fixes#19486
### Problem
Right now dynamic deferred tools are filtered at normal-turn prompt
building time, rather than upstream while building the `ToolRouter`
itself. This causes issues because dynamic deferred tools are then
wrongly included in the router's `model_visible_specs`, which is what
the compaction request-building flow relies on.
### Fix
Move the dynamic deferred tool filtering to `ToolRouter` creation time
to solve this problem for every request that relies on `ToolRouter` for
`model_visible_specs`, which solves the issue generically.
### Tests
Added unit + integration tests to ensure dynamic deferred tools are
omitted from `model_visible_specs` and compaction request respectively.
Tested against live `/compact` endpoint; raw deferred dynamic tools
without `tool_search` returned `400` (current bug), while the filtered
payload (this fix) returns `200`.
## Summary
Extends rollout tracing across tool dispatch and code-mode runtime
boundaries. This records canonical tool-call lifecycle events and links
code-mode execution/wait operations back to the model-visible calls that
caused them.
## Stack
This is PR 3/5 in the rollout trace stack.
- [#18876](https://github.com/openai/codex/pull/18876): Add rollout
trace crate
- [#18877](https://github.com/openai/codex/pull/18877): Record core
session rollout traces
- [#18878](https://github.com/openai/codex/pull/18878): Trace tool and
code-mode boundaries
- [#18879](https://github.com/openai/codex/pull/18879): Trace sessions
and multi-agent edges
- [#18880](https://github.com/openai/codex/pull/18880): Add debug trace
reduction command
## Review Notes
This PR is about attribution. Reviewers should focus on whether direct
tool calls, code-mode-originated tool calls, waits, outputs, and
cancellation boundaries are recorded with enough source information for
deterministic reduction without coupling the reducer to live runtime
internals.
The stack remains valid after this layer: tool and code-mode traces
reduce through the existing crate model, while the broader session and
multi-agent relationships are added in the next PR.
## Summary
When auto-review is enabled, it should handle request_permissions tool.
We'll need to clean up the UX but I'm planning to do that in a separate
pass
## Testing
- [x] Ran locally
<img width="893" height="396" alt="Screenshot 2026-04-17 at 1 16 13 PM"
src="https://github.com/user-attachments/assets/4c045c5f-1138-4c6c-ac6e-2cb6be4514d8"
/>
---------
Co-authored-by: Codex <noreply@openai.com>
Adds new events for streaming apply_patch changes from responses api.
This is to enable clients to show progress during file writes.
Caveat: This does not work with apply_patch in function call mode, since
that required adding streaming json parsing.
stacked on #17402.
MCP tools returned by `tool_search` (deferred tools) get registered in
our `ToolRegistry` with a different format than directly available
tools. this leads to two different ways of accessing MCP tools from our
tool catalog, only one of which works for each. fix this by registering
all MCP tools with the namespace format, since this info is already
available.
also, direct MCP tools are registered to responsesapi without a
namespace, while deferred MCP tools have a namespace. this means we can
receive MCP `FunctionCall`s in both formats from namespaces. fix this by
always registering MCP tools with namespace, regardless of deferral
status.
make code mode track `ToolName` provenance of tools so it can map the
literal JS function name string to the correct `ToolName` for
invocation, rather than supporting both in core.
this lets us unify to a single canonical `ToolName` representation for
each MCP tool and force everywhere to use that one, without supporting
fallbacks.
## Why
For more advanced MCP usage, we want the model to be able to emit
parallel MCP tool calls and have Codex execute eligible ones
concurrently, instead of forcing all MCP calls through the serial block.
The main design choice was where to thread the config. I made this
server-level because parallel safety depends on the MCP server
implementation. Codex reads the flag from `mcp_servers`, threads the
opted-in server names into `ToolRouter`, and checks the parsed
`ToolPayload::Mcp { server, .. }` at execution time. That avoids relying
on model-visible tool names, which can be incomplete in
deferred/search-tool paths or ambiguous for similarly named
servers/tools.
## What was added
Added `supports_parallel_tool_calls` for MCP servers.
