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Ocarina – Automate and test MCP servers from YAML, no LLM

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Ocarina – Automate and test MCP servers from YAML, no LLM

Hi all. As someone who has spent years working with Ansible and other automation frameworks, the recent MCP boom has me fascinated. People are creating nice, typed, LLM-readable (and thus human-readable) interfaces for their servers, over a standardized protocol that exposes both tools and resources. I wanted to see if I could create a way to run scripts against these servers directly, with no AI in the loop. Ocarina lets you inspect MCP server characteristics and write Rondos (the equivalent of Ansible playbooks) that express tool calls in yaml so you can play them step-by-step without needing to drive with an LLM. Here's what a Rondo looks like: keys: owner: acme repo: api server: command: npx args: [-y, "@modelcontextprotocol/server-github"] rondo: - name: recent commits tool: list_commits args: owner: "{{owner}}" repo: "{{repo}}" grab: ".0.sha" echo: latest_sha - name: commit detail tool: get_commit args: owner: "{{owner}}" repo: "{{repo}}" sha: "{{latest_sha}}" expect: contains: "feat" I have started using Ocarina myself to test and validate the MCP servers I am creating at my job, and I am interested in the broader field of how we can maximize the use of the massive MCP ecosystem. The README includes links to repos you can clone and try right away (and a little Blender demo ;]) Happy Saturday!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, mcp, context · Missing: mac, agents, macos
75%75% 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: started, maximize · Missing: supports, reddit linkedin, podcasting
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface, calls · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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