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SearXNG MCP Server

Searxng Ingest Search

searxng_ingest_search

Run SearXNG searches and ingest results into an epistemic knowledge graph, creating linked document, query, and engine nodes for traceable source capture.

Instructions

Run a SearXNG search and natively ingest its results into epistemic-graph.

Wire-First: performs the search, then pushes each result into the knowledge graph as a search :Document (+ :SearchQuery / :SearchEngine typed nodes and their :resultOf / :fromEngine links). Best-effort: ingested is None when no engine is reachable. CONCEPT:AU-KG.ingest.enterprise-source-extractor.

Input Schema

NameRequiredDescriptionDefault
queryYesSearch query to submit to SearXNG
pagenoNoPage number of results to fetch
enginesNoOptional list of specific search engines to use
languageNoLanguage code for search results (e.g. en-US)en-US
categoriesNoOptional list of categories to search in (e.g. general, news, science)

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.3.2

TDQS

A3.6/5.0
Behavior3/5

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

No annotations exist, so the description bears the full burden. It does disclose valuable behaviour: best-effort semantics with 'ingested' being None when no engine is reachable, and the write shape (:Document, :SearchQuery, :SearchEngine nodes with :resultOf/:fromEngine links). It omits whether repeated ingests dedupe/idempotent, whether it mutates an existing graph destructively, permission requirements, and rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded and short, but the body is broken by hard line breaks mid-sentence, and the trailing 'CONCEPT:AU-KG.ingest.enterprise-source-extractor' is internal taxonomy jargon that costs tokens without helping an agent decide or invoke. Roughly one of three sentences does no work.

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 five-parameter tool with a full output schema, the description covers purpose, the write target, and the failure mode (`ingested` is None), which is enough to call it correctly; return-value detail is rightly left to the output schema. The remaining gap is graph-side side effects and idempotency on repeat calls.

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 all five parameters carry their own descriptions, so the schema does the heavy lifting. The description adds no parameter-level meaning (e.g. engine naming conventions, category values, paging interaction with ingestion), so baseline 3 applies.

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?

States a specific compound verb plus resource: run a SearXNG search AND ingest results into epistemic-graph, naming the target graph and the node/link types created. That ingestion clause is exactly what separates it from searxng_search_app and web_search, without needing to name them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'Wire-First: performs the search, then pushes each result into the knowledge graph' line implies the selection condition (you want results persisted, not just returned), but never states when to prefer this over searxng_search_app or web_search, nor any prerequisites such as an existing graph connection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.