# GraphQL Query Complexity Analyzer MCP for AI Agents MCP > GraphQL Query Complexity Analyzer lets you audit your GraphQL queries for performance bottlenecks before they hit production. It breaks down the structural depth and token count of any query, helping you spot resource-heavy requests that might crash your backend. It's a safety net for developers who need to keep their APIs fast and stable. ## Overview - **Category:** security - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_La4Qg4f4AghAUbDDvit6e9M1582UdhhgOiZWaSBS/mcp - **Tags:** graphql, complexity, analysis, security, query-optimization ## Description GraphQL Query Complexity Analyzer lets you audit your GraphQL queries for performance bottlenecks before they hit production. It breaks down the structural depth and token count of any query, helping you spot resource-heavy requests that might crash your backend. It's a safety net for developers who need to keep their APIs fast and stable. You're building an API and want to make sure a single request doesn't take down your entire database. It's easy for a user to write a query that looks fine on the surface but actually triggers a massive amount of nested data fetching. This MCP gives your AI agent the eyes it needs to see those hidden costs. Instead of waiting for a production incident to tell you a query is too heavy, you can check it during the development phase. It calculates a complexity score by looking at how deep the nesting goes and how many tokens the query actually uses. When you're working through the Vinkius marketplace, this tool stands out because it acts like a gatekeeper for your backend health. It helps you set boundaries for what your clients can request, ensuring that your infrastructure stays responsive even when users try to pull a lot of data at once. You can use it to validate new features, audit existing endpoints, or even set up automated checks in your CI/CD pipeline to catch bad queries early. It takes the guesswork out of query optimization by providing hard numbers on how your requests will behave under load. ## Tools ### analyze_query_complexity Calculates a complexity score and nesting depth for a given GraphQL query to identify performance risks. ## Prompt Examples **Prompt:** ``` Check this query for me: query { user { posts { comments { author { name } } } } } ``` **Response:** ``` ### Query Analysis Results **Complexity Score:** 45.0 **Token Count:** 20 **Max Depth:** 4 **Warning:** This query has a high nesting depth. Consider flattening the structure to improve performance. ``` **Prompt:** ``` Is this query too heavy for a mobile app? "query { products { id, price, category { name } } }" ``` **Response:** ``` ### Query Analysis Results **Complexity Score:** 8.0 **Token Count:** 12 **Max Depth:** 2 **Verdict:** This query is well within safe limits for most mobile clients. ``` **Prompt:** ``` Give me a complexity score for: "query { user { id } }" ``` **Response:** ``` ### Query Analysis Results **Complexity Score:** 15.0 **Token Count:** 6 **Max Depth:** 1 **Verdict:** This is a standard, efficient query with low resource impact. ``` ## Capabilities ### Calculate complexity scores Get a numerical value representing the computational weight of any GraphQL query. ### Measure nesting depth Identify how many levels deep a query goes to prevent recursive data fetching issues. ### Count query tokens See the total number of tokens used in a query to manage data volume. ### Identify heavy queries Spot specific requests that risk exhausting your backend resources. ### Audit GraphQL documents Review and validate the structure of your GraphQL schema and queries. ### Prevent backend crashes Stop resource-heavy requests from reaching your production environment. ## Use Cases ### Auditing a new user profile query A backend dev wants to see if a new nested query for user profiles is too deep. They ask their agent to check the complexity and get a score of 45.0, prompting them to refactor the data fetching. ### Identifying DoS vectors A security lead wants to find queries that could crash the server. They use the analyzer to find queries with a nesting depth of 10 or more and block them. ### Mobile app data optimization A product manager wants to ensure the mobile app isn't over-fetching data. They ask the agent to audit the current query to see if it stays under a certain token count. ### Refactoring legacy endpoints A developer is refactoring an old API and needs to audit every query. They use the analyzer to generate a complexity report for all existing endpoints. ## Benefits - Stop production crashes by using analyze_query_complexity to catch deep nesting before deployment. - Save on cloud costs by identifying high-token queries that drain your resources. - Improve API response times by spotting inefficient data fetching patterns early. - Strengthen your security posture by detecting potential DoS attacks in your GraphQL schema. - Speed up the QA process by providing your team with clear complexity scores for every new request. ## How It Works The bottom line is you get a clear warning before a heavy query ever hits your production environment. 1. Paste a GraphQL query or document into your AI client. 2. The agent calls the analyzer to parse the structure and nesting. 3. You get a breakdown of the depth, token count, and a final complexity score. ## Frequently Asked Questions **What is the GraphQL Query Complexity Analyzer MCP?** It is a tool that checks how heavy a GraphQL query is. It looks at how many layers deep the data is nested and how many tokens it uses to give you a complexity score. **How does this help my API performance?** It helps by catching inefficient queries during development. By knowing the complexity score early, you can optimize the query to be faster and lighter before it hits your production environment. **Can I use this to prevent DoS attacks?** Yes. By identifying queries with excessive depth or token counts, you can set limits on your API to block malicious or poorly written requests that try to crash your system. **Does this tool work for all GraphQL queries?** It works for any standard GraphQL query document. You can pass it any query you're planning to run to see its structural complexity and resource impact. **Is this for frontend or backend developers?** It is for both. Backend devs use it to secure the API, while frontend devs can use it to make sure they aren't requesting more data than they actually need for the UI. **How do I know if a query is 'too heavy'?** The MCP provides a complexity score. While the specific threshold depends on your backend limits, it helps you identify outliers that are significantly more expensive than your standard requests. **What does this tool analyze?** It analyzes the structural complexity of a GraphQL query by calculating token count and nesting depth. **How is the complexity score calculated?** The score is derived from the number of tokens in the query and the maximum nesting depth, clamped between 0 and 100.