このプロジェクトは、 SearxNGインスタンスを使用してWeb検索を実行できるMCPサーバーを提供します。Gooseなどの他のMCP対応アプリケーションから呼び出せるツールを公開しています。
SEARXNG_BASE_URL環境変数を SearxNG インスタンスのベース URL に設定します。
パッケージをインストールします: pip install .
Related MCP server: mcp_server_searXNG
**拡張機能のインストール:**パッケージをインストールした後、この MCP サーバーを Goose の拡張機能として追加できます。
Goose で拡張機能を追加します。 [設定] > [拡張機能] > [追加] に移動します。
**拡張タイプを設定します。**タイプを StandardIO に設定します。
**拡張機能の詳細を入力します。**拡張機能の ID、名前、説明を入力します。
**コマンドを設定します。**コマンドフィールドに、 uv runを使用して実行ファイルへの絶対パスを入力します。例:
uv run /full/path/to/mcp-searxng-search/.venv/bin/mcp-searxng-search/full/path/to/mcp-searxng-searchプロジェクト ディレクトリへの実際のパスに置き換えてください。
**拡張機能の使用：**連携が完了したら、Gooseで拡張機能の使用を開始できます。Gooseのチャットインターフェースを開き、必要に応じてツールを呼び出します。「どんなツールをお持ちですか？」と尋ねることで、Gooseがカスタム拡張機能からツールを取得したかどうかを確認できます。
ツールIDはsearxng_searchです。このツールは2つのパラメータ（ query （検索クエリ）とmax_results （返される結果の最大数、デフォルトは30））を受け取ります。
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.
