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Memory Store MCP Server

by divslingerx
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JavaScript
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内存存储 MCP 服务器

使用 Puppeteer 提供网络搜索功能的模型上下文协议 (MCP) 服务器。

特征

  • 通过 Google 实现网页搜索功能

  • 结构化 JSON 结果

  • 轻量级、无状态设计

  • 轻松与支持 MCP 的系统集成

Related MCP server: webmcp

安装

  1. 克隆存储库：

    git clone https://github.com/yourusername/mcp-server.git
cd mcp-server

  2. 安装依赖项：

    npm install

  3. 构建项目：

    npm run build

配置

使用以下环境变量在项目根目录中创建一个.env文件：

# Puppeteer configuration
PUPPETEER_EXECUTABLE_PATH=/path/to/chrome
PUPPETEER_HEADLESS=true

# Server settings
PORT=3000

用法

启动服务器：

npm start

该服务器将对 MCP 客户端开放。通过 MCP 使用的示例：

{
  "tool": "search_web",
  "arguments": {
    "query": "example search"
  }
}

发展

构建项目

npm run build

运行测试

npm test

代码检查

npm run lint

格式化

npm run format

执照

该项目根据 MIT 许可证获得许可 - 有关详细信息，请参阅LICENSE文件。

Available Tools

1 tool
search_webC

Search the web using Google

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the basic function without mentioning rate limits, authentication needs, result format, pagination, or any other operational characteristics that would help an agent understand how to interact with it effectively.

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 extremely concise at just four words, front-loaded with the core function, and contains no unnecessary information. Every word earns its place in communicating the essential purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what kind of results to expect, how they're structured, or any behavioral aspects like limitations or error handling, leaving significant gaps for agent understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the input schema already documents the single 'query' parameter adequately. The description doesn't add any additional meaning or context about the parameter beyond what's in the schema, which meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Search') and target ('the web using Google'), providing a specific verb+resource combination. However, with no sibling tools mentioned, there's no opportunity to distinguish from alternatives, which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus other search methods or tools, nor does it mention any prerequisites or exclusions. It simply states what the tool does without contextual usage information.

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. 1 tool update
    • First observedsearch_web
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'search_web' has a single, distinct purpose that cannot be confused with any other tool in this set.

Naming Consistency5/5

The single tool name follows a clear verb_noun pattern ('search_web'), and since there is only one tool, consistency is inherently perfect with no deviations or mixed conventions to evaluate.

Tool Count2/5

A single tool is too few for a server named 'Memory Store MCP Server', which implies functionality related to storing, retrieving, or managing memory data. The tool 'search_web' does not align with this domain, making the count inappropriate and mismatched with the apparent scope.

Completeness1/5

The tool set is severely incomplete for the inferred domain of memory storage. There are no tools for creating, reading, updating, or deleting memory entries, which are essential operations for a memory store, leaving significant gaps that will cause agent failures.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • webmcp by AuthBits

    webmcpofficial

    AuthBits
    A
    license
    Not graded
    quality
    C
    maintenance
    MCP server for web search and content extraction using DuckDuckGo or SearXNG, with Playwright-based fetching and LLM-powered data extraction.
    139
    MIT

  • puppeteer

    silvino-av
    F
    license
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    MCP server for headless browser automation using Puppeteer, enabling AI to navigate, click, fill forms, take screenshots, and execute JavaScript on web pages.
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  • evo-scry

    krich11
    A
    license
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    quality
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    MCP server for internet search via direct Google and DuckDuckGo HTML scraping with AI-powered result normalization and optional summarization, requiring no API keys for search.
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