Before:
```toml
[mcp_servers.docs]
command = "docs-server"
```
After:
```toml
[mcp_servers.docs]
command = "docs-server"
supports_parallel_tool_calls = true
```
MCP calls remain serial by default. Only tools from opted-in servers are
eligible to run in parallel. Docs also now warn to enable this only when
the server’s tools are safe to run concurrently, especially around
shared state or read/write races.
## Testing
Tested with a local stdio MCP server exposing real delay tools. The
model/Responses side was mocked only to deterministically emit two MCP
calls in the same turn.
Each test called `query_with_delay` and `query_with_delay_2` with `{
"seconds": 25 }`.
| Build/config | Observed | Wall time |
| --- | --- | --- |
| main with flag enabled | serial | `58.79s` |
| PR with flag enabled | parallel | `31.73s` |
| PR without flag | serial | `56.70s` |
PR with flag enabled showed both tools start before either completed;
main and PR-without-flag completed the first delay before starting the
second.
Also added an integration test.
Additional checks:
- `cargo test -p codex-tools` passed
- `cargo test -p codex-core
mcp_parallel_support_uses_exact_payload_server` passed
- `git diff --check` passed
- [x] Expand tool search to custom MCPs.
- [x] Rename several variables/fields to be more generic.
Updated tool & server name lifecycles:
**Raw Identity**
ToolInfo.server_name is raw MCP server name.
ToolInfo.tool.name is raw MCP tool name.
MCP calls route back to raw via parse_tool_name() returning
(tool.server_name, tool.tool.name).
mcpServerStatus/list now groups by raw server and keys tools by
Tool.name: mod.rs:599
App-server just forwards that grouped raw snapshot:
codex_message_processor.rs:5245
**Callable Names**
On list-tools, we create provisional callable_namespace / callable_name:
mcp_connection_manager.rs:1556
For non-app MCP, provisional callable name starts as raw tool name.
For codex-apps, provisional callable name is sanitized and strips
connector name/id prefix; namespace includes connector name.
Then qualify_tools() sanitizes callable namespace + name to ASCII alnum
/ _ only: mcp_tool_names.rs:128
Note: this is stricter than Responses API. Hyphen is currently replaced
with _ for code-mode compatibility.
**Collision Handling**
We do initially collapse example-server and example_server to the same
base.
Then qualify_tools() detects distinct raw namespace identities behind
the same sanitized namespace and appends a hash to the callable
namespace: mcp_tool_names.rs:137
Same idea for tool-name collisions: hash suffix goes on callable tool
name.
Final list_all_tools() map key is callable_namespace + callable_name:
mcp_connection_manager.rs:769
**Direct Model Tools**
Direct MCP tool declarations use the full qualified sanitized key as the
Responses function name.
The raw rmcp Tool is converted but renamed for model exposure.
**Tool Search / Deferred**
Tool search result namespace = final ToolInfo.callable_namespace:
tool_search.rs:85
Tool search result nested name = final ToolInfo.callable_name:
tool_search.rs:86
Deferred tool handler is registered as "{namespace}:{name}":
tool_registry_plan.rs:248
When a function call comes back, core recombines namespace + name, looks
up the full qualified key, and gets the raw server/tool for MCP
execution: codex.rs:4353
**Separate Legacy Snapshot**
collect_mcp_snapshot_from_manager_with_detail() still returns a map
keyed by qualified callable name.
mcpServerStatus/list no longer uses that; it uses
McpServerStatusSnapshot, which is raw-inventory shaped.
## Summary
- reduce public module visibility across Rust crates, preferring private
or crate-private modules with explicit crate-root public exports
- update external call sites and tests to use the intended public crate
APIs instead of reaching through module trees
- add the module visibility guideline to AGENTS.md
## Validation
- `cargo check --workspace --all-targets --message-format=short` passed
before the final fix/format pass
- `just fix` completed successfully
- `just fmt` completed successfully
- `git diff --check` passed
## Why
`codex-core` was re-exporting APIs owned by sibling `codex-*` crates,
which made downstream crates depend on `codex-core` as a proxy module
instead of the actual owner crate.
