`ToolsConfig::new()` taking a large number of boolean params was hard to
manage and it finally bit us (see
https://github.com/openai/codex/pull/2660). This changes
`ToolsConfig::new()` so that it takes a struct (and also reduces the
visibility of some members, where possible).
Fixes https://github.com/openai/codex/issues/2610
This PR sorts the tools in `get_openai_tools` by name to ensure a
consistent MCP tool order.
Currently, MCP servers are stored in a HashMap, which does not guarantee
ordering. As a result, the tool order changes across turns, effectively
breaking prompt caching in multi-turn sessions.
An alternative solution would be to replace the HashMap with an ordered
structure, but that would require a much larger code change. Given that
it is unrealistic to have so many MCP tools that sorting would cause
performance issues, this lightweight fix is chosen instead.
By ensuring deterministic tool order, this change should significantly
improve cache hit rates and prevent users from hitting usage limits too
quickly. (For reference, my own sessions last week reached the limit
unusually fast, with cache hit rates falling below 1%.)
## Result
After this fix, sessions with MCP servers now show caching behavior
almost identical to sessions without MCP servers.
Without MCP | With MCP
:-------------------------:|:-------------------------:
<img width="1368" height="1634" alt="image"
src="https://github.com/user-attachments/assets/26edab45-7be8-4d6a-b471-558016615fc8"
/> | <img width="1356" height="1632" alt="image"
src="https://github.com/user-attachments/assets/5f3634e0-3888-420b-9aaf-deefd9397b40"
/>
Bumps [whoami](https://github.com/ardaku/whoami) from 1.6.0 to 1.6.1.
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Historically, Codex CLI has treated `apply_patch` (and its sometimes
misspelling, `applypatch`) as a "virtual CLI," intercepting it when it
appears as the first arg to `command` for the `"container.exec",
`"shell"`, or `"local_shell"` tools.
This approach has a known limitation where if, say, the model created a
Python script that runs `apply_patch` and then tried to run the Python
script, we have no insight as to what the model is trying to do and the
Python Script would fail because `apply_patch` was never really on the
`PATH`.
One way to solve this problem is to require users to install an
`apply_patch` executable alongside the `codex` executable (or at least
put it someplace where Codex can discover it). Though to keep Codex CLI
as a standalone executable, we exploit "the arg0 trick" where we create
a temporary directory with an entry named `apply_patch` and prepend that
directory to the `PATH` for the duration of the invocation of Codex.
- On UNIX, `apply_patch` is a symlink to `codex`, which now changes its
behavior to behave like `apply_patch` if arg0 is `apply_patch` (or
`applypatch`)
- On Windows, `apply_patch.bat` is a batch script that runs `codex
--codex-run-as-apply-patch %*`, as Codex also changes its behavior if
the first argument is `--codex-run-as-apply-patch`.
## Summary
We're seeing some issues in the freeform tool - let's disable by default
until it stabilizes.
## Testing
- [x] Ran locally, confirmed codex-cli could make edits
this dramatically improves time to run `cargo test -p codex-core` (~25x
speedup).
before:
```
cargo test -p codex-core 35.96s user 68.63s system 19% cpu 8:49.80 total
```
after:
```
cargo test -p codex-core 5.51s user 8.16s system 63% cpu 21.407 total
```
both tests measured "hot", i.e. on a 2nd run with no filesystem changes,
to exclude compile times.
approach inspired by [Delete Cargo Integration
Tests](https://matklad.github.io/2021/02/27/delete-cargo-integration-tests.html),
we move all test cases in tests/ into a single suite in order to have a
single binary, as there is significant overhead for each test binary
executed, and because test execution is only parallelized with a single
binary.
Adds web_search tool, enabling the model to use Responses API web_search
tool.
- Disabled by default, enabled by --search flag
- When --search is passed, exposes web_search_request function tool to
the model, which triggers user approval. When approved, the model can
use the web_search tool for the remainder of the turn
<img width="1033" height="294" alt="image"
src="https://github.com/user-attachments/assets/62ac6563-b946-465c-ba5d-9325af28b28f"
/>
---------
Co-authored-by: easong-openai <easong@openai.com>
We want to send an aggregated output of stderr and stdout so we don't
have to aggregate it stderr+stdout as we lose order sometimes.
---------
Co-authored-by: Gabriel Peal <gpeal@users.noreply.github.com>
This can be the underlying logic in order to start a conversation from a
previous message. will need some love in the UI.
Base for building this: #2588
## Summary
- read the shell exec approval request's actual id instead of assuming
it is always 0
- use that id when validating and responding in the test
## Testing
- `cargo test -p codex-mcp-server
test_shell_command_approval_triggers_elicitation`
------
https://chatgpt.com/codex/tasks/task_i_68a6ab9c732c832c81522cbf11812be0
Prior to this change, when we got a `CallToolResult` from an MCP server,
we JSON-serialized its `content` field as the `content` to send back to
the model as part of the function call output that we send back to the
model. This meant that we were dropping the `structuredContent` on the
floor.
Though reading
https://modelcontextprotocol.io/specification/2025-06-18/schema#tool, it
appears that if `outputSchema` is specified, then `structuredContent`
should be set, which seems to be a "higher-fidelity" response to the
function call. This PR updates our handling of `CallToolResult` to
prefer using the JSON-serialization of `structuredContent`, if present,
using `content` as a fallback.
Also, it appears that the sense of `success` was inverted prior to this
PR!
Michael Bolin