Agency Mcp Server

io.github.npupko由 GitHub 账号 npupko 发布v0.2.2更新于 Oct 24, 2026

On-demand access to 150+ specialist AI agent templates — search, browse, and spawn agents

概览

AI 生成的概览

一个本地 MCP 服务器,为助手提供 150 多个专家代理模板的可搜索库,并能针对任务启动最匹配的代理。

可以做什么
按任务描述搜索专家代理模板,可限定某个分部,返回匹配项及启动说明和文件路径。浏览分部,或列出某个分部下的代理。检查索引新鲜度:代理数量、上次更新时间、是否有可用更新。从 git 拉取最新模板并在不重启的情况下重建搜索索引。以资源形式读取完整代理索引和分部列表,或使用提示词按任务描述取得最匹配的代理。
适用场景
适合希望助手扮演专家角色(例如游戏经济设计师、安全审计员或技术文档撰写者),又不想把所有代理文件都装入上下文窗口的场景。也适合偏好按需查找代理、而非长期加载整个代理目录的用户。
运行要求
通过 stdio 在本地运行,通常经 npx 启动,需要 Node.js 和网络访问。首次启动会从 git 仓库克隆模板到本地缓存,并定期刷新。可选环境变量:AGENCY_AGENTS_PATH、AGENCY_REPO_URL、AGENCY_AUTO_UPDATE、AGENCY_UPDATE_INTERVAL。未声明身份验证。
安装前请注意
首次运行会克隆第三方 git 仓库,默认每 24 小时自动拉取更新;AGENCY_REPO_URL 可指向任意仓库,请只使用可信来源。启动的代理会把完整 Markdown 正文作为系统提示加载,内容可能很大并影响助手行为。将 AGENCY_AGENTS_PATH 指向本地目录可避免克隆。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Agency Mcp Server,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

agency-mcp-server

[npm version] [JSR] [CI] [License: MIT]

One MCP config entry. 150+ specialist agents on demand. No manual setup.

Your AI assistant is a generalist. Sometimes you need a specialist -- a game economy designer, a security auditor, a technical writer. This MCP server gives your assistant instant access to 150+ expert agent templates. Describe what you need, it finds the right agent and spawns it.

You: "Help me design a balanced game economy"Claude: [searches -> finds Game Economy Designer -> spawns it -> expert response]

Templates auto-fetch on first run from agency-agents and stay updated. You don't touch a thing.

Why not just install agents locally?

You can. The agency-agents install script copies all 160+ agent files directly into your tool's config directory (e.g. ~/.claude/agents/). It works -- but every agent's name and description is loaded into the context window of every conversation, whether you use them or not.

We measured it:

ApproachContext costWhen
Installed agents (~/.claude/agents/)~8,300 tokensEvery conversation, always
MCP server (idle)~55 tokensEvery conversation
MCP server (searching)~350 tokensOnly when you search
MCP server (using an agent)~2,700 tokensOnly when you spawn one (median)

That's a 150x reduction in baseline context usage. You get the same 160+ agents, but you only pay for the one you're actually using.

How we measured this

Installed agents (8,300 tokens): We ran the agency-agents install script (install.sh --tool claude-code), which copied 162 agent files to ~/.claude/agents/. Then opened a fresh Claude Code session and ran /context. Claude Code reported "Custom agents: 8.3k tokens" -- loaded into every conversation regardless of whether any agent is used.

MCP idle (55 tokens): With the MCP server configured instead, /context shows only the two deferred tool names (agency_search, agency_browse) and a brief server description in the system prompt. No agent data is loaded.

MCP searching (350 tokens): Measured by tokenizing the full JSON tool schemas that get loaded when the assistant calls ToolSearch to resolve the agency_search and agency_browse tools. Counted with @anthropic-ai/tokenizer.

MCP using an agent (2,700 tokens): The median token count across all 145 agent files, measured with @anthropic-ai/tokenizer. Only the single agent file you're actually using gets loaded into context. The range is 383–12,724 tokens depending on the agent (p25: 1,549, p75: 3,584).

