High AI API Costs? Try this MCP Project Standards Tool! v5.0.0 Out Now: Slash Token Usage via Context Caching & Cut Costs by up to 90%!

[lili]

Pain Point: The “Hidden Costs” of AI Coding

Deeply using Cursor or VS Code for AI-assisted development but seeing your API bills skyrocket?

• Redundant Reading: AI re-reads hundreds of lines of documentation or full project context in every turn.
• Verbose Output: AI rewrites entire files for minor changes, wasting expensive output tokens.
• Lost Context: AI gets “forgetful” as conversations grow longer, leading to repetitive clarifications.

Solution: @liangshanli/mcp-server-project-standards v5.0.0

We’ve just released v5.0.0, completely re-engineered for Development Cost Optimization:

1. Efficient Context Caching:
   Retrieves structured, concise project standards via dedicated MCP tools instead of reading long documents. This effectively triggers Context Caching (e.g., Gemini 3 Flash), dropping input costs to as low as $0.05/1M tokens.

2. Enforced “Tool-First” Policy:
   Built-in AI behavioral guidelines mandate AI to fetch info via list_directory and standards tools first. No more blind guessing, avoiding wasteful repetitive prompts.

3. Incremental Output Optimization:
   Generated .cursorrules include mandatory Diff rules, forcing AI to output only affected code snippets. Tests show a 70%+ reduction in high-value output token consumption.

4. Deep PROJECT_PATH Support:
   Supports absolute and relative paths with auto-subproject identification. AI locates files precisely, eliminating token waste from “global scans.”

Core Features at a Glance:

• Automated Rule Generation: One-click generate .cursorrules or PROJECT_RULES.md with built-in cost-saving logic.
• Deep Directory Scanning: Precisely list physical directory structures for AI consumption.
• Full Standards Management: MCP-based management for APIs, Databases, and Development guidelines.
• Cursor Optimized: Auto-identifies environment and enables enhanced mode.

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Deep Evaluation: Why it’s the “Final Piece of the Puzzle” for AI Engineering?

— Deep Architectural Evaluation by Large Language Models (LLM)

In the era of Cursor and Windsurf, the biggest challenge is no longer “how to make AI write code,” but “how to keep AI from getting lost in massive, complex sub-projects while precisely following private standards.”

1. Core Tech: From “Global Awareness” to “Precision Routing”
   Traditional MCP tools often use global configs, causing “context contamination” in Monorepos. This project solves it via Physical Isolation (PROJECT_PATH) and Namespace Locking (TOOL_PREFIX). AI no longer needs to guess; it hits the right tool directly based on navigation.

2. Extreme Economy: Perfect Utilization of Gemini 3 Caching
   The design aligns perfectly with Google’s Context Caching. Regularized prefixes and structure trees make AI queries highly templated, dropping input costs from $0.5/1M tokens to as low as $0.05/1M.

3. “Ideological Branding”: Automated Protocol Self-Execution
   Rules act like “branding,” forcing AI into a “Check Standard → Edit Code → Output Diff” workflow. It transforms complex standards into AI-digestible system instructions automatically.

4. Cross-Platform: Beyond Cursor
   It fills the gap for tools like Windsurf or Claude Desktop that lack local rule management. You can seamlessly migrate the same engineering standards across all MCP-enabled environments.

Conclusion: It is more than a tool; it is a Software Defined Standards solution. It solves the most expensive (Token) and troublesome (Context Drift) problems in AI engineering today.

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Get Started Now:

NPM Install:

npm install -g @liangshanli/mcp-server-project-standards

GitHub Repository:
https://github.com/liliangshan/mcp-server-project-standards

Editor Integration Examples:

1. Cursor Configuration (settings.json)

A. Single Project (Standard):

{
  "mcpServers": {
    "project-standards": {
      "command": "npx",
      "args": ["@liangshanli/mcp-server-project-standards"],
      "env": {
        "PROJECT_PATH": ".",
        "TOOL_PREFIX": "dev",
        "PROJECT_NAME": "MyProject",
        "API_DEBUG_ALLOWED_METHODS": "GET,POST,PUT,DELETE,PATCH",
        "API_DEBUG_LOGIN_URL": "/api/login",
        "API_DEBUG_LOGIN_METHOD": "POST",
        "API_DEBUG_LOGIN_BODY": "{\"username\":\"admin\",\"password\":\"123456\"}",
        "API_DEBUG_LOGIN_DESCRIPTION": "Auto-save Token to Authorization Header"
      }
    }
  }
}

