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AstroFabric MCP 服务器

AstroFabric 是一个智能体 AI 平台:自主智能体在增长、营收和数字化运营中,从目标到成果全程推进工作。这个 MCP 服务器将整个平台带到你的编辑器后面——用自然语言给它一个开放式的目标,它就会规划并执行任务:市场和竞争情报、网站和技术审计、基于实时购买信号验证的潜在客户名单、外联、内容和需求计划、从图片和视频到设计页面的成品创意,以及交付到 100+ 个已连接的应用——并连同背后的证据一起返回最终结果。

本仓库既是托管 AstroFabric MCP 端点的连接器(添加远程 URL 和你的 API 密钥即可开始工作),也是面向偏好本地进程的客户端的可运行 stdio 服务器——参见本地运行。

前提条件

  1. 一个付费套餐的 AstroFabric 工作区——在 https://www.astrofabric.ai 注册

  2. 一个 API 密钥:在 AstroFabric 控制台中,打开 API 密钥 → 创建密钥,并复制 ek_live_... 令牌(只显示一次)。

Related MCP server: Agentic Control Framework (ACF)

设置

每个客户端都使用相同的端点(https://www.astrofabric.ai/api/mcp)和相同的密钥。选择你的客户端:

claude mcp add --transport http astrofabric https://www.astrofabric.ai/api/mcp --header "Authorization: Bearer ek_live_YOUR_KEY_HERE"

添加一个使用裸 URL 的自定义连接器,并在提示时登录——服务器会引导你完成 AstroFabric 登录,并将一个作用域密钥交给客户端,你可以从控制台撤销该密钥:

https://www.astrofabric.ai/api/mcp

添加到 .cursor/mcp.json(项目)或 ~/.cursor/mcp.json(全局):

{
  "mcpServers": {
    "astrofabric": {
      "url": "https://www.astrofabric.ai/api/mcp",
      "headers": { "Authorization": "Bearer ek_live_YOUR_KEY_HERE" }
    }
  }
}

添加到 cline_mcp_settings.json(Cline → MCP Servers → 配置,或 Remote Servers → 按 URL 添加):

{
  "mcpServers": {
    "astrofabric": {
      "type": "streamableHttp",
      "url": "https://www.astrofabric.ai/api/mcp",
      "headers": {
        "Authorization": "Bearer ek_live_YOUR_KEY_HERE"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

添加到 .vscode/mcp.json:

{
  "servers": {
    "astrofabric": {
      "type": "http",
      "url": "https://www.astrofabric.ai/api/mcp",
      "headers": { "Authorization": "Bearer ek_live_YOUR_KEY_HERE" }
    }
  }
}

添加到 ~/.codex/config.toml:

[mcp_servers.astrofabric]
url = "https://www.astrofabric.ai/api/mcp"
http_headers = { "Authorization" = "Bearer ek_live_YOUR_KEY_HERE" }

添加到 ~/.gemini/settings.json 的 mcpServers 下:

{
  "astrofabric": {
    "httpUrl": "https://www.astrofabric.ai/api/mcp",
    "headers": { "Authorization": "Bearer ek_live_YOUR_KEY_HERE" }
  }
}

添加到 ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "astrofabric": {
      "serverUrl": "https://www.astrofabric.ai/api/mcp",
      "headers": { "Authorization": "Bearer ek_live_YOUR_KEY_HERE" }
    }
  }
}

快捷方式:安装 CLI 后,运行 astrofabric connect <client> 会打印你的客户端配置,密钥已自动填入。

如果你的客户端无法发送请求头,密钥也可以放在 URL 中:https://www.astrofabric.ai/api/mcp?key=ek_live_YOUR_KEY_HERE——请将整个 URL 视为机密。

两个工具面

  • 默认(上面的 URL):一个工具,mission_agent——自主任务运行器。最适合编辑器:工具列表中一个条目,背后是整个平台。

  • 完整目录:在 URL 后追加 ?tools=all,还会暴露所有单独的数据和执行工具(SEO 和关键词情报、广告库、买家意图搜索、邮箱查找和验证、创意生成等),以及你的工作区已连接的任何 MCP 服务器。

使用方法

要求结果,而不是步骤:

"建立一份 40 家对仓库机器人表现出购买意向的公司的名单,验证邮箱,并为每家起草一封首次触达邮件。"

"拆解 competitor.com——广告、关键词、招聘、定位——并给我三个抢占市场的打法。"

结果以 [thread:<id>] 行开头。在后续消息中传入该 id 作为 thread_id("验证这些邮箱"、"将它们格式化为 LinkedIn Ads"),智能体会带着之前所有已询问和已交付内容的完整记忆继续工作。它可能会回复一个澄清问题;用同样的方式回答即可。

本地运行(stdio)

本仓库也是一个可运行的 stdio MCP 服务器——相同的 mission_agent 工具,通过 AstroFabric 平台使用你的密钥执行任务。适用于偏好本地进程而非远程 URL 的客户端:

