feat(Message API): MCP tool streaming support - #414
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📝 WalkthroughWalkthroughThe PR introduces streaming MCP tool-loop support for Anthropic Messages API with a new interception layer that detects tool_use blocks, executes MCP tool calls, and emits synthetic tool events within the SSE stream. Additionally, three handler functions are exposed at crate visibility, and router logic is updated to conditionally route streaming requests through the tool-loop when MCP servers are present. Changes
Sequence DiagramsequenceDiagram
participant Client
participant Router as AnthropicRouter
participant ToolLoop as execute_streaming_tool_loop
participant SSEConsumer as SSE Channel
participant UpstreamAPI as Anthropic API
participant MCPServers as MCP Tool Servers
Client->>Router: POST messages (streaming=true, mcp_servers)
Router->>ToolLoop: execute_streaming_tool_loop(router, req_ctx, mcp_servers)
ToolLoop->>SSEConsumer: Create SSE channel for client response
ToolLoop->>UpstreamAPI: Spawn background task, send upstream request
UpstreamAPI-->>ToolLoop: SSE stream (content_block_start, delta, stop)
ToolLoop->>ToolLoop: parse_sse_frame, detect tool_use blocks
ToolLoop->>ToolLoop: Transform content_block_start to mcp_tool_use, accumulate state
ToolLoop->>SSEConsumer: Forward/emit mcp_tool_use events
ToolLoop->>ToolLoop: Detect StopReason::ToolUse
ToolLoop->>MCPServers: execute_mcp_tool_calls(tool_blocks)
MCