## Description
This makes Codex Apps tool reads use a shared in-memory snapshot instead
of rereading the disk cache every time `list_all_tools()` runs. Disk
still seeds the cache on startup and gets updated after successful
fetches, but it is no longer the live read path.
The core change is that `McpManager` now owns a process-scoped
`CodexAppsToolsCache`. Codex threads in the same app-server process now
share this Codex Apps in-memory tools snapshot. The snapshot is keyed by
the Codex home plus the Codex Apps identity: the active Codex auth
user/workspace and the effective Codex Apps MCP source config.
There's already code to hard-refresh the cache, so we respect it in this
PR.
## Local benchmark
I ran a local steady-state microbenchmark of the exact repeated Codex
Apps cached-tools read this PR removes, using the same real local cache
payload in both trees: `3,678,138` bytes and `381` tools. The cache file
was already warm in the OS page cache, so this measures same-process
reread/deserialization work rather than cold-disk latency or full turn
latency. Each run is 25 iterations (mimicking a turn that makes 25
inference calls).
| Version | Run 1 | Run 2 | Avg |
|---|---:|---:|---:|
| `origin/main` disk read + JSON deserialize + `filter_tools` | `50.755
ms` | `52.894 ms` | `51.825 ms` |
| This branch in-memory `current_tools` + `filter_tools` | `0.740 ms` |
`0.778 ms` | `0.759 ms` |
That removes about `51 ms` from each repeated Codex Apps cached-tools
read on this machine, roughly `68x` faster for that subpath. It is
useful evidence for the hot path this PR changes, but not a claim that
every production turn gets `51 ms` faster; end-to-end impact also
depends on the rest of `list_all_tools()` and tool-payload construction.
This is on my M2 Max macbook, so with a slower disk this would be much
worse (and indeed we did see this really blew up turn runtime with a
slow disk).
Owen Lin