Removing those forwards makes crate boundaries explicit and lets leaf
crates drop unnecessary `codex-core` dependencies. In this PR, this
reduces the dependency on `codex-core` to `codex-login` in the following
files:
```
codex-rs/backend-client/Cargo.toml
codex-rs/mcp-server/tests/common/Cargo.toml
```
## What
- Remove `codex-rs/core/src/lib.rs` re-exports for symbols owned by
`codex-login`, `codex-mcp`, `codex-rollout`, `codex-analytics`,
`codex-protocol`, `codex-shell-command`, `codex-sandboxing`,
`codex-tools`, and `codex-utils-path`.
- Delete the `default_client` forwarding shim in `codex-rs/core`.
- Update in-crate and downstream callsites to import directly from the
owning `codex-*` crate.
- Add direct Cargo dependencies where callsites now target the owner
crate, and remove `codex-core` from `codex-rs/backend-client`.
## Why
`codex-rs/tools/src/lib.rs` already defines the [canonical `codex_tools`
export
surface](https://github.com/openai/codex/blob/bf081b9e286e620d25c1d61cf0f38f74a0f982ce/codex-rs/tools/src/lib.rs#L83-L88)
for `ToolsConfig`, `ToolsConfigParams`, and the shell backend config
types. Re-exporting those same types from `core/src/tools/spec.rs` gives
`codex-core` two import paths for one API and blurs which crate owns
those config definitions.
This PR removes that duplicate path so `codex-core` callsites depend on
`codex_tools` directly.
## What
- Remove the five `codex_tools` re-exports from
`core/src/tools/spec.rs`.
- Update `codex-core` production and test callsites to import
`ShellCommandBackendConfig`, `ToolsConfig`, `ToolsConfigParams`,
`UnifiedExecShellMode`, and `ZshForkConfig` from `codex_tools`.
## Verification
- Ran `cargo test -p codex-core`.
- The package run is currently red in five unrelated config tests in
`core/src/config/config_tests.rs` (`approvals_reviewer_*` and
`smart_approvals_alias_*`), while the tool/spec and shell tests touched
by this import cleanup passed.
## Why
`codex-rs/core/src/client_common.rs` still had a `tools` re-export
module that forwarded `codex_tools` types back into `codex-core`. After
the earlier extraction work in #16379, #16471, #16477, and #16481, that
extra layer no longer adds value.
Removing it keeps dependencies explicit: the `codex-core` modules that
actually use `ToolSpec` and related types now depend on `codex_tools`
directly instead of reaching through `client_common`.
## What Changed
- removed the `client_common::tools` re-export module from
`core/src/client_common.rs`
- updated the remaining `codex-core` consumers to import `codex_tools`
directly
- adjusted the affected test code to reference
`codex_tools::ResponsesApiTool` directly as well
This is a mechanical cleanup only. It does not change tool behavior or
runtime logic.
## Testing
- `cargo test -p codex-core client_common::tests`
- `cargo test -p codex-core tools::router::tests`
- `cargo test -p codex-core tools::context::tests`
- `cargo test -p codex-core tools::spec::tests`
## Why
`#16193` moved the pure `tool_search` and `tool_suggest` spec builders
into `codex-tools`, but `codex-core` still owned the shared
discoverable-tool model that those builders and the `tool_suggest`
runtime both depend on. This change continues the migration by moving
that reusable model boundary out of `codex-core` as well, so the
discovery/suggestion stack uses one shared set of types and
`core/src/tools` no longer needs its own `discoverable.rs` module.
## What changed
- Moved `DiscoverableTool`, `DiscoverablePluginInfo`, and
`filter_tool_suggest_discoverable_tools_for_client()` into
`codex-rs/tools/src/tool_discovery.rs` alongside the extracted
discovery/suggestion spec builders.
- Added `codex-app-server-protocol` as a `codex-tools` dependency so the
shared discoverable-tool model can own the connector-side `AppInfo`
variant directly.
- Updated `core/src/tools/handlers/tool_suggest.rs`,
`core/src/tools/spec.rs`, `core/src/tools/router.rs`,
`core/src/connectors.rs`, and `core/src/codex.rs` to consume the shared
`codex-tools` model instead of the old core-local declarations.
