mcp sdk memory usage climbs on every tool call, never drops - is there a leak or am i configuring this wrong
running mcp sdk 0.5.0 with 12 tools in production for 6 hours yesterday and memory climbed from 340mb to 1.8gb. RSS grows ~3mb per tool call and never drops even when tools complete succesfully. profiled with memory_profiler and the sdk's tool response cache just accumulates indefinitely - no eviction, no size limit, nothing. anyone else seeing this or is our setup busted? we're on python 3.11.6 with default sdk config, no custom caching.
probbaly a leak yeah. we saw this on 0.4.2 with tools taht return large payloads - memory climbs on every call and never drops even after tool finishes. sdk dosent cleanup response buffers properly
We're seeing similar memory behavior on mcp sdk 0.5.0 with long-running agent workflows - baseline memory climbs from ~340mb at init to ~1.2gb after 200 tool calls, never drops even with gc.collect(). Are you running tool calls sequentially or in parallel batches? Wondering if it's the tool response buffering or something in the registry that's holding references.