Let a decision model pick your agent's MCP tools, not the LLM. An MCP gateway routed by Jev: 649 tools, 1/32 the cost of LLM routing.
View on GitHub ↗Let a decision model pick your agent's MCP tools, not the LLM.
An MCP gateway that sits between your AI agent and your MCP servers. For each request, Jev (TypeSafe AI's "System One" decision model) answers one yes/no question per server and per tool, and the LLM only ever sees the handful of tools Jev said yes to.
With MCP, every tool from every connected server is put into the LLM's prompt on every turn. Connect a few real servers (GitHub, Grafana, PagerDuty, Jira, Kubernetes...) and that is hundreds of tools and ~200k tokens before the user's request is even read. The LLM then spends expensive reasoning deciding which 2 or 3 of them to call.
Choosing tools is a decision, not a generation task. Jev doesn't generate text: it takes a state plus typed questions and returns an answer per question, for about $0.04 per million input tokens.
649 real tools from 25 MCP servers, 111 hand-labeled requests (details):
All needed tools picked Cost to choose tools Time to choose tools ------------ Jev (this repo) 95% $0.0003 ~1 s idle, 2.0 s under load Claude Sonnet 5, full catalog in prompt (cached) 99% $0.0101 3.2 s Claude Haiku 4.5, same 93% $0.0031 1.8 s GPT-5 nano, same 86% $0.0003 3.2 s Embedding search, top 20 85% ~$0 = 0.3, tuned so a needed server is rarely dropped), then the tools of the kept servers. If a server is clearly needed but none of its tools clears 0.5, its top-ranked tool is taken anyway. - Skills / playbooks (skills//SKILL.md) are routed the same way. The chosen playbook's text goes straight into the response, so the LLM doesn't spend a turn loading it. - Jev
From the project README.
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