- Changed `core/src/plugins/discoverable.rs` to return
`DiscoverablePluginInfo` directly, moved the pure client-filter coverage
into `tool_discovery_tests.rs`, and deleted the old
`core/src/tools/discoverable.rs` module.
- Updated `codex-rs/tools/README.md` so the crate boundary documents
that `codex-tools` now owns the discoverable-tool models in addition to
the discovery/suggestion spec builders.
## Test plan
- `cargo test -p codex-tools`
- `CARGO_TARGET_DIR=/tmp/codex-core-discoverable-model cargo test -p
codex-core --lib tools::handlers::tool_suggest::`
- `CARGO_TARGET_DIR=/tmp/codex-core-discoverable-model cargo test -p
codex-core --lib tools::spec::`
- `CARGO_TARGET_DIR=/tmp/codex-core-discoverable-model cargo test -p
codex-core --lib plugins::discoverable::`
- `just bazel-lock-check`
- `just argument-comment-lint`
## References
- #16193
- #16154
- #15923
- #15928
- #15944
- #15953
- #16031
- #16047
- #16129
- #16132
- #16138
- #16141
## Why
This continues the `codex-tools` migration by moving another passive
tool-spec layer out of `codex-core`.
After `ToolSpec` moved into `codex-tools`, `codex-core` still owned
`ConfiguredToolSpec` and `create_tools_json_for_responses_api()`. Both
are data-model and serialization helpers rather than runtime
orchestration, so keeping them in `core/src/tools/registry.rs` and
`core/src/tools/spec.rs` left passive tool-definition code coupled to
`codex-core` longer than necessary.
## What changed
- moved `ConfiguredToolSpec` into `codex-rs/tools/src/tool_spec.rs`
- moved `create_tools_json_for_responses_api()` into
`codex-rs/tools/src/tool_spec.rs`
- re-exported the new surface from `codex-rs/tools/src/lib.rs`, which
remains exports-only
- updated `core/src/client.rs`, `core/src/tools/registry.rs`, and
`core/src/tools/router.rs` to consume the extracted types and serializer
from `codex-tools`
- moved the tool-list serialization test into
`codex-rs/tools/src/tool_spec_tests.rs`
- added focused unit coverage for `ConfiguredToolSpec::name()`
- simplified `core/src/tools/spec_tests.rs` to use the extracted
`ConfiguredToolSpec::name()` directly and removed the now-redundant
local `tool_name()` helper
- updated `codex-rs/tools/README.md` so the crate boundary reflects the
newly extracted tool-spec wrapper and serialization helper
## Test plan
- `cargo test -p codex-tools`
- `CARGO_TARGET_DIR=/tmp/codex-core-configured-spec cargo test -p
codex-core --lib tools::spec::`
- `CARGO_TARGET_DIR=/tmp/codex-core-configured-spec cargo test -p
codex-core --lib client::`
- `just fix -p codex-tools -p codex-core`
- `just argument-comment-lint`
## References
- #15923
- #15928
- #15944
- #15953
- #16031
- #16047
Moves Code Mode to a new crate with no dependencies on codex. This
create encodes the code mode semantics that we want for lifetime,
mounting, tool calling.
The model-facing surface is mostly unchanged. `exec` still runs raw
JavaScript, `wait` still resumes or terminates a `cell_id`, nested tools
are still available through `tools.*`, and helpers like `text`, `image`,
`store`, `load`, `notify`, `yield_control`, and `exit` still exist.
The major change is underneath that surface:
- Old code mode was an external Node runtime.
- New code mode is an in-process V8 runtime embedded directly in Rust.
- Old code mode managed cells inside a long-lived Node runner process.
- New code mode manages cells in Rust, with one V8 runtime thread per
active `exec`.
- Old code mode used JSON protocol messages over child stdin/stdout plus
Node worker-thread messages.
- New code mode uses Rust channels and direct V8 callbacks/events.
This PR also fixes the two migration regressions that fell out of that
substrate change:
- `wait { terminate: true }` now waits for the V8 runtime to actually
stop before reporting termination.
- synchronous top-level `exit()` now succeeds again instead of surfacing
as a script error.
---
- `core/src/tools/code_mode/*` is now mostly an adapter layer for the
public `exec` / `wait` tools.
