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Perceive URLs in Batch

perceive_batch

Batch-render up to 1000 URLs with one shared options block, returning markdown, PDFs, screenshots, and structured data. Poll by job ID for progress and download all artifacts as a ZIP.

Instructions

Render up to 1000 URLs with one shared options block. Small batches (about 10 or fewer) complete inline in the response; larger ones return status 'queued', so poll get_perceive_batch with the job_id.

Use when: the same outputs are needed from many known URLs (e.g. markdown of every doc page you just discovered with discover_urls).

Do NOT use when: one URL (use perceive_url); the goal is RAG-ready chunked output (use start_ingest); the URLs are unknown (use discover_urls or web_search first).

Returns: job_id, status (queued/processing/completed/failed/partial), per-URL counts, and one full perceive result per URL once processed. output_mode 'zip' bundles every artifact into one ZIP.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesURLs to render with the shared options.
mobileNoEmulate a mobile device. Default false.
schemaNoJSON schema for LLM structured extraction (plan-gated). Combine with outputs including 'structured'.
contextYesExplain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): "Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization."
extractNoHeuristic extraction targets. Implemented today: tables, metadata, main_content, headings, structured_data; others return a warning.
js_codeNoJavaScript executed after page load, before capture.
outputsNoArtifacts to produce. Default: ['markdown','structured'].
wait_forNoCSS selector (optionally 'css:...') or 'js:<expr>' to await after navigation.
cache_modeNoDefault 'enabled' (~1h cache). 'bypass' skips the cache; 'refresh' re-renders.
output_modeNo'manifest' (default) or 'zip' (bundle all artifacts once complete).
pdf_optionsNoOnly meaningful when outputs includes 'pdf'.
respect_robotsNoDefault false.
viewport_widthNoDefault 1920.
block_resourcesNoResource types the browser should not load (faster, cheaper renders).
viewport_heightNoDefault 1080.
wait_timeout_msNoDefault 30000.
Behavior4/5

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

No annotations are provided, so the description carries the full transparency burden. It discloses async queueing for large batches, the need to poll with job_id, status values including failed/partial, and return structure with per-URL counts and full results. It also explains output_mode 'zip' bundling. It doesn't detail failure handling but covers the key behaviors.

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?

Four sentences, each with a distinct purpose: purpose, queueing behavior, usage guidance, and return value description. Information is front-loaded and every sentence earns its place without fluff.

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?

For a complex 16-parameter tool with no output schema, the description covers purpose, usage alternatives, async behavior, return values, and output modes. It leverages the schema for parameter details and provides enough context for an agent to select and invoke correctly. Minor gap: no explicit error-handling details, but not essential.

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?

Input schema has 100% parameter coverage, so the baseline is 3. The description adds only a few semantic hints like output_mode 'zip' bundling and the 'shared options block' concept, but does not need to repeat schema details. No significant gaps in parameter understanding.

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?

Description uses specific verb 'Render' and clearly identifies batch processing of up to 1000 URLs with a shared options block. It also distinguishes from siblings by explicitly naming perceive_url, start_ingest, and discover_urls in the 'Do NOT use when' section.

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 'Use when' and 'Do NOT use when' guidance with named alternatives (perceive_url, start_ingest, discover_urls/web_search). Also clarifies behavior for small vs large batches and directs polling via get_perceive_batch with job_id.

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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