该项目提供了一个MCP服务器，允许您使用SearxNG实例执行网页搜索。它提供了一个工具，可供其他兼容 MCP 的应用程序（例如 Goose）调用。
将SEARXNG_BASE_URL环境变量设置为 SearxNG 实例的基本 URL。
安装软件包： pip install .
Related MCP server: mcp_server_searXNG
**安装扩展：**安装软件包后，您可以在 Goose 中将此 MCP 服务器添加为扩展。
**在 Goose 中添加扩展：**转到设置 > 扩展 > 添加。
**设置扩展类型：**将类型设置为StandardIO。
**提供扩展详细信息：**提供扩展的 ID、名称和描述。
**设置命令：**在“命令”字段中，使用uv run提供可执行文件的绝对路径。例如：
uv run /full/path/to/mcp-searxng-search/.venv/bin/mcp-searxng-search确保将/full/path/to/mcp-searxng-search替换为项目目录的实际路径。
**使用扩展程序：**集成后，您就可以在 Goose 中开始使用扩展程序了。打开 Goose 聊天界面，并根据需要调用您的工具。您可以通过询问“您有哪些工具？”来验证 Goose 是否已从您的自定义扩展程序中获取工具。
该工具 ID 为searxng_search 。它接受两个参数： query （搜索查询）和max_results （返回的最大结果数，默认为 30）。
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.
