
`total_blocks`	Yes
`total_tokens`	Yes
`content_quality`	Yes
`tokens_returned`	Yes
`extraction_source`	Yes

TDQS

A4.7/5.0

Behavior5/5

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

With readOnlyHint and openWorldHint annotations already covering safety, the description adds valuable behavioral context: token-budgeted output, paragraph-anchor addressing, and the exact return shape (outline, requested window, next_cursor). It also promises 'clean, source-grounded text,' setting accurate expectations beyond the schema.

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, each earning its place: the core capability, the return behavior, and the usage routing to a sibling. The most decision-relevant information is front-loaded, and there is no fluff or repetition of schema details.

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?

The description fully enables an agent to call this tool correctly: it covers the input type, output structure, pagination continuation via next_cursor, and when to choose a different tool. The presence of a rich output schema and full parameter descriptions means nothing essential 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%, and the schema already documents url, cursor, and max_tokens with detailed guidance including defaults and ranges. The description's references to 'token limit' and 'paragraph anchor' reinforce the schema but do not add materially new parameter semantics, so the baseline of 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 a specific verb ('Fetch') and resource ('clean, source-grounded text for a public article or YouTube URL'), and immediately distinguishes itself from search_article by framing this tool as the whole-text reader. An agent can tell exactly what this tool does and how it differs from the closest sibling.

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 says 'Prefer search_article when you have a specific question rather than needing the whole text,' giving both a clear when-not-to-use condition and the correct alternative. This leaves no ambiguity about the intended selection between the two reading-oriented tools.

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

A3.7/5.0

Disambiguation4/5

Each tool has a largely distinct job: discovery (search_feeds, search_web), content retrieval/search within URLs (get_article, search_article), and library/feed management (save_article, follow_feeds, update_library_item, create_monitor). The only mild overlap is get_article vs open_web_pages, both of which extract content from public URLs, but their different output styles and intended use cases keep them mostly separate.

Naming Consistency5/5

All nine tool names follow a consistent imperative verb + object pattern in snake_case: create_monitor, follow_feeds, get_article, open_web_pages, save_article, search_article, search_feeds, search_web, update_library_item. There are no mixed conventions, vague verbs, or surprising abbreviations.

Tool Count5/5

Nine tools is well-scoped for a reading and summarization product: three search/discovery tools, three content-retrieval tools, and three library/feed management tools. Each tool contributes a distinct workflow step and the set avoids bloat.

Completeness3/5

The set covers discovery, fetching and searching content, saving to a library, following feeds, monitoring page changes, and updating library items. However, there are no removal operations (unfollow feed, delete monitor, delete library item) and no direct way to list or read the user's saved library or followed sources, creating noticeable lifecycle gaps.

Resources

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