- `code-mode/src/service.rs` owns cell sessions and async control flow
in Rust.
- `code-mode/src/runtime/*.rs` owns the embedded V8 isolate and
JavaScript execution.
- each `exec` spawns a dedicated runtime thread plus a Rust
session-control task.
- helper globals are installed directly into the V8 context instead of
being injected through a source prelude.
- helper modules like `tools.js` and `@openai/code_mode` are synthesized
through V8 module resolution callbacks in Rust.
---
Also added a benchmark for showing the speed of init and use of a code
mode env:
```
$ cargo bench -p codex-code-mode --bench exec_overhead -- --samples 30 --warm-iterations 25 --tool-counts 0,32,128
Finished [`bench` profile [optimized]](https://doc.rust-lang.org/cargo/reference/profiles.html#default-profiles) target(s) in 0.18s
Running benches/exec_overhead.rs (target/release/deps/exec_overhead-008c440d800545ae)
exec_overhead: samples=30, warm_iterations=25, tool_counts=[0, 32, 128]
scenario tools samples warmups iters mean/exec p95/exec rssΔ p50 rssΔ max
cold_exec 0 30 0 1 1.13ms 1.20ms 8.05MiB 8.06MiB
warm_exec 0 30 1 25 473.43us 512.49us 912.00KiB 1.33MiB
cold_exec 32 30 0 1 1.03ms 1.15ms 8.08MiB 8.11MiB
warm_exec 32 30 1 25 509.73us 545.76us 960.00KiB 1.30MiB
cold_exec 128 30 0 1 1.14ms 1.19ms 8.30MiB 8.34MiB
warm_exec 128 30 1 25 575.08us 591.03us 736.00KiB 864.00KiB
memory uses a fresh-process max RSS delta for each scenario
```
---------
Co-authored-by: Codex <noreply@openai.com>
Summary
- add the code_mode_only feature flag/config schema and wire its
dependency on code_mode
- update code mode tool descriptions to list nested tools with detailed
headers
- restrict available tools for prompt and exec descriptions when
code_mode_only is enabled and test the behavior
Testing
- Not run (not requested)
Summary
- make all code-mode tools accessible as globals so callers only need
`tools.<name>`
- rename text/image helpers and key globals (store, load, ALL_TOOLS,
etc.) to reflect the new shared namespace
- update the JS bridge, runners, descriptions, router, and tests to
follow the new API
Testing
- Not run (not requested)
## Why
PR #13783 moved the `codex.rs` unit tests into `codex_tests.rs`. This
applies the same extraction pattern across the rest of `codex-rs/core`
so the production modules stay focused on runtime code instead of large
inline test blocks.
Keeping the tests in sibling files also makes follow-up edits easier to
review because product changes no longer have to share a file with
hundreds or thousands of lines of test scaffolding.
## What changed
- replaced each inline `mod tests { ... }` in `codex-rs/core/src/**`
with a path-based module declaration
- moved each extracted unit test module into a sibling `*_tests.rs`
file, using `mod_tests.rs` for `mod.rs` modules
- preserved the existing `cfg(...)` guards and module-local structure so
the refactor remains structural rather than behavioral
## Testing
- `cargo test -p codex-core --lib` (`1653 passed; 0 failed; 5 ignored`)
- `just fix -p codex-core`
- `cargo fmt --check`
- `cargo shear`
## Why
to support a new bring your own search tool in Responses
API(https://developers.openai.com/api/docs/guides/tools-tool-search#client-executed-tool-search)
we migrating our bm25 search tool to use official way to execute search
on client and communicate additional tools to the model.
## What
- replace the legacy `search_tool_bm25` flow with client-executed
`tool_search`
- add protocol, SSE, history, and normalization support for
`tool_search_call` and `tool_search_output`
- return namespaced Codex Apps search results and wire namespaced
follow-up tool calls back into MCP dispatch
Summary
- document output types for the various tool handlers and registry so
the API exposes richer descriptions
- update unified execution helpers and client tests to align with the
new output metadata
- clean up unused helpers across tool dispatch paths
Testing
- Not run (not requested)
## Summary
This changes `custom_tool_call_output` to use the same output payload
shape as `function_call_output`, so freeform tools can return either
plain text or structured content items.
The main goal is to let `js_repl` return image content from nested
`view_image` calls in its own `custom_tool_call_output`, instead of
relying on a separate injected message.
## What changed
- Changed `custom_tool_call_output.output` from `string` to
`FunctionCallOutputPayload`
- Updated freeform tool plumbing to preserve structured output bodies
- Updated `js_repl` to aggregate nested tool content items and attach
them to the outer `js_repl` result
- Removed the old `js_repl` special case that injected `view_image`
results as a separate pending user image message
- Updated normalization/history/truncation paths to handle multimodal
`custom_tool_call_output`
- Regenerated app-server protocol schema artifacts
## Behavior
Direct `view_image` calls still return a `function_call_output` with
image content.
When `view_image` is called inside `js_repl`, the outer `js_repl`
`custom_tool_call_output` now carries:
- an `input_text` item if the JS produced text output
- one or more `input_image` items from nested tool results
So the nested image result now stays inside the `js_repl` tool output
instead of being injected as a separate message.
## Compatibility
This is intended to be backward-compatible for resumed conversations.
Older histories that stored `custom_tool_call_output.output` as a plain
string still deserialize correctly, and older histories that used the
previous injected-image-message flow also continue to resume.
Added regression coverage for resuming a pre-change rollout containing:
- string-valued `custom_tool_call_output`
- legacy injected image message history
#### [git stack](https://github.com/magus/git-stack-cli)
- 👉 `1` https://github.com/openai/codex/pull/12948
## Summary
- Fix `js_repl` so `await codex.tool("view_image", { path })` actually
attaches the image to the active turn when called from inside the JS
REPL.
- Restore the behavior expected by the existing `js_repl`
image-attachment test.
- This is a follow-up to
[#12553](https://github.com/openai/codex/pull/12553), which changed
`view_image` to return structured image content.
## Root Cause
- [#12553](https://github.com/openai/codex/pull/12553) changed
`view_image` from directly injecting a pending user image message to
returning structured `function_call_output` content items.
- The nested tool-call bridge inside `js_repl` serialized that tool
response back to the JS runtime, but it did not mirror returned image
content into the active turn.
- As a result, `view_image` appeared to succeed inside `js_repl`, but no
`input_image` was actually attached for the outer turn.
## What Changed
- Updated the nested tool-call path in `js_repl` to inspect function
tool responses for structured content items.
- When a nested tool response includes `input_image` content, `js_repl`
now injects a corresponding user `Message` into the active turn before
returning the raw tool result back to the JS runtime.
- Kept the normal JSON result flow intact, so `codex.tool(...)` still
returns the original tool output object to JavaScript.
## Why
- `js_repl` documentation and tests already assume that `view_image` can
be used from inside the REPL to attach generated images to the model.
- Without this fix, the nested call path silently dropped that
attachment behavior.
## Summary
Introduces the initial implementation of Feature::RequestPermissions.
RequestPermissions allows the model to request that a command be run
inside the sandbox, with additional permissions, like writing to a
specific folder. Eventually this will include other rules as well, and
the ability to persist these permissions, but this PR is already quite
large - let's get the core flow working and go from there!
<img width="1279" height="541" alt="Screenshot 2026-02-15 at 2 26 22 PM"
src="https://github.com/user-attachments/assets/0ee3ec0f-02ec-4509-91a2-809ac80be368"
/>
## Testing
- [x] Added tests
- [x] Tested locally
- [x] Feature
## Summary
- Limit `search_tool_bm25` indexing to `codex_apps` tools only, so
non-Apps MCP servers are no longer discoverable through this search
path.
- Move search-tool discovery guidance into the `search_tool_bm25` tool
description (via template include) instead of injecting it as a separate
developer message.
- Update Apps discovery guidance wording to clarify when to use
`search_tool_bm25` for Apps-backed systems (for example Slack, Google
Drive, Jira, Notion) and when to call tools directly.
- Remove dead `core` helper code (`filter_codex_apps_mcp_tools` and
`codex_apps_connector_id`) that is no longer used after the
tool-selection refactor.
- Update `core` search-tool tests to assert codex-apps-only behavior and
to validate guidance from the tool description.
