MCPSpec – Ship reliable MCP servers without writing test code

Hacker News

Hi HN, I've been building MCPSpec, an open-source CLI for MCP server reliability. Record sessions, generate mock servers, catch Tool Poisoning, and fail your CI build when something's wrong — without writing test code. There are ways to validate MCP servers today — the MCP Inspector, ad-hoc SDK scripts, unit tests for server internals — but nothing that handles regression detection, security auditing, mock generation, and CI pass/fail checks in one tool. MCPSpec does that: 1. Record a session against your real server, replay it after changes to catch regressions 2. Generate a standalone .js mock from any recording — no API keys, no live server needed in CI 3. Security audit with 8 rules including Tool Poisoning (prompt injection hidden in tool descriptions) 4. 0-100 quality score across documentation, schema quality, error handling, responsiveness, and security 5. One command to generate GitHub Actions / GitLab CI configs No LLMs in the loop. Deterministic and fast. Ships with 70 ready-to-run tests for 7 popular MCP servers. GitHub: https://github.com/light-handle/mcpspec Docs: https://light-handle.github.io/mcpspec/ Would love feedback or feature ideas.

Visit product View on Hacker NewsFeb 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, code, open · Missing: mac, agents, macos

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · Missing: https docs, excited, just released

28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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