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Octen-Team

octen-mcp

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by Octen-Team

extract

Fetch URLs to get clean, LLM-ready content with automatic classification. Optionally retrieve relevance-ranked snippets for specific topics.

Instructions

Fetch one or more URLs and return LLM-ready content from Octen. By default (no query) it returns each page's full content — this is what you want in almost all cases. Only pass query when the user explicitly asks to fetch relevance-ranked snippets for a specific topic; doing so returns highlights INSTEAD of the full body, so the content will be partial. Every result also includes a category (topical) and page_structure (typology) classification, unique to Octen. Bare hosts like 'octen.ai' are auto-normalized to https. Cached when fresh.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesURLs to extract. 1-20 per call. Bare hosts ok.
queryNoOptional — leave UNSET in the normal case. When unset, each result returns the page's `full_content` (the complete text). Only set this when the user explicitly wants relevance-ranked snippets for a specific query/topic: setting it makes each result return `highlights` (ranked excerpts) and OMIT `full_content`, so the page body will be incomplete. Do not pass it just to focus a normal fetch.
formatNoOutput format. Default markdown.markdown
timeoutNoPer-URL timeout in seconds (1-60).
include_audioNoReturn audio URLs found on each page.
include_imagesNoReturn image resources found on each page (also enables `cover_image` when the page has one).
include_videosNoReturn video URLs found on each page.
max_age_secondsNoMaximum age of cached content in seconds. Default 24h. Lower this for time-sensitive pages (news / prices).
Behavior5/5

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

Discloses auto-normalization of hosts, caching behavior, and the effect of the `query` parameter on output structure (highlights vs full content). Also mentions classification fields unique to Octen.

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?

Five sentences, front-loaded with purpose. Every sentence serves a distinct purpose without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers key behavioral aspects (caching, auto-normalization, query effect) and mentions return fields. Lacks explicit details on return format but sufficient given no output schema.

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%, so description adds extra value by explaining auto-normalization, caching, and the nuance of `query`. The baseline is 3; the additional context justifies a 4.

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 starts with 'Fetch one or more URLs and return LLM-ready content from Octen', a specific verb+resource. It clearly differentiates from sibling search tools by focusing on extraction from given URLs.

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?

Provides clear guidance on when to use the `query` parameter vs default behavior, and warns about the tradeoff. Does not explicitly contrast with siblings, but the function's purpose is distinct from search 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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