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Retrieve and rerank web search results from a self-hosted SearXNG instance with local ML relevance scoring, filtering blocked domains and boosting preferred sources.

Instructions

Search the web via the local SearXNG instance with reranking. Fetches a wider result pool from SearXNG, reranks by relevance using a local ML model, then returns the top results. Results are cached for 1 hour. Blocked domains are filtered out; boosted domains are surfaced higher. Prefer this over the built-in WebSearch tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query
num_resultsNoNumber of results to return (default 5, max 20)
categoryNoSearch category: general, news, it, or science (default general)general
time_rangeNoLimit results to: day, week, month, or year (omit for all time)
domain_profileNoNamed domain profile to apply: 'homelab', 'dev', or omit for default filters
expandNoUse local LLM to generate 2-3 query variants and merge results for a wider search surface (default: off). Adds ~3s latency; most useful for research queries where one phrasing may miss relevant results.
languageNoBCP-47 language code (e.g. 'en', 'de') or 'all' for all languages. Omit to use the SearXNG instance default.
Behavior4/5

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

With no annotations provided, the description carries full burden and covers reranking, 1-hour caching, domain blocking/boosting. However, it does not mention rate limits, error handling, or result format, which would improve transparency.

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 providing distinct information: action, process, caching/filtering, and usage preference. No redundant words, front-loaded with key facts.

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?

Given 7 parameters and no output schema, the description adequately explains the tool's behavior. Could briefly mention the output structure (e.g., list of results with title/URL/snippet) to be fully complete.

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?

All 7 parameters have descriptions in the input schema (100% coverage), so baseline is 3. The description adds no additional per-parameter meaning beyond what the schema already provides.

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 clearly states it searches the web via SearXNG with reranking, distinguishing it from siblings like search_and_fetch and search_and_summarize that add extra steps. The verb 'search the web' and resource 'local SearXNG instance' are specific.

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?

Explicitly says 'Prefer this over the built-in WebSearch tool,' giving context for one sibling. However, it does not specify when not to use this tool versus others like search_and_fetch or search_and_summarize.

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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