## Validation
- ✅ `just fmt`
- ✅ `cargo test -p codex-core search_tool`
- ⚠️ `cargo test -p codex-core` was attempted, but the run repeatedly
stalled on
`tools::js_repl::tests::js_repl_can_attach_image_via_view_image_tool`.
## Tickets
- None
Took over the work that @aaronl-openai started here:
https://github.com/openai/codex/pull/10397
Now that app-server clients are able to set up custom tools (called
`dynamic_tools` in app-server), we should expose a way for clients to
pass in not just text, but also image outputs. This is something the
Responses API already supports for function call outputs, where you can
pass in either a string or an array of content outputs (text, image,
file):
https://platform.openai.com/docs/api-reference/responses/create#responses_create-input-input_item_list-item-function_tool_call_output-output-array-input_image
So let's just plumb it through in Codex (with the caveat that we only
support text and image for now). This is implemented end-to-end across
app-server v2 protocol types and core tool handling.
## Breaking API change
NOTE: This introduces a breaking change with dynamic tools, but I think
it's ok since this concept was only recently introduced
(https://github.com/openai/codex/pull/9539) and it's better to get the
API contract correct. I don't think there are any real consumers of this
yet (not even the Codex App).
Old shape:
`{ "output": "dynamic-ok", "success": true }`
New shape:
```
{
"contentItems": [
{ "type": "inputText", "text": "dynamic-ok" },
{ "type": "inputImage", "imageUrl": "data:image/png;base64,AAA" }
]
"success": true
}
```
We started working with MCP in Codex before
https://crates.io/crates/rmcp was mature, so we had our own crate for
MCP types that was generated from the MCP schema:
https://github.com/openai/codex/blob/8b95d3e082376f4cb23e92641705a22afb28a9da/codex-rs/mcp-types/README.md
Now that `rmcp` is more mature, it makes more sense to use their MCP
types in Rust, as they handle details (like the `_meta` field) that our
custom version ignored. Though one advantage that our custom types had
is that our generated types implemented `JsonSchema` and `ts_rs::TS`,
whereas the types in `rmcp` do not. As such, part of the work of this PR
is leveraging the adapters between `rmcp` types and the serializable
types that are API for us (app server and MCP) introduced in #10356.
Note this PR results in a number of changes to
`codex-rs/app-server-protocol/schema`, which merit special attention
during review. We must ensure that these changes are still
backwards-compatible, which is possible because we have:
```diff
- export type CallToolResult = { content: Array<ContentBlock>, isError?: boolean, structuredContent?: JsonValue, };
+ export type CallToolResult = { content: Array<JsonValue>, structuredContent?: JsonValue, isError?: boolean, _meta?: JsonValue, };
```
so `ContentBlock` has been replaced with the more general `JsonValue`.
Note that `ContentBlock` was defined as:
```typescript
export type ContentBlock = TextContent | ImageContent | AudioContent | ResourceLink | EmbeddedResource;
```
so the deletion of those individual variants should not be a cause of
great concern.
Similarly, we have the following change in
`codex-rs/app-server-protocol/schema/typescript/Tool.ts`:
```
- export type Tool = { annotations?: ToolAnnotations, description?: string, inputSchema: ToolInputSchema, name: string, outputSchema?: ToolOutputSchema, title?: string, };
+ export type Tool = { name: string, title?: string, description?: string, inputSchema: JsonValue, outputSchema?: JsonValue, annotations?: JsonValue, icons?: Array<JsonValue>, _meta?: JsonValue, };
```
so:
- `annotations?: ToolAnnotations` ➡️ `JsonValue`
- `inputSchema: ToolInputSchema` ➡️ `JsonValue`
- `outputSchema?: ToolOutputSchema` ➡️ `JsonValue`
and two new fields: `icons?: Array<JsonValue>, _meta?: JsonValue`
---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed
with [ReviewStack](https://reviewstack.dev/openai/codex/pull/10349).
* #10357
* __->__ #10349
* #10356
## Summary
Add dynamic tool injection to thread startup in API v2, wire dynamic
tool calls through the app server to clients, and plumb responses back
into the model tool pipeline.
