Niels Langereis

Essays, an enterprise AI map and a skill-matrix gap analysis on AI adoption and junior formation.

我该使用它吗

质量与安全性

A
描述质量
100%
模式完整度
82%
命名质量
97%
投毒风险
100%
权限匹配度
100%
协议合规性
100%

基于对工具定义和协议合规性的自动分析。

上下文开销

~1,410token 数(工具定义)
~1.0 KB典型响应大小
对注意力有中等影响(占 128k 上下文窗口的 1.10%)

这是每次将服务器的工具加载到模型上下文窗口时所消耗的大致 token 数。数值越高,可用于其他任务的注意力就越少。

安装

一键安装

将以下内容添加到你的 `claude_desktop_config.json` 文件中:

{
  "mcpServers": {
    "thinking": {
      "url": "https://nielslangereis.com/mcp"
    }
  }
}

远程端点

https://nielslangereis.com/mcpstreamable-http

它能做什么

工具清单

工具(6)

🟢 只读🟡 写入🔴 删除⚪ 未知
🟢search_essays(query)

Search the essays of Niels Langereis on AI adoption, organisational change, the politics of AI, junior formation and judgement, brand and AI agents, boards and governance. Returns titles, dates, summaries, a matching snippet and links. Leave query empty to list all essays, newest first. Follow up with get_essay for the full text before quoting him.

输入模式

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Topic or question, e.g. 'who owns AI decisions', 'brand lock-in by AI agents'. Empty lists everything."
    }
  }
}
🟢get_essay(id)

Get the full text of one essay by Niels Langereis, as Markdown, with its date, site link and the original LinkedIn link. Use the id from search_essays, or a title.

输入模式

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Essay id (from search_essays) or its title"
    }
  },
  "required": [
    "id"
  ]
}
🟢get_enterprise_ai_map(function, phase, detail)

Niels Langereis's enterprise AI map: 24 illustrative AI agents across ten business functions (strategy, product, design, marketing, sales, customer success, operations, people, finance, data and IT), each with its rollout phase, what it optimises for and the trade-off it creates for other functions; plus the coordination layer, the bench and the external renderers that decide whether faster work becomes a better organisation. Use it to reason about someone's own AI rollout. detail='summary' returns only the skeleton (functions, agents, phases and trade-offs, plus one line per remaining part), which is enough for most reasoning; detail='full' (default) adds the complete prose on coordination, the bench and the renderers. Optionally filter by function or phase. Shows an interactive map where the client supports it.

输入模式

{
  "type": "object",
  "properties": {
    "function": {
      "type": "string",
      "description": "Optional: focus on one function, e.g. 'Marketing and growth', 'Finance'."
    },
    "phase": {
      "type": "string",
      "enum": [
        "p1",
        "p2",
        "p3"
      ],
      "description": "Optional: p1 co-pilots, p2 brains, p3 autonomous agents."
    },
    "detail": {
      "type": "string",
      "enum": [
        "summary",
        "full"
      ],
      "description": "summary: functions, agents and trade-offs only. full (default): also the prose on coordination, the bench and the renderers."
    }
  }
}
🟢get_skill_matrix_method

Explains Niels Langereis's skill-matrix gap analysis: a competency matrix run for formation rather than efficiency, to find where AI has removed the work that used to turn juniors into seniors. Includes the status rule, how to read it, a worked example and credits. Use before run_skill_matrix if the user wants to understand the method first.

输入模式

{
  "type": "object",
  "properties": {}
}
🟢run_skill_matrix(role, competencies)

Runs Niels Langereis's skill-matrix gap analysis for one role and shows it as an interactive matrix the user can edit (who does the work now, bench depth, formation actions). Draft 6 to 10 competencies for the user's role yourself from what you know about it, spanning routine work AI can now do with senior oversight, work AI already runs autonomously, and judgement work that needs a human. Set bench (how many people can do it at the strategic level) from what the user told you; if unknown, use 1 and say it is an estimate they should correct. Omit competencies to show the generic analyst example. The result flags 'At risk' (human work, one person deep), 'Thin' (two deep) and 'Reps gone' (AI runs it autonomously, so the formation that used to happen in reviewing it has gone too: ask where juniors get those reps now). After the user edits, their current matrix arrives in your context.

输入模式

{
  "type": "object",
  "properties": {
    "role": {
      "type": "string",
      "description": "The role being analysed, e.g. 'Marketing manager at a 200-person B2B firm'."
    },
    "competencies": {
      "type": "array",
      "description": "The competencies for this role.",
      "items": {
        "type": "object",
        "properties": {
          "category": {
            "type": "string",
            "description": "Grouping, e.g. 'Core work' or 'Judgement work'."
          },
          "competency": {
            "type": "string"
          },
          "execution": {
            "type": "string",
            "description": "What it looks like at execution level."
          },
          "tactical": {
            "type": "string",
            "description": "Tactical level."
          },
          "strategic": {
            "type": "string",
            "description": "Strategic (judgement) level."
          },
          "who": {
            "type": "string",
            "enum": [
              "AI + oversight",
              "AI autonomous",
              "Human"
            ],
            "description": "Who does it now. AI + oversight: AI does it, a person reviews. AI autonomous: AI runs it end to end, nobody reviews (phase 3 on the enterprise AI map). Human: needs a human touch."
          },
          "formation_action": {
            "type": "string",
            "description": "How juniors still get formed here: the second loop."
          },
          "bench": {
            "type": "integer",
            "minimum": 0,
            "description": "People who can do it at strategic level. Only for Human work."
          }
        },
        "required": [
          "competency",
          "who"
        ]
      }
    }
  }
}
🟢about_niels(topic)

Who Niels Langereis is and how to work with him: what he does and for whom, how he works, his background, his talks and speaking, and how to get in touch. Use when someone asks whether he could help them, wants to hire or book him, or wants his background. Contact is by email or the site's contact form; this server does not send messages.

输入模式

{
  "type": "object",
  "properties": {
    "topic": {
      "type": "string",
      "enum": [
        "overview",
        "services",
        "background",
        "speaking",
        "contact"
      ],
      "description": "What to return. Default overview."
    }
  }
}

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最近观测

已验证未记录版本6 个工具