{
  "mcpServers": {
    "astrofabric": {
      "command": "npx",
      "args": ["-y", "github:sam1siam/astrofabric-mcp"],
      "env": { "ASTROFABRIC_API_KEY": "ek_live_YOUR_KEY_HERE" }
    }
  }
}

或者使用 Docker:docker build -t astrofabric-mcp . && docker run -i --rm -e ASTROFABRIC_API_KEY=ek_live_... astrofabric-mcp

偏好终端?CLI

同一个平台也以零依赖 CLI 的形式提供(npm 上的 astrofabric):

npm install -g astrofabric

astrofabric login --key ek_live_...
astrofabric run "Audit example.com and list the top 5 fixes"

在编码智能体(Claude Code、Codex、Gemini CLI)中,整个设置只需一个提示词:"帮我安装 AstroFabric CLI,然后运行 astrofabric docs 来了解它。" 而 astrofabric connect cursor(或 claude-code、codex、gemini、vscode、windsurf)会为每个客户端打印可直接粘贴的 MCP 配置。

注意事项

  • 任务会从你的工作区积分中扣费;控制台的用量页面会显示每次运行。

  • 对已连接系统的写入遵循你工作区的审批设置——自主写入可以被门控到人工审批队列。

  • 也支持通过标准发现机制使用 OAuth 2.1(动态客户端注册 + PKCE),适用于偏好登录而非密钥的客户端。

许可证

本连接器仓库的内容采用 MIT 许可证。AstroFabric 平台是商业服务。

Available Tools

1 tool
mission_agentMission Agent (autonomous missions)A

Give AstroFabric an open-ended objective across growth, revenue or digital operations in plain language and it plans and executes the whole mission autonomously: research markets, audit sites, analyze competitors, build prospect lists from buyer intent and signals, verify emails, draft outbound, produce creative, run data work in the code sandbox, deliver into connected apps, and more. The result starts with a [thread:] line - pass that id as thread_id on follow-ups ("verify those emails", "format them for LinkedIn Ads") and the agent remembers everything already asked and delivered. It may reply with a clarifying question; answer it the same way. Prefer this over the individual tools for anything multi-step.

NameRequiredDescriptionDefault
objectiveYesWhat to accomplish, e.g. "Build a list of 100 companies showing high interest in building insulation"
thread_idNoThe thread id from a previous result, to continue that mission with full memory

TDQS

A4.7/5.0
Behavior5/5

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

The description goes far beyond the annotations by explaining autonomous planning/execution, the [thread:<id>] result prefix, memory across follow-ups, and the possibility of a clarifying question. It also mentions capabilities like running data work in the code sandbox and delivering into connected apps, which gives an agent a concrete sense of side effects. No contradiction with annotations was mentioned.

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 dense but every sentence adds new information: core functionality, key output format, continuation pattern, clarification behavior, and usage preference. The front-loaded sentence explains scope first, followed by operational details. It is aligned with the best vertex for high-functioning agents.

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

Completeness5/5

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

Given the tool is an autonomous agent with no sibling tools and no output schema, the description surprisingly covers everything essential: what the objective should be, what outcomes can be expected, how to start, how to continue, how to handle clarifying questions, and when to prefer it. The emptiness of a formal output schema is compensated by the explicit mention of the thread line at the beginning. This is a high-quality, self-sufficient description.

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 input schema already covers 100% of parameters with meaningful descriptions and an example for objective. The description reinforces thread_id usage in the continuation context but does not materially add new semantic details beyond the schema. This matches the baseline where the schema carries the parameter-load.

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 that the tool takes a plain-language objective and autonomously plans/executes a multi-step mission across growth, revenue, and digital operations. It lists concrete actions (research, audits, competitor analysis, email verification) and explicitly contrasts itself with individual tools. This goes well beyond a vague restatement of the name.

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

Usage Guidelines5/5

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

It explicitly says 'Prefer this over the individual tools for anything multi-step', which specifies when to use it and strongly implies when not to use it (single-step operations). It also documents the follow-up workflow using thread_id and how to respond to clarifying questions. This is strong, actionable usage direction.

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 updatev1.1.0
    • First observedmission_agent

TDQS

A4.4/5.0

Scored across 1 tool

Disambiguation5/5

The server exposes exactly one tool, so there is no possibility of confusing it with another. All tool selection is unambiguous by definition.

Naming Consistency4/5

The lone tool name 'mission_agent' is clear and internally consistent, but the pattern cannot be validated across a set. No naming conflicts or inconsistencies exist.

Tool Count3/5

A single tool is borderline for a server that describes a broad range of capabilities. The one mission_agent can be a legitimate monolithic entry point, but the count feels thin given the scope.

Completeness3/5

The mission_agent handles open-ended missions and follow-ups via thread_id, but there are no explicit controls for listing, canceling, or monitoring missions. The description hints at individual granular tools that are absent from this server surface.

Maintenance

ActivitySlowing
ResponsivenessNo recent community issues

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