Quick Start

Claude Code

As a plugin:

bash
/plugin marketplace add npupko/agency-mcp-server/plugin install agency@agency-mcp-server

Or via CLI:

bash
claude mcp add agency -- npx -y agency-mcp-server

Cursor, Windsurf, and other MCP clients

Add to your MCP config:

json
{  "mcpServers": {    "agency": {      "command": "npx",      "args": ["-y", "agency-mcp-server"]    }  }}

That's it. First launch clones templates to ~/.cache/agency-mcp-server/ and pulls updates every 24 hours.

Verify it works

Ask your assistant:

"Search for a game economy designer agent"

You should see results from the agency_search tool. If it's the first run, templates will auto-download (~30 seconds).

How It Works

Your assistant gets four tools:

  1. agency_search(query, division?) -- describe a task, get matching agents with spawn instructions
  2. agency_browse(division?) -- explore divisions and agents when you want to see what's available
  3. agency_status() -- check index freshness: agent count, last update time, whether an update is available
  4. agency_update() -- pull latest templates from git and rebuild the search index without restarting

When you ask for help with something specific, your assistant calls agency_search, picks the best match, and spawns a subagent with that specialist's full system prompt. You get an expert response without ever touching a config file.

What's available

Agents are organized into divisions:

DivisionExamples
EngineeringSoftware Architect, DevOps Engineer, Technical Writer
DesignUI Designer, UX Researcher, Design Systems
Game DevelopmentGame Economy Designer, Game Mechanics Designer
MarketingContent Strategist, SEO Specialist, Email Marketing
Security & SpecializedSecurity Auditor, Data Scientist, Legal Analyst
...and moreAcademic, Sales, Strategy, Support, Testing, Spatial Computing

Configuration

All configuration is through environment variables in your MCP config:

VariableDefaultDescription
AGENCY_AGENTS_PATH~/.cache/agency-mcp-server/agency-agentsPath to agent templates. Set this to use your own templates instead of auto-cloning
AGENCY_REPO_URLhttps://github.com/msitarzewski/agency-agents.gitGit repo to clone templates from. Point at your fork
AGENCY_AUTO_UPDATEtrueSet to false to disable automatic pulls
AGENCY_UPDATE_INTERVAL24Hours between update checks

Use your own templates

Point at a local directory:

json
{  "mcpServers": {    "agency": {      "command": "npx",      "args": ["-y", "agency-mcp-server"],      "env": {        "AGENCY_AGENTS_PATH": "/path/to/your/agent-templates"      }    }  }}

Or clone from your own repo:

json
{  "mcpServers": {    "agency": {      "command": "npx",      "args": ["-y", "agency-mcp-server"],      "env": {        "AGENCY_REPO_URL": "https://github.com/yourorg/custom-agents.git"      }    }  }}

Template format

Each agent is a Markdown file with YAML front-matter, organized by division:

engineering/  software-architect.md  devops-engineer.mddesign/  ui-designer.mdgame-development/  game-economy-designer.md
yaml
---name: Software Architectdescription: Expert software architect specializing in system design...---
Full agent system prompt goes here.

The server indexes the name and description fields for search. The full Markdown body becomes the agent's system prompt when spawned.

MCP Interface

Tools

  • agency_search(query, division?) -- find agents by task description, returns matches with file paths and a ready-to-use spawn template
  • agency_browse(division?) -- list all divisions, or list agents within a specific division
  • agency_status() -- check index freshness: agent count, last update time, whether an update is due
  • agency_update() -- pull latest templates from git and rebuild the search index at runtime

Resources

  • agency://agents -- full agent index as JSON
  • agency://divisions -- division list with counts and examples

Prompts

  • use-agent -- describe a task, get the best-matching agent with spawn instructions

Development

bash
npm installnpm run build
# Run with auto-fetched templatesnode dist/index.js
# Run with local templatesAGENCY_AGENTS_PATH=./my-agents node dist/index.js
# Type checkingnpm run typecheck
# MCP Inspectornpm run inspect

Credits

Agent templates from agency-agents by @msitarzewski.

License

MIT

来源:README.md,提交 e82d938

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v0.2.2最新Sep 16, 2026

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