7 个 MCP 服务器吃掉 15% 上下文，两字提示耗 2.8 万 token

{
  "name"： "search_project_issues_advanced"，
  "description"： "跨仓库搜索和过滤项目 issue。每当用户询问 bug、任务、冲刺或工单状态时使用此工具。支持分页、按创建或更新日期排序，以及按负责人、标签、里程碑或状态过滤。"，
  "inputSchema"： {
    "type"： "object"，
    "properties": {
      "query": {
        "type": "string",
        "description": "与 issue 标题或正文 markdown 匹配的全文本搜索字符串。"
      },
      "state": {
        "type": "string",
        "enum": ["open", "closed", "all"],
        "description": "按 issue 当前生命周期状态进行筛选。默认为 open。"
      },
      "labels": {
        "type": "array",
        "items": { "type": "string" },
        "description": "用于筛选的精确标签名列表（应用 AND 逻辑）。"
      },
      "sortBy": {
        "type": "string",
        "enum": ["created", "updated", "comments"],
        "description": "用于对返回的 issue 列表排序的字段。"
      }
    },
    "required": ["query"]
  }
}

// audit-tools.ts
export interface ToolDefinition {
  name: string;
  description: string;
  inputSchema: Record<string, unknown>;
}

export interface AuditReport {
  toolCount: number;
  rawBytes: number;
  estimatedTokensPerTurn: number;
  tokensPer10StepLoop: number;
}

/**
 * 估算 JSON schema 的 LLM token 用量（结构化 JSON 约每 token 3.6 个字符）
 * 并计算多步智能体循环中累积的上下文压力。
 */
export function auditToolFootprint(tools: ToolDefinition[]): AuditReport {
  const serialized = JSON.stringify(tools);
  const rawBytes = Buffer.byteLength(serialized, "utf8");
  const estimatedTokensPerTurn = Math.ceil(serialized.length / 3.6);

  return {
    toolCount: tools.length,
    rawBytes,
    estimatedTokensPerTurn,
    tokensPer10StepLoop: estimatedTokensPerTurn * 10,
  };
}

// 模拟一个臃肿的 45 工具 MCP 配置与一个聚焦的 3 工具微型配置
const bloatedSetup: ToolDefinition[] = Array.from({ length: 45 }, (_, i) => ({
  name: `enterprise_integration_action_${i + 1}`,
  description:
    "Comprehensive enterprise integration endpoint supporting filtering, pagination, sorting, metadata expansion, and audit logging across workspaces.",
  inputSchema: {
    type: "object",
    properties: {
      workspaceId: { type: "string", description: "Target workspace UUID" },
      filterQuery: { type: "string", description: "Lucene-compatible filter syntax" },
      includeMetadata: { type: "boolean", description: "Include full audit trail" },
      limit: { type: "number", description: "Max records to return (1-500)" },
      cursor: { type: "string", description: "Pagination cursor from previous call" },
    },
    required: ["workspaceId", "filterQuery"],
  },
}));

const microToolSetup: ToolDefinition[] = [
  {
    name: "run_focused_check",
    description: "Run local project verification and return concise failure lines only.",
    inputSchema: {
      type: "object",
      properties: {
        scope: { type: "string", enum: ["types", "tests", "lint"] },
      },
      required: ["scope"],
    },
  },
];

console.table({
  "Bloated MCP Stack (45 tools)": auditToolFootprint(bloatedSetup),
  “聚焦型微工具（1 个工具）”: auditToolFootprint(microToolSetup),
});

#!/usr/bin/env node
// scripts/pr-summary.ts — 完整 GitHub MCP 服务器的零膨胀替代方案
import { execFileSync } from "node:child_process";

const prNumber = process.argv[2];
if (!prNumber) {
  console.error("Usage: pr-summary <pr-number>");
  process.exit(1);
}

// 只获取 AI 推理实际所需的精确字段
const raw = execFileSync(
  "gh",
  ["pr", "view", prNumber, "--json", "title,state,body,files"],
  { encoding: "utf8" }
);

const data = JSON.parse(raw);
console.log(`PR: ${data.title} [${data.state}]`);
console.log(`Changed Files (${data.files.length}):`); for (const file of data.files) { console.log(` - ${file.path} (+${file.additions}/-${file.deletions})`);
}