### Flow (high level)
- Thread start injects `dynamic_tools` into the model tool list for that
thread (validation is done here).
- When the model emits a tool call for one of those names, core raises a
`DynamicToolCallRequest` event.
- The app server forwards it to the client as `item/tool/call`, waits
for the client’s response, then submits a `DynamicToolResponse` back to
core.
- Core turns that into a `function_call_output` in the next model
request so the model can continue.
### What changed
- Added dynamic tool specs to v2 thread start params and protocol types;
introduced `item/tool/call` (request/response) for dynamic tool
execution.
- Core now registers dynamic tool specs at request time and routes those
calls via a new dynamic tool handler.
- App server validates tool names/schemas, forwards dynamic tool call
requests to clients, and publishes tool outputs back into the session.
- Integration tests
helpful in the future if we want more granularity for requesting
escalated permissions:
e.g when running in readonly sandbox, model can request to escalate to a
sandbox that allows writes
It's pretty amazing we have gotten here without the ability for the
model to see image content from MCP tool calls.
This PR builds off of 4391 and fixes#4819. I would like @KKcorps to get
adequete credit here but I also want to get this fix in ASAP so I gave
him a week to update it and haven't gotten a response so I'm going to
take it across the finish line.
This test highlights how absured the current situation is. I asked the
model to read this image using the Chrome MCP
<img width="2378" height="674" alt="image"
src="https://github.com/user-attachments/assets/9ef52608-72a2-4423-9f5e-7ae36b2b56e0"
/>
After this change, it correctly outputs:
> Captured the page: image dhows a dark terminal-style UI labeled
`OpenAI Codex (v0.0.0)` with prompt `model: gpt-5-codex medium` and
working directory `/codex/codex-rs`
(and more)
Before this change, it said:
> Took the full-page screenshot you asked for. It shows a long,
horizontally repeating pattern of stylized people in orange, light-blue,
and mustard clothing, holding hands in alternating poses against a white
background. No text or other graphics-just rows of flat illustration
stretching off to the right.
Without this change, the Figma, Playwright, Chrome, and other visual MCP
servers are pretty much entirely useless.
I tested this change with the openai respones api as well as a third
party completions api
Today `sub_id` is an ID of a single incoming Codex Op submition. We then
associate all events triggered by this operation using the same
`sub_id`.
At the same time we are also creating a TurnContext per submission and
we'd like to start associating some events (item added/item completed)
with an entire turn instead of just the operation that started it.
Using turn context when sending events give us flexibility to change
notification scheme.
# Tool System Refactor
- Centralizes tool definitions and execution in `core/src/tools/*`:
specs (`spec.rs`), handlers (`handlers/*`), router (`router.rs`),
registry/dispatch (`registry.rs`), and shared context (`context.rs`).
One registry now builds the model-visible tool list and binds handlers.
- Router converts model responses to tool calls; Registry dispatches
with consistent telemetry via `codex-rs/otel` and unified error
handling. Function, Local Shell, MCP, and experimental `unified_exec`
all flow through this path; legacy shell aliases still work.
- Rationale: reduce per‑tool boilerplate, keep spec/handler in sync, and
make adding tools predictable and testable.
Example: `read_file`
- Spec: `core/src/tools/spec.rs` (see `create_read_file_tool`,
registered by `build_specs`).
- Handler: `core/src/tools/handlers/read_file.rs` (absolute `file_path`,
1‑indexed `offset`, `limit`, `L#: ` prefixes, safe truncation).
- E2E test: `core/tests/suite/read_file.rs` validates the tool returns
the requested lines.
## Next steps:
- Decompose `handle_container_exec_with_params`
- Add parallel tool calls
sayan-oaiandGitHub
Curtis 'Fjord' HawthorneandGitHub
cassirer-openaiandGitHub
pakrym-oaiandGitHub
Akshay NathanandGitHub
Matthew ZengandGitHub
josiah-openaiandGitHub
Vivian FangandGitHub
Michael BolinandGitHub
Anton PanasenkoandGitHub
daveaitel-openaiandGitHub
Owen LinandGitHub
jif-oaiandGitHub
zhao-oaiandGitHub
Gabriel PealandGitHub
