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searxng-mcp

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search_and_fetch

Search the web, rerank results, and fetch full content of top results as clean markdown, with caching and domain filtering.

Instructions

Search the web, rerank results, then fetch the full content of the top result(s). GitHub URLs are fetched via the GitHub API; all others go through a fetch cascade: Firecrawl → Crawl4AI → raw HTTP. Results and fetched pages are cached. Blocked domains are filtered. Returns the result list plus clean markdown of the fetched pages.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
siteNoRestrict results to one domain or a list of domains (e.g. 'github.com'). Best-effort — applied as a site: query operator; most engines honor it but some ignore it.
queryYesSearch query
expandNoUse local LLM to generate 2-3 query variants and merge results for a wider search surface (default: off). Adds ~3s latency.
enginesNoComma-separated SearXNG engine names to restrict the search to (e.g. 'google,duckduckgo'). Forwarded verbatim; unknown/disabled engines degrade to fewer results rather than erroring.
categoryNoSearch category: general, news, it, or science (default general)general
languageNoBCP-47 language code (e.g. 'en', 'de') or 'all' for all languages. Omit to use the SearXNG instance default.
min_scoreNoDrop results whose reranker relevance score is below this threshold (0-1, omit for no filtering). Filters on the RAW cross-encoder relevance score, not the recency-adjusted score used for ordering. Scores are strongly bimodal — relevant results cluster near 1.0 and irrelevant ones near 0, with little in between — so any value in roughly 0.01-0.9 behaves about the same; 0.01-0.1 is the useful range and 0.5 is not a midpoint. A high score means topically related, NOT correct: an Apache mod_proxy page scores 0.967 on an nginx query. Thresholds are model-dependent and not comparable across rerankers. No-op (with a logged warning) when the reranker is unavailable, since no scores exist to filter on.
time_rangeNoLimit results to: day, week, month, or year (omit for all time)
fetch_countNoNumber of top results to fetch full content for (default 1, max 3)
domain_profileNoNamed domain profile to apply: 'homelab', 'dev', or omit for default filters

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv3.23.0
    • addedInput schema / properties / min_score
      Added value: +{
+  "description": "Drop results whose reranker relevance score is below this threshold (0-1, omit for no filtering). Filters on the RAW cross-encoder relevance score, not the recency-adjusted score used for ordering. Scores are strongly bimodal — relevant results cluster near 1.0 and irrelevant ones near 0, with little in between — so any value in roughly 0.01-0.9 behaves about the same; 0.01-0.1 is the useful range and 0.5 is not a midpoint. A high score means topically related, NOT correct: an Apache mod_proxy page scores 0.967 on an nginx query. Thresholds are model-dependent and not comparable across rerankers. No-op (with a logged warning) when the reranker is unavailable, since no scores exist to filter on.",
+  "maximum": 1,
+  "minimum": 0,
+  "type": "number"
+}
  2. Changed2 schema fields changedv3.15.1
    • addedInput schema / properties / engines
      Added value: +{
+  "description": "Comma-separated SearXNG engine names to restrict the search to (e.g. 'google,duckduckgo'). Forwarded verbatim; unknown/disabled engines degrade to fewer results rather than erroring.",
+  "type": "string"
+}
    • addedInput schema / properties / site
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    }
+  ],
+  "description": "Restrict results to one domain or a list of domains (e.g. 'github.com'). Best-effort — applied as a site: query operator; most engines honor it but some ignore it."
+}
  3. First observedv3.12.0

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure — and it delivers substantially. It reveals the multi-step pipeline (search → rerank → fetch), the GitHub-API special path, the fallback cascade order (Firecrawl → Crawl4AI → raw HTTP), caching of results and pages, blocked-domain filtering, and the return shape (result list + clean markdown). This far exceeds what the name or schema alone would convey.

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 with the core purpose front-loaded in sentence one, followed by the most operationally important details in priority order: GitHub special-casing, fetch cascade, caching/filtering, and return format. Every sentence earns its place; there is zero redundancy or filler.

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 10-parameter tool with no annotations and no output schema, this is close to complete: it covers the pipeline, fetch strategy, caching, filtering, and return format. The gaps are minor — no explicit error behavior, rate limits, or detailed result-entry structure — but the highly descriptive schema compensates for most of what the description omits.

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?

Schema description coverage is 100%, and the schema itself is unusually rich — e.g., `min_score` documents bimodal score distribution, the 0.01-0.1 useful range, model-dependence, and no-op behavior. The description adds pipeline-level framing that helps connect parameters to stages (rerank → min_score, top results → fetch_count), but provides no per-parameter details beyond the schema. The baseline 3 applies as schema does the heavy lifting.

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 states a specific verb chain and resource: 'Search the web, rerank results, then fetch the full content of the top result(s).' This clearly differentiates it from sibling tools `search` (search only) and `fetch_url` (fetch only) by describing the combined pipeline. The GitHub-API special case and fetch cascade further pin down exactly what the tool does.

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?

The pipeline description makes the usage context clear: this tool is for when an agent needs both ranked search results AND full page content. The details about caching, blocked-domain filtering, and the fetch cascade give a concrete picture of behavior. However, it never explicitly names alternatives or states when NOT to use it (e.g., 'if only URLs are needed, use search'), so exclusion guidance is left to inference.

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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