A
licenseA
qualityC
maintenanceMCP server for web crawling, searching, and AI-powered content extraction, supporting single-page, batch, and full-site crawling along with text, news, image, book, and video search.
8
1
MIT
MCP Smart Searcher
by PXSR
MCP 智能搜索器
一个用于多引擎网络搜索的智能 MCP（模型上下文协议）服务器，提供 AI 驱动的搜索结果。
功能特性
多引擎搜索 — 同时搜索 6 个引擎：DuckDuckGo、百度、掘金、GitHub、GitHub 代码、Tavily
网页内容提取 — 从任何公共 URL 获取并提取纯净文本，支持去噪和提示词引导过滤
速率限制 — 通过信号量实现内置并发控制
代理支持 — 每个引擎的独立代理配置
引擎白名单 — 限制可使用的搜索引擎
Related MCP server: evo-scry
安装
pip install -e ".[dev]"使用方法
运行服务器
python -m mcp_smart_searcherMCP 客户端配置
添加到你的 MCP 客户端配置中（例如 Claude Desktop）：
{
"mcpServers": {
"smart-searcher": {
"command": "python",
"args": ["-m", "mcp_smart_searcher"]
}
}
}开发
# Run with MCP inspector
mcp dev src/mcp_smart_searcher/server.py
# Run tests
PYTHONPATH=src pytest
# Build
python -m build配置
环境变量 | 描述 | 默认值 |
| 默认搜索引擎 |
|
| 逗号分隔的引擎白名单 | (全部允许) |
| Tavily AI 搜索 API 密钥 | (无) |
| GitHub API 令牌 | (无) |
| 启用代理 |
|
| 代理 URL |
|
| 使用代理的引擎（逗号分隔） | (全部) |
| 最大并发搜索请求数 |
|
| 日志级别 |
|
许可证
Apache-2.0
Available Tools
2 toolsfetch_web_contentA
Fetch and extract text content from any public URL.
Args: url: Public HTTP/HTTPS URL to fetch prompt: Optional hint for what to extract. When provided, the content is filtered to prioritize paragraphs most relevant to the prompt keywords. Useful for AI agents to focus on specific content (e.g., "extract code examples only", "summarize the main argument"). max_chars: Maximum characters to return (default 30000)
Returns: Extracted text content from the webpage, optionally filtered by prompt
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| prompt | No | ||
| max_chars | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A3.6/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It mentions 'any public URL' and optional filtering, but omits details on error handling, redirects, authentication, or rate limits. The transparency is basic.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with a clear header, Args list, and Returns section. It is not overly long, though the prompt examples could be slightly trimmed. Overall, it is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the basic inputs and output, but lacks details on error scenarios, maximum allowable characters, or non-HTTP content handling. With no annotations, more context would be beneficial for complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, requiring the description to add meaning. It does so by describing the URL as 'public HTTP/HTTPS URL,' the prompt as an optional filter with examples, and max_chars with its default. This adds significant value beyond the schema.
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?
The description clearly states 'Fetch and extract text content from any public URL,' providing a specific verb and resource. It distinguishes from the sibling 'web_search' by focusing on fetching a known URL rather than searching for URLs.
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 description explains the prompt parameter and default max_chars, implying usage for targeted extraction. However, it does not explicitly contrast with the sibling 'web_search' or state when not to use this tool, leaving room for ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
web_searchA
Search the web using one or more search engines simultaneously.
Args: query: Search query string (non-empty) engines: Search engines to use (duckduckgo, baidu, juejin, github, github_code, tavily) limit: Maximum number of results per engine (1-50, default 10)
Returns: Formatted search results from all specified engines
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| engines | No | ||
| limit | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears the full burden. It mentions simultaneous multi-engine searching and returns formatted results, but does not disclose rate limits, error handling for failed engines, or blocking behavior. Adequate but not comprehensive.
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?
The description is brief and well-structured, starting with the main purpose and then listing parameters in a clear args format. Every sentence serves a purpose with no redundancy.
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?
The tool has an output schema (not shown but indicated), so the description does not need to detail return values. With three parameters and a fairly broad search function, the description adequately covers the essentials, though more details on result format would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. It clarifies that the query must be non-empty, lists the available engines (partially compensating for missing enums), and specifies the limit range (1-50). However, it does not explain the default behavior when 'engines' is null.
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?
The description clearly states the verb 'search' and the resource 'web', and specifies the distinctive feature of using multiple search engines simultaneously. This differentiates it from the sibling tool 'fetch_web_content' which likely retrieves content from a single URL.
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 description implies usage for multi-engine web searches but does not explicitly state when to prefer this tool over the sibling or when not to use it. No exclusion criteria or alternative suggestions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
v0.2.1- First observed
fetch_web_content - First observed
web_search
TDQS
A3.8/5.0
Scored across 2 tools
Disambiguation5/5
The two tools have clearly distinct purposes: one fetches content from a specific URL, the other performs web searches. There is no overlap or ambiguity.
Naming Consistency5/5
Both tool names follow a consistent verb_noun pattern (fetch_web_content, web_search) using snake_case, making them predictable and easy to understand.
Tool Count3/5
With only 2 tools, the server is minimal but covers the basic search-and-fetch workflow. It feels slightly thin for a server named 'Smart Searcher' as it lacks additional features like content summarization or multi-URL fetch.
Completeness3/5
The surface covers the core operations of searching and fetching web content, but there are notable gaps such as no tool to fetch search results directly or to handle multiple URLs. Agents may need to combine calls manually.
Maintenance
ActivitySlowing
ResponsivenessNo issues
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