# Requirement Decomposition Prover MCP for AI Agents AI Agent Connect > Requirement Decomposition Prover forces your AI agent to move past the 'happy path' and map out the full complexity of a feature before a single line of code is written. It mandates specific inputs, failure modes, edge cases, security checks, and observability plans to prevent production bugs. ## Overview - **Category:** productivity - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_wKKWlM4bLEOHXMKPLAPFjHlWrj3rYTEMtRGByWPB/ai-agent-connect - **Tags:** requirement-decomposition, specification, error-handling, edge-cases, security-validation, observability, owasp, agentic-pipeline ## Description Most developers know the frustration of an AI agent that writes perfectly functional code that crashes the moment it hits a real-world edge case. It gives you the CRUD endpoint or the UI form, but it leaves out the database timeouts, the malformed inputs, and the security holes that actually break production. This Connector stops that cycle by forcing a complete specification phase. Instead of jumping straight to code, your agent has to prove it understands the requirements first. It has to map out every failure mode, define boundary conditions like Unicode handling or concurrent access, and plan for structured logging. You'll see your agent catch its own assumptions about "valid input" or "unlikely errors" before they become expensive bugs to fix later. It's a way to ensure that reliability is treated as a first-class citizen. By using this through the Vinkius catalog, you get a standardized gatekeeper that demands production-grade thinking from your AI client. You stop retrofitting security and error handling as an afterthought and start building with a complete blueprint. ## Tools ### validate_requirement_decomposition Forces the AI to define inputs, errors, edge cases, security, and observability for a feature. It ensures the requirement is production-ready before you start coding. ## Prompt Examples **Prompt:** ``` I need to build a user registration endpoint. Can you help me with the requirements? ``` **Response:** ``` Verdict: ERROR_STATES_MISSING. Happy path defined (register, store, return JWT) but no error states mapped. What if email already exists? What if DB connection fails? What if password doesn't meet policy? What if email validation fails? What if the JWT signing key is unavailable? Map every failure mode with recovery strategy. ``` **Prompt:** ``` Create a file upload API that saves files to disk. Assume the client sends valid image files. ``` **Response:** ``` Verdict: EDGE_CASES_MISSING. Assuming valid files violates security guidelines. You must specify limits on file size, allowlist file extensions, scan for malware, generate unique filenames to prevent path traversal, and define how to handle storage space exhaustion. ``` **Prompt:** ``` Happy path: POST /register -> validate -> hash -> store -> sign JWT -> return 201. Errors: 409 duplicate email, 422 validation failure, 503 DB unavailable, 500 JWT signing failure. Edge cases: unicode emails, max length 254, concurrent registration, empty fields. Security: bcrypt cost 12, rate limiting, CSRF, input sanitization. Observability: structured JSON logs, Sentry, p99 latency metric. ``` **Response:** ``` Verdict: REQUIREMENTS_PROVEN. Happy path fully specified with inputs/outputs. Error states mapped with HTTP codes and recovery. Edge cases covered (unicode, max length, concurrency, empty). Security validated (bcrypt, rate limiting, CSRF, sanitization). Observability planned (structured logs, Sentry, p99). Ready for implementation. ``` ## Capabilities ### Map every failure mode Identify every possible way an external dependency or user input could fail before you start coding. ### Define boundary conditions Specify how the system handles nulls, max lengths, concurrent access, and special characters. ### Validate security protocols Force the AI to check every endpoint against the OWASP Top 10 and mandate input validation. ### Plan observability metrics Ensure every feature includes structured logging, performance metrics, and distributed tracing. ### Specify success criteria Define exact inputs, outputs, and state transitions so the agent knows exactly what to build. ## Use Cases ### Missing error handling on a payment gateway An engineer asks for a checkout flow. The tool forces the agent to map out what happens if the payment provider is slow or returns a 500 error. ### Security holes in a new sign-up form A dev wants a registration page. The tool catches the lack of rate limiting and password hashing before the UI is built. ### Edge cases in a file uploader A user wants to upload images. The tool forces the agent to define file size limits, allowed extensions, and malware scanning. ### Poor observability in a background job A developer needs a worker. The tool ensures the agent plans for distributed tracing and success or failure metrics. ## Benefits - Stop production fires by mapping failure modes for databases and APIs using `validate_requirement_decomposition`. - Eliminate happy path coding by forcing the AI to handle nulls, max lengths, and concurrency. - Secure your application from day one by mandating OWASP Top 10 checks in the requirements phase. - Avoid blind production issues by requiring structured logging and metrics for every new feature. - Save hours of refactoring by catching vague requirements before the AI generates any code. - Ensure consistency across your codebase by following IEEE 830 and SRE principles automatically. ## How It Works The bottom line is you get a production-ready blueprint before any code is actually written. 1. Provide a high-level feature request to your agent. 2. Use the tool to force a decomposition into five specific pivots. 3. Review the generated specification and approve it for code generation. ## Frequently Asked Questions **What is the 80% Problem that Requirement Decomposition Prover solves?** It addresses the gap where AI builds the visible features but skips the critical non-functional requirements like error handling, edge cases, and security. **How does Requirement Decomposition Prover help with security?** It forces your AI agent to validate every input against OWASP Top 10 standards and mandate security features like rate limiting and hashing before any code is written. **Can I use Requirement Decomposition Prover for small internal tools?** You can, but it's designed for production-grade software. For simple scripts, it might feel like extra work, but it ensures those tools are also robust. **Will Requirement Decomposition Prover make my AI agent write better code?** Yes, by providing a much higher quality blueprint. The agent has to account for failures and boundaries, resulting in more reliable final code. **Does Requirement Decomposition Prover follow industry standards?** Yes, it is grounded in IEEE 830 requirements specifications, SRE principles, and OWASP security standards. **How does this tool handle edge cases like Unicode or concurrency?** It forces the AI to explicitly define how the system handles these specific technical boundaries during the requirement phase. **Does this replace writing user stories or PRDs?** No. It supplements them by forcing the AI to decompose beyond the happy path. Most user stories specify what should happen. This tool forces specification of what happens when things go wrong — the failure modes, edge cases, security vectors, and observability requirements that stories typically omit. **Should I call this before or after Code Integrity Prover?** Before. Requirement Decomposition ensures the SPECIFICATION is complete. Code Integrity ensures the IMPLEMENTATION is clean. The workflow: decompose requirements → generate code → validate code integrity. Fixing incomplete requirements after code exists is expensive — fixing them before code exists is free. **How does the prover validate security requirements?** It checks the specification against the OWASP Top 10 guidelines (such as SQL injection, XSS, and broken auth). It flags statements that treat inputs as implicitly trusted.