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Web3 研究 MCP

Community
aaronjmars

Deep Research for crypto - free & fully local

Publisheraaronjmars
Repositoryweb3-research-mcp
LanguageTypeScript
Forks
51
Stars
154
Available tools
9
Transport typestdio
Categories
LicenseMIT
Links
  • Connect tools to AI workflows

    Web3 研究 MCP exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 9 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    154 stars and 51 forks from the linked repository.

Web3 Research MCP

smithery badge

Deep Research for crypto - free & fully local 🧠

🚀 Preview

Preview Preview2

🧠 Features

  • Comprehensive Research: Gather detailed information about any cryptocurrency token
  • Multi-Source Analysis: Research across multiple sources including CoinGecko, CoinMarketCap, DeFiLlama, and more
  • Structured Reporting: Generate detailed reports covering technical fundamentals, market data, social sentiment, and more
  • Resource Management: Automatically stores search results and content for reference
  • Status Tracking: Track research progress through different stages and sections

📋 Requirements

  • Node.js (v16 or higher)

🔧 Installation & Setup

Installing via Smithery

To install web3-research-mcp for Claude Desktop automatically via Smithery:

bash
npx -y @smithery/cli install web3-research-mcp --client claude

🔌 Using with Claude Desktop

Edit your Claude Desktop config file

  • Windows: %APPDATA%\Claude\claude_desktop_config.json
  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Add this to your Claude Desktop configuration file:

json
{
  "mcpServers": {
    "web3-research-mcp": {
      "command": "npx",
      "args": ["-y", "web3-research-mcp@latest"]
    }
  }
}

Then restart Claude Desktop

🔌 Using with Cursor

Go to: Settings -> Cursor Settings -> MCP -> Add new global MCP server Paste this into your Cursor ~/.cursor/mcp.json file. See Cursor MCP docs for more info.

json
{
  "mcpServers": {
    "web3-research-mcp": {
      "command": "npx",
      "args": ["-y", "web3-research-mcp@latest"]
    }
  }
}

Then restart Cursor

🛠️ Tools

create-research-plan

Creates a structured research plan for a token.

Parameters:

  • tokenName: Full name of the token
  • tokenTicker: Ticker symbol of the token

search

Performs a web search and returns the results.

Parameters:

  • query: Search query
  • searchType: Type of search (web, news, images, videos)

research-with-keywords

Searches for a token with specific keywords and saves the results.

Parameters:

  • tokenName: Name of the token
  • tokenTicker: Ticker symbol
  • keywords: Array of keywords to search for

update-status

Updates the status of a research section.

Parameters:

  • section: Section name to update (e.g., 'projectInfo', 'technicalFundamentals')
  • status: New status for the section (planned, in_progress, completed)

fetch-content

Fetches content from a URL and saves it as a resource.

Parameters:

  • url: URL to fetch content from
  • format: Output format (text, html, markdown, json)

list-resources

Lists all available resources that have been saved.

search-source

Searches for information about a token from a specific source.

Parameters:

  • tokenName: Name of the token
  • tokenTicker: Ticker symbol
  • source: Source to search (e.g., 'CoinGecko', 'DeFiLlama', 'News')

coingecko-data

Fetches live market data directly from the CoinGecko public API — price, market cap, 24h/7d/30d changes, ATH/ATL, circulating supply, contract addresses across chains, and social/dev links. Bypasses the 403 issues of HTML scraping.

Parameters:

  • tokenName: Full name of the token (e.g., 'Bitcoin')
  • tokenTicker: Ticker symbol (e.g., 'BTC')

No API key required. Uses the free public tier (~30 req/min).

Optional: set COINGECKO_API_KEY in the environment to use a CoinGecko Pro API key. When set, requests are sent to https://pro-api.coingecko.com/api/v3 with the x-cg-pro-api-key header. Requests time out after 15s.

coingecko-search

Searches CoinGecko's coin index and returns candidate matches with their CoinGecko IDs. Useful when the ticker is ambiguous (e.g., multiple tokens with the same symbol).

Parameters:

  • query: Search query — name, ticker, or contract address

coingecko-tickers

Fetches active exchange listings for a token from CoinGecko — which CEXs/DEXs trade the pair, per-venue 24h USD volume, last price, bid-ask spread, trust score, and trade URL. Sorted by 24h USD volume desc; anomalies and stale prints are filtered out. Bypasses HTML scraping for the "where does this token actually trade" question.

Parameters:

  • tokenName: Full name of the token (e.g., 'Bitcoin')
  • tokenTicker: Ticker symbol (e.g., 'BTC')
  • limit: How many top venues to return (default 15, max 50)

No API key required. Uses the free public tier. Requests time out after 15s.

defillama-data

Fetches protocol data directly from the DeFiLlama public API — total TVL, per-chain TVL breakdown, fees (24h/7d/30d/all-time), token addresses, fundraising rounds, and links. Bypasses HTML scraping for the most common DeFi-protocol lookup.

Parameters:

  • tokenName: Full protocol/token name (e.g., 'Uniswap')
  • tokenTicker: Ticker symbol (e.g., 'UNI')

No API key required. Uses the free public API. Requests time out after 15s. The protocol index is cached for 5 minutes per process to avoid hammering /protocols on every call.

defillama-search

Searches DeFiLlama's protocol index and returns candidate matches with their slugs, TVL, and category. Useful when the ticker is ambiguous (e.g., multiple protocols with similar names).

Parameters:

  • query: Search query — protocol name, ticker, or slug

📝 Prompts

token-research

Initiates comprehensive research on a cryptocurrency token.

Parameters:

  • tokenName: Full name of the cryptocurrency token
  • tokenTicker: Ticker symbol of the token (e.g., BTC, ETH)

🧠 How It Works

  1. When research begins, a structured plan is created covering all aspects of the token
  2. The server performs searches across multiple sources for information
  3. Search results are stored as resources that can be referenced
  4. The research progresses through different sections, with status tracking
  5. A comprehensive report is generated covering all aspects of the token

⚠️ Limitations

  • Some websites block web scraping, so direct content fetching may fail with 403 errors
  • Relies on search results which may not always be comprehensive
  • Rate limits may apply to search operations

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "aaronjmars-web3-research-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "web3-research-mcp"
      ]
    }
  }
}

Use Web3 研究 MCP MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Web3 研究 MCP is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Web3 研究 MCP as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Web3 研究 MCP MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "aaronjmars-web3-research-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "web3-research-mcp"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Web3 研究 MCP MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Web3 研究 MCP MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Web3 研究 MCP to help me with this task?
Web3 研究 MCP
Sure. I read it.
Here is what I found using Web3 研究 MCP.

Frequently asked questions

What is the Web3 研究 MCP MCP server used for?

Web3 研究 MCP is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Web3 研究 MCP MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Web3 研究 MCP with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Web3 研究 MCP MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Web3 研究 MCP connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Web3 研究 MCP MCP to TypingMind?

Web3 研究 MCP runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Web3 研究 MCP MCP provide in TypingMind?

Web3 研究 MCP exposes 9 MCP tools that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Web3 研究 MCP MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Web3 研究 MCP requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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