tf2-wiki-mcp

tf2-wiki-mcp

Gives LLMs live, accurate access to Team Fortress Wiki content, enabling search, page retrieval, structured weapon stats, patch notes, and other TF2-specific information.

Category
访问服务器

README

tf2-wiki-mcp

An MCP server that exposes the Team Fortress Wiki to LLM clients (Claude Desktop, Claude Code, Cursor, VS Code) over stdio.

Install in VS Code Install in VS Code Insiders Install in Visual Studio Install in Cursor

Not affiliated with Valve Corporation or the Team Fortress Wiki contributors. Team Fortress 2 is a trademark of Valve Corporation. This is unofficial fan tooling.

What it does

Gives an LLM live, accurate access to TF2 wiki content so it stops guessing at weapon stats, patch notes, and cosmetic details.

  • Generic wiki access — search, fetch pages, get summaries, list recent changes.
  • TF2 domain tools — structured weapon stats, class loadouts, cosmetic lookup, event item lists, patch notes.

Tools

Tool Purpose
search_wiki(query, limit) Full-text search
get_page(title, format) Page content as wikitext, plain, or html
get_page_summary(title) Lead-section plaintext
get_page_sections(title) Section TOC for selective fetching
get_recent_changes(limit, namespace) Recently edited pages
get_weapon_stats(weapon_name) Parsed weapon infobox → structured stats
list_class_loadout(class_name) All weapons in a class's wiki category
get_cosmetic(name) Cosmetic item infobox params
list_event_items(event_name) Items added in an update/event
get_patch_notes(update_name) Patch notes for a specific update

Plus: resource tf2wiki://main, prompts analyze_loadout and compare_weapons.

Installation

Requires uv (which manages the Python toolchain for you). Once installed, uvx tf2-wiki-mcp will fetch and run the server on demand — no manual clone needed.

Standard config works in most MCP clients:

{
  "mcpServers": {
    "tf2-wiki-mcp": {
      "command": "uvx",
      "args": ["tf2-wiki-mcp"],
      "env": {}
    }
  }
}

<details> <summary>Claude Desktop</summary>

Add the standard config above to claude_desktop_config.json:

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

Restart Claude Desktop. The TF2 wiki tools appear in the tool picker. </details>

<details> <summary>Claude Code</summary>

claude mcp add tf2-wiki-mcp -- uvx tf2-wiki-mcp

</details>

<details> <summary>Codex</summary>

Add to ~/.codex/config.toml:

[mcp_servers.tf2-wiki-mcp]
command = "uvx"
args = ["tf2-wiki-mcp"]

See the Codex MCP docs. </details>

<details> <summary>Cursor</summary>

Install in Cursor

Or add the standard config to ~/.cursor/mcp.json. </details>

<details> <summary>Gemini CLI</summary>

Add the standard config to your Gemini CLI settings.json per the Gemini CLI MCP guide. </details>

<details> <summary>VS Code</summary>

Install in VS Code

Or via the VS Code CLI:

code --add-mcp '{"name":"tf2-wiki-mcp","command":"uvx","args":["tf2-wiki-mcp"],"env":{}}'

See the VS Code MCP guide for details. </details>

<details> <summary>VS Code Insiders</summary>

Install in VS Code Insiders

Or via the CLI:

code-insiders --add-mcp '{"name":"tf2-wiki-mcp","command":"uvx","args":["tf2-wiki-mcp"],"env":{}}'

</details>

<details> <summary>Visual Studio</summary>

Install in Visual Studio

Or manually:

  1. Open the GitHub Copilot Chat window.
  2. Click the tools icon (🛠️) → + Add Server.
  3. Fill in: Server ID tf2-wiki-mcp, Type stdio, Command uvx, Arguments tf2-wiki-mcp.
  4. Save.

See the Visual Studio MCP docs. </details>

<details> <summary>GitHub Copilot Coding Agent</summary>

{
  "mcpServers": {
    "tf2-wiki-mcp": {
      "command": "uvx",
      "args": ["tf2-wiki-mcp"],
      "env": {},
      "type": "local",
      "tools": ["*"]
    }
  }
}

Add this in repository settings under Copilot → Coding agent. See the Copilot Coding Agent MCP docs. </details>

Development

Run from a local clone instead of PyPI:

git clone https://github.com/yusufaf/tf2-wiki-mcp
cd tf2-wiki-mcp
uv sync --dev
uv run pytest              # offline, uses recorded cassettes
uv run pytest --live       # also hits the real wiki

To point an MCP client at the local checkout:

{
  "mcpServers": {
    "tf2-wiki-mcp": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/tf2-wiki-mcp", "tf2-wiki-mcp"]
    }
  }
}

Licensing & Attribution

Code: MIT (see LICENSE).

Wiki content: Not redistributed. This project contains zero scraped wiki data. All page content is fetched live from wiki.teamfortress.com at the user's request and returned directly to the user's LLM client — the same posture as a browser extension.

Wiki content is © its respective contributors under Valve's Steam Subscriber Agreement (Game Site terms). Users of this tool are responsible for compliance with Valve's terms. Code license ≠ content license: MIT covers this codebase, not the wiki content it fetches.

Requests to the wiki include a descriptive User-Agent (tf2-wiki-mcp/<version> (https://github.com/yusufaf/tf2-wiki-mcp)) per MediaWiki etiquette and respect maxlag/Retry-After responses.

Contributing

Don't check in scraped wiki text. Infobox examples for parser tests are fine (short, fair-use-grade fixtures). Full page dumps are not.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选