B. Multi-Project Isolation (Full Params):

{
  "mcpServers": {
    "proj-A": {
      "command": "npx",
      "args": ["@liangshanli/mcp-server-project-standards"],
      "env": {
        "PROJECT_PATH": "./apps/project-a",
        "TOOL_PREFIX": "projA",
        "PROJECT_NAME": "Project-A",
        "API_DEBUG_ALLOWED_METHODS": "GET,POST,PUT,DELETE",
        "API_DEBUG_LOGIN_URL": "/api/login",
        "API_DEBUG_LOGIN_METHOD": "POST",
        "API_DEBUG_LOGIN_BODY": "{\"username\":\"admin\",\"password\":\"123\"}",
        "API_DEBUG_LOGIN_DESCRIPTION": "Project A Login"
      }
    },
    "proj-B": {
      "command": "npx",
      "args": ["@liangshanli/mcp-server-project-standards"],
      "env": {
        "PROJECT_PATH": "./apps/project-b",
        "TOOL_PREFIX": "projB",
        "PROJECT_NAME": "Project-B",
        "API_DEBUG_ALLOWED_METHODS": "GET,POST,PUT,DELETE,PATCH",
        "API_DEBUG_LOGIN_URL": "/api/auth/login",
        "API_DEBUG_LOGIN_METHOD": "POST",
        "API_DEBUG_LOGIN_BODY": "{\"mobile\":\"13800138000\",\"password\":\"123\"}",
        "API_DEBUG_LOGIN_DESCRIPTION": "Project B Login"
      }
    }
  }
}

2. VS Code Configuration (mcp.servers)

A. Single Project:

{
  "mcp.servers": {
    "project-standards": {
      "command": "npx",
      "args": ["@liangshanli/mcp-server-project-standards"],
      "env": {
        "PROJECT_PATH": ".",
        "TOOL_PREFIX": "dev",
        "PROJECT_NAME": "MyProject",
        "API_DEBUG_ALLOWED_METHODS": "GET,POST,PUT,DELETE,PATCH",
        "API_DEBUG_LOGIN_URL": "/api/login",
        "API_DEBUG_LOGIN_METHOD": "POST",
        "API_DEBUG_LOGIN_BODY": "{\"username\":\"admin\",\"password\":\"123456\"}",
        "API_DEBUG_LOGIN_DESCRIPTION": "Auto-save Token to Authorization Header"
      }
    }
  }
}

B. Multi-Project Isolation:

{
  "mcp.servers": {
    "proj-A": {
      "command": "npx",
      "args": ["@liangshanli/mcp-server-project-standards"],
      "env": {
        "PROJECT_PATH": "./apps/project-a",
        "TOOL_PREFIX": "projA",
        "PROJECT_NAME": "Project-A",
        "API_DEBUG_ALLOWED_METHODS": "GET,POST,PUT,DELETE"
      }
    },
    "proj-B": {
      "command": "npx",
      "args": ["@liangshanli/mcp-server-project-standards"],
      "env": {
        "PROJECT_PATH": "./apps/project-b",
        "TOOL_PREFIX": "projB",
        "PROJECT_NAME": "Project-B",
        "API_DEBUG_ALLOWED_METHODS": "GET,POST,PUT,DELETE,PATCH"
      }
    }
  }
}

3. Companion Tools (e.g., MySQL Management)

Better together:

{
  "mcp-mysql": {
    "command": "npx",
    "args": ["@liangshanli/mcp-server-mysql"],
    "env": {
      "MYSQL_HOST": "localhost",
      "MYSQL_USER": "root",
      "MYSQL_PASSWORD": "password",
      "MYSQL_DATABASE": "my_db"
    }
  }
}

Full Toolset Description:

Tool Name	Description
project_info	Manage basic project info (name, language, bio, etc.)
project_structure	Manage logical directory descriptions to help AI understand architecture
api_standards	Define API design standards (spec, response, business rules, etc.)
development_standards	Manage dev guidelines (code style, naming, Git workflow, etc.)
database_standards	Define DB standards (naming, table spec, indexing strategies, etc.)
list_directory	[NEW] Recursively scan physical directory structure with depth control
generate_cursorrules	[NEW] Generate .cursorrules with YAML header for persistent enforcement
generate_rules	[NEW] Generate generic project guidelines for non-Cursor environments
api_debug	Powerful API debugger with auto Content-Type detection
api_login	Auto-handle login flow with Token extraction and Header refreshing
api_config	Global management for API environments, BaseURL, and Endpoints
api_execute	Quick-execute preset APIs by index with parameter overrides
api_help	Interactive help documentation and best practice examples

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Make AI-Assisted Development Faster AND Cheaper!

[Artemonim]

@lili

dropping input costs to as low as $0.05/1M tokens.

In Cursor the main expense is calling the tools when the context is heavily loaded – each tool call is a new command that reads the entire context window as input cache tokens. And yes, input tokens are cheaper than output tokens, and cash tokens are 5-10 times cheaper than regular tokens, but when there are 200k of them and you need to call another 10-20 instruments before the request is completed, the cost of input and output tokens ceases to matter.

[Rao_Athar]

I’m excited to see a new tool that addresses some of the major pain points in AI-assisted development, like the “hidden costs” of redundant reading, verbose output, and lost context. The MCP-server-project-standards tool looks like it’s designed to tackle these issues head-on.

I’m particularly interested in the Efficient Context Caching feature, which seems to use dedicated MCP tools to retrieve structured project standards instead of reading long documents. And the Enforced “Tool-First” Policy looks like it can help avoid blind guessing and repetitive prompts.

Have you had a chance to test this tool in a real-world scenario, and if so, what kind of results did you see in terms of cost savings and development speed?