이 프로젝트는 SearxNG 인스턴스를 사용하여 웹 검색을 수행할 수 있는 MCP 서버를 제공합니다. 또한, Goose와 같은 다른 MCP 호환 애플리케이션에서 호출할 수 있는 도구를 제공합니다.
SEARXNG_BASE_URL 환경 변수를 SearxNG 인스턴스의 기본 URL로 설정합니다.
패키지를 설치합니다: pip install .
Related MCP server: mcp_server_searXNG
확장 기능 설치: 패키지를 설치한 후 이 MCP 서버를 Goose의 확장 기능으로 추가할 수 있습니다.
Goose에서 확장 프로그램을 추가하려면 설정 > 확장 프로그램 > 추가로 이동하세요.
확장 유형을 설정합니다. 유형을 StandardIO로 설정합니다.
확장 프로그램 세부 정보 제공: 확장 프로그램의 ID, 이름, 설명을 제공하세요.
명령 설정: 명령 필드에 uv run 을 사용하여 실행 파일의 절대 경로를 입력하세요. 예:
지엑스피1
/full/path/to/mcp-searxng-search 프로젝트 디렉토리의 실제 경로로 바꿔야 합니다.
확장 프로그램 사용: 통합이 완료되면 Goose에서 확장 프로그램을 사용할 수 있습니다. Goose 채팅 인터페이스를 열고 필요에 따라 도구를 호출하세요. Goose가 사용자 지정 확장 프로그램에서 도구를 가져왔는지 확인하려면 "어떤 도구를 가지고 있나요?"라고 질문하세요.
도구 ID는 searxng_search 입니다. 두 개의 매개변수를 받습니다. query (검색 쿼리)와 max_results (반환할 최대 결과 수, 기본값은 30개)입니다.
Searches the web using a SearxNG instance and returns a list of results.
Args:
query: The search query.
max_results: The maximum number of results to return. Defaults to 30.
Returns:
A list of dictionaries, where each dictionary represents a search result
and contains the title, URL, and content snippet. Returns an error
message in a dictionary if the search fails.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| max_results | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool 'returns a list of results' and 'returns an error message in a dictionary if the search fails,' which adds some context about success and failure behaviors. However, it lacks details on rate limits, authentication needs, or other operational traits, leaving gaps in transparency for a tool that interacts with external services.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with a clear purpose statement followed by structured sections for 'Args' and 'Returns.' Each sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is reasonably complete. It covers the purpose, parameters, and return values, and the output schema likely details the result structure, reducing the need for extensive return explanations. However, it could improve by addressing potential errors or usage constraints more explicitly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics beyond the input schema. The schema has 0% description coverage, but the description explains that 'query' is 'The search query' and 'max_results' is 'The maximum number of results to return. Defaults to 30.' This clarifies the purpose and default value, compensating well for the low schema coverage. However, it doesn't detail constraints like query length or result range limits.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Searches the web using a SearxNG instance and returns a list of results.' It specifies the verb ('searches'), resource ('the web'), and mechanism ('using a SearxNG instance'), making it easy to understand what the tool does. However, since there are no sibling tools mentioned, it doesn't need to differentiate from alternatives, so it doesn't reach the highest score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage through its purpose statement but lacks explicit guidance on when to use this tool versus alternatives. With no sibling tools provided, there's no need to distinguish from them, but it doesn't offer any context on prerequisites, limitations, or best practices. This results in an implied usage scenario without detailed exclusions or recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0searxng_search1 field changedInput schema / titleAdded value: +"searxng_searchArguments"searxng_searchScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'searxng_search' has a clear, distinct purpose for web searching.
The single tool name 'searxng_search' follows a consistent pattern (noun_verb style), and with only one tool, there is no inconsistency to evaluate. The naming is straightforward and descriptive.
A single tool for a search server is too minimal for effective agent use. While it covers the core search function, it lacks supporting tools (e.g., for filtering, advanced queries, or handling different search types), making the scope feel thin and incomplete for a typical search domain.
The tool surface is severely incomplete for a search server. It only provides basic search functionality without any tools for related operations like image search, news search, autocomplete, or result management, leading to significant gaps that will limit agent capabilities.
MCP server for Google search results via SERP API
MCP server for Firecrawl — web search, scraping, and biomedical/arXiv paper search.
Docs: https://docs.keenable.ai/mcp-server Keenable is a free, remote MCP server that gives agents access to the web index. Search the web with ranked results and date/site filters, then fetch any indexed page as clean markdown. Works out of the box with no account or API key.
