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fetch_many

Read-onlyIdempotent

Solve batch web reading by fetching and extracting multiple URLs in parallel. Deduplicates after canonicalisation, returning per-source data and aggregate stats.

Instructions

Fetch and extract several URLs in parallel. URLs are deduplicated after canonicalisation. Each per-source full text is preserved in hidden _meta.pages[].full_text. Best for: reading a batch of search results, comparing several known sources, building a multi-source citation set. Returns: per-source {title, url, excerpt, citations, render_mode} and aggregate stats (success_count, error_count, elapsed_ms). Rendered fetch is automatic for JS-heavy pages; pass rendered=true to force browser mode for every URL (slower). Use fetch_url for a single URL; research for an end-to-end multi-query workflow.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesList of 1–20 absolute http(s) URLs to fetch in parallel. Duplicates are dedupe-d after canonicalisation.
max_excerpt_charsNoExcerpt character cap for the visible output (200–50000). Default comes from server settings. The full extracted text is always returned in hidden `_meta.full_text`.
max_linksNoMaximum outbound links to surface in the visible output (0–64, default 8). The complete link list is always included in hidden `_meta`.
renderedNoForce browser-based rendering for fetches. Default false: the server auto-renders only when a page is JS-heavy or returns near-empty text. Set true when a previous fetch came back nearly empty or to read a known SPA. Forced rendering is several times slower than HTTP fetch.
render_wait_msNoExtra milliseconds to wait after DOM content load before extracting (0–15000). Use a higher value (e.g. 1500–4000) for SPAs that hydrate slowly.
concurrencyNoMaximum concurrent backend requests for this fan-out (1–16). Higher is faster but puts more load on the SearXNG instance and remote pages. Omit for the server default.
ttlNoCache TTL override in seconds (0–86400). 0 disables caching for this call. Omit to use the server's default TTL.
Behavior5/5

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

Annotations indicate read-only, idempotent, non-destructive; description adds details like parallel execution, deduplication, full text preservation, and auto-rendering behavior. No contradictions.

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?

Concise and well-structured: starts with purpose, then best-for, returns, parameter details. Every sentence adds value without redundancy.

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 (7 parameters, parallel fetching), the description covers purpose, usage, parameter behavior, and return structure. No output schema but return fields are listed.

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%, baseline 3. Description adds practical context for each parameter (e.g., when to force rendering, how to set render_wait_ms for SPAs, concurrency load implications), exceeding baseline.

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 uses specific verbs ('Fetch and extract several URLs in parallel') and resources, and distinguishes from siblings by mentioning 'fetch_url' for single URL and 'research' for multi-query workflow.

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?

Explicitly states best use cases (e.g., reading batch search results, building citation set) and provides alternatives for single URL or multi-query workflows.

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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