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PXSR

MCP Smart Searcher

by PXSR

MCP 스마트 검색기

AI 기반 결과와 다중 엔진 웹 검색을 위한 스마트 MCP(Model Context Protocol) 서버입니다.

기능

  • 다중 엔진 검색 — 6개의 엔진(DuckDuckGo, Baidu, Juejin, GitHub, GitHub Code, Tavily)에서 동시에 검색

  • 웹 콘텐츠 추출 — 공개 URL에서 노이즈 제거 및 프롬프트 기반 필터링을 통해 깔끔한 텍스트 추출

  • 속도 제한 — 세마포어를 통한 내장 동시성 제어

  • 프록시 지원 — 엔진별 프록시 구성

  • 엔진 허용 목록 — 사용 가능한 엔진 제한

Related MCP server: evo-scry

설치

pip install -e ".[dev]"

사용법

서버 실행

python -m mcp_smart_searcher

MCP 클라이언트 구성

MCP 클라이언트 구성(예: Claude Desktop)에 추가하세요:

{
  "mcpServers": {
    "smart-searcher": {
      "command": "python",
      "args": ["-m", "mcp_smart_searcher"]
    }
  }
}

개발

# Run with MCP inspector
mcp dev src/mcp_smart_searcher/server.py

# Run tests
PYTHONPATH=src pytest

# Build
python -m build

구성

환경 변수

설명

기본값

DEFAULT_SEARCH_ENGINE

기본 검색 엔진

duckduckgo

ALLOWED_SEARCH_ENGINES

쉼표로 구분된 엔진 허용 목록

(모두 허용)

TAVILY_API_KEY

Tavily AI 검색 API 키

(없음)

GITHUB_TOKEN

GitHub API 토큰

(없음)

USE_PROXY

프록시 활성화

true

PROXY_URL

프록시 URL

http://127.0.0.1:7890

PROXY_ENGINES

프록시를 사용하는 쉼표로 구분된 엔진

(모두)

MAX_CONCURRENT_SEARCH

최대 동시 검색 요청 수

5

LOG_LEVEL

로깅 레벨

INFO

라이선스

Apache-2.0

Available Tools

2 tools
fetch_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
NameRequiredDescriptionDefault
urlYes
promptNo
max_charsNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv0.2.1
    • First observedfetch_web_content
    • First observedweb_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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