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searxng-mcp

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search_and_summarize

Search the web, fetch top results, and receive a synthesized answer with citations, using a local LLM. Falls back to raw content if summarization is unavailable.

Instructions

Search, rerank, fetch top results, then synthesize a summary with citations using a local LLM (OLLAMA_SUMMARIZE_MODEL). Returns a structured answer with source attribution. If the LLM is unavailable it falls back to raw fetched content, and that fallback is always announced by a leading '--- summarization unavailable (: ) --- ' marker: if you do not see that marker, what you received is a real synthesis. Best for deep research where you want pre-digested synthesis rather than raw 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.
queryYesResearch query to search for and summarize
expandNoUse query expansion before searching (default: off)
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 and synthesize (default 3, max 5)
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

A5/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so thoroughly: it discloses reranking, fetching limits, local LLM synthesis, the explicit fallback marker, min_score no-op warnings, and per-parameter default 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?

Although long, the description is information-dense and every sentence adds operational detail. The fallback marker is slightly repetitive but still useful for programmatic detection.

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, the description covers search expansion, engine restrictions, categories, language, time range, fetch count, scoring thresholds, profiles, and fallback behavior. It is sufficiently complete without an output schema.

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?

All 10 parameters are documented with meaningful detail beyond the schema. Especially strong for min_score, explaining the bimodal distribution, non-comparability across rerankers, and the no-op warning when unavailable.

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?

Clearly states a multi-step purpose: search, rerank, fetch top results, and synthesize a cited summary. Explicitly distinguishes itself from raw-page tools with 'rather than raw pages' and 'deep research'.

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?

Tells when to use it ('Best for deep research where you want pre-digested synthesis') and contrastively implies when not to use it (raw pages). Also documents fallback behavior, which helps manage expectations.

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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