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omniseek_read

Turn any URL or file into readable text. Automatically handles web pages, PDFs, DOCX, PPTX, XLSX, and more, using pagination for long documents and OCR for text in images.

Instructions

Read text from any URL OR document FILE — OmniSeek's single "read this deep" verb. AUTO-ROUTES.

ROUTING: if target is a local filesystem path OR ends with a document extension (.pdf / .pptx / .docx / .xlsx / .txt / .md / .csv, case-insensitive, a ?query is tolerated) it routes to the DOCUMENT reader (below); otherwise it routes to the URL reader. start_char / max_chars window the body on BOTH branches (see below); export_media / ocr apply only to the document branch (a URL read has no image-extraction path) and are IGNORED on the URL branch.

URL BRANCH: fetch + normalize ONE URL. Tries each registered adapter until one claims it — a specific article link (a Reddit post, an arXiv paper, a Bluesky post) as a normalized document. arXiv is two-tier by design: an /abs/<id> URL returns abstract-level metadata (title / authors / abstract, a fast lookup), while an /pdf/<id> URL routes to the PDF extractor and returns the WHOLE body (e.g. 2203.02155v1 → 68 pages of full text). Pass the URL whose depth you want. vs the open web: reads ONE specific URL you already have; to FIND open-web pages use WebSearch first, then omniseek_read to normalize the page (a common pairing). The normalized body is WINDOWED by start_char / max_chars (default 24000), exactly like the document branch: a big page (a SEC 10-K/20-F is ~2 MB → ~200k chars, a long article) would otherwise return one blob that overflows the tool channel and is unreadable. When truncated is true, re-call with start_char bumped by returned_chars to page through the rest. A small page (< max_chars) returns whole, truncated=false — unchanged from before. URL branch returns: {"url", "matched": bool, "document": Document as dict | None, "total_chars", "returned_chars", "start_char", "truncated"} (the last four only when matched). On matched:false a reason is added: walled (anti-bot challenge -> retry the source via CDP, e.g. omniseek_search(sources=[...], raw=True, full=True)) vs empty vs blocked, so you can tell "gated, drill it another way" from "genuinely nothing there".

DOCUMENT BRANCH (pptx / docx / xlsx / pdf / txt / md / csv): read the FILE into readable, structured text — the document counterpart of omniseek_transcribe (speech). Free, keyless, cached. WHERE THE FILE LIVES:

  • the operator's machine: scp it to OmniSeek host inbox first — scp "" :omniseek-inbox/ then call with "omniseek-inbox/".

  • Anywhere on the web: just pass the URL (conference slide decks, a shared docx, a PDF). WHAT COMES BACK: outline = per slide/sheet/page {label, chars, media} — the MAP of the whole document, always complete and tiny; text = the readable content ("## Slide 3" / "## Sheet: budget" / "## Page 5" headers), windowed by start_char/max_chars for big docs (truncated=true + total_chars tell you to re-call with start_char to continue); media/media_total = the image inventory per section. THE IMAGE HALF (be honest about it): a figure deck or scanned doc carries its meaning in IMAGES — text extraction alone is NOT the document. Two ways to read it: omniseek_view delivers the figures to your OWN vision in-band (judging the figure is yours); ocr=True here runs OCR over every embedded image and folds the recognized text-in-pixels (scanned page body, chart labels, palette HEX/RGB codes) into the body under a '图中文字 (OCR)' section — mechanical text transcription, NOT figure interpretation, and labeled as possibly imperfect. Use ocr for text-bearing images (scans, labels); use omniseek_view to SEE the figure. Document branch returns: {source, format, title, outline, text, total_chars, returned_chars, start_char, truncated, media_total, media, media_dir, ocr_images?, cached} — or {source, error, inbox_files?}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ocrNo
targetYes
max_charsNo
start_charNo
export_mediaNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.2.0

TDQS

A4.9/5.0
Behavior5/5

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

No annotations exist, so the description carries the full burden — and it delivers extensively. It discloses auto-routing rules, the windowing/pagination protocol (truncated flag, bump start_char by returned_chars), the arXiv two-tier /abs vs /pdf behavior, the matched:false reason taxonomy (walled/empty/blocked), that export_media/ocr are IGNORED on the URL branch, and the honest caveat that OCR is mechanical transcription, not figure interpretation. This is exemplary behavioral disclosure for a dual-branch tool.

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?

The description is long but earned: the tool is genuinely complex with two branches, five parameters, and no output schema. Markdown-style headers (URL BRANCH, DOCUMENT BRANCH, WHERE THE FILE LIVES, WHAT COMES BACK, THE IMAGE HALF) make it scannable. Minor redundancy exists — windowing is explained twice, once per branch — and some phrasing ('unchanged from before', 'be honest about it') adds color without information. Nearly every sentence earns its place, but a few could be tightened.

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?

For a complex auto-routing tool with 5 parameters, no output schema, and no annotations, the description is essentially complete. It documents both return shapes, error/edge cases (matched:false reasons, {source, error, inbox_files?}), the file-ingestion workflow (scp to inbox), caching, and parameter interactions across branches. An agent has everything needed to call this tool correctly and interpret its results.

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 description coverage is 0%, so the description must compensate fully — and it does. target's routing semantics (path vs extension vs URL), start_char/max_chars windowing with paging instructions, and the branch-specific behavior of ocr / export_media are all explained in detail. Every one of the 5 parameters gains meaning beyond its schema definition.

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 opens with a clear verb+resource pair: 'Read text from any URL OR document FILE' and identifies itself as OmniSeek's single 'read this deep' verb. It is explicitly differentiated from siblings: omniseek_transcribe is called the 'document counterpart (speech)' and omniseek_view is positioned as the image-seeing tool. The auto-routing between URL and document branches is precisely scoped.

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?

Provides explicit when-to-use guidance: 'reads ONE specific URL you already have; to FIND open-web pages use WebSearch first, then omniseek_read to normalize the page (a common pairing)'. It also names alternatives for specific cases — omniseek_view for seeing figures, omniseek_transcribe for speech, and omniseek_search with CDP for walled sources. No ambiguity remains about which sibling to pick.

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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