I think that's true but do you see MCP as enough of a discovery primitive on its own, or does it still lack a ranking/trust layer? My intuition is that capability exposure is only half the problem and the harder part is how agents evaluate and choose between multiple similar tools.
Take Supabase for example. It’s disproportionately recommended by LLMs when people ask for backend/database stacks. It can't be just because of it's capability since a lot of tools expose similar primitives. Something in the model’s training data, ecosystem visibility, or reinforcement layer is shaping that ranking.
If agents start choosing tools autonomously, the real leverage point isn’t just “can you describe your capabilities in MCP?” but “how does the agent decide you’re preferred over 5 near identical alternatives?”
Do you think that ranking layer sits inside the model providers, or if it becomes an external reputation network?
nworley|5 days ago
Take Supabase for example. It’s disproportionately recommended by LLMs when people ask for backend/database stacks. It can't be just because of it's capability since a lot of tools expose similar primitives. Something in the model’s training data, ecosystem visibility, or reinforcement layer is shaping that ranking.
If agents start choosing tools autonomously, the real leverage point isn’t just “can you describe your capabilities in MCP?” but “how does the agent decide you’re preferred over 5 near identical alternatives?”
Do you think that ranking layer sits inside the model providers, or if it becomes an external reputation network?