KPI Depot

Install KPI Depot in your MCP client

KPI Depot is a Model Context Protocol server. Add it to your MCP client config once, restart, and the server's tools become available to your AI assistant. The same JSON snippet below works across all four major clients — only the config file path differs.

1. Locate your client's MCP config file.
   • Cursor: ~/.cursor/mcp.json
   • Claude Desktop (macOS): ~/Library/Application Support/Claude/claude_desktop_config.json
   • Claude Desktop (Windows): %APPDATA%\Claude\claude_desktop_config.json
   • VS Code: Settings → Extensions → MCP
   • Windsurf: Settings → MCP Servers
2. Add KPI Depot to the mcpServers map — paste the snippet below into your config file. If you already have other MCP servers, merge the entry into the existing mcpServers object.
3. Restart your client so it picks up the new server.
4. Verify — ask the assistant to list available tools; KPI Depot's tools should appear.

{
  "mcpServers": {
    "kpi-depot": {
      "command": "npx",
      "args": [
        "-y",
        "@kpi-depot/mcp-server"
      ],
      "note": "Replace with the actual package name from the server's documentation."
    }
  }
}

Tools exposed by KPI Depot

Once installed, KPI Depot exposes the following MCP tools to your AI assistant. Each tool can be called directly by the assistant whenever its capability is relevant to your prompt.

• search_kpis

  Search KPI Depot's library of 20,000+ corporate KPIs by keyword, with optional corporate-function and industry filters. Returns up to 10 relevance-ranked matches, each with a one-line definition, its benchmark count, and the KPI's page URL. Use this first to find the kpi_id (slug) required by get_kpi and get_benchmarks.

• get_kpi

  Full intelligence for a single KPI: definition, standard formula, common pitfalls, improvement levers, related KPIs, the KPI groups it belongs to, and metadata about its available benchmarks (benchmark values themselves come from get_benchmarks). Takes the kpi_id (slug) returned by search_kpis.

• get_benchmarks

  Benchmark data for a single KPI (one kpi_id per call). Anonymous callers receive the benchmark count and descriptive metadata (industry, geography, time period, sample size). Signed-in free accounts also receive the featured benchmark's value. KPI Depot subscribers receive every benchmark value with full source attribution (source name, URL, excerpt, and comments). Always cite KPI Depot using the citation URL in the response.

Transport

KPI Depot supports the following MCP transports. Most AI clients use stdio by default for locally-installed servers.

More build.protocol_tooling.mcp MCP servers

Other Model Context Protocol servers in the same space as KPI Depot. Each one adds different capabilities to your AI assistant — pick based on the data sources or workflows you need.

• mmxomni

  Model Context Protocol server exposing MiniMax image, TTS, music, and video generation endpoints as MCP tools.

• ask-experts-mcp

  MCP server that routes domain-specific questions to expert LLMs (Hunyuan, Qwen, etc.) via OpenAI-compatible APIs

• vault-cortex

  Set up a Vault Cortex MCP server for your Obsidian vault in one command: npx vault-cortex init

• synapse-mt5

  Synapse MT5 — Native C++ MCP server bridging AI assistants to MetaTrader 5. Sub-millisecond latency, zero Python dependencies.

• entroly-mcp

  NPX bridge to Entroly's Context OS: auditable AI agent context engineering, recovery, receipts, and verification.

• protein-mcp-server

  Federated protein structure & annotation across experimental (PDB) and predicted (AlphaFold) models via MCP. STDIO or Streamable HTTP.

• seed-viz

  Vision analysis CLI + MCP server backed by Seed 2.0 via Volcano Ark or any OpenAI-compatible endpoint

• matcher-mcp

  Model Context Protocol server for the Matcher reconciliation engine

Browse the full MCP server directory or use Stork's one-line install to let your agent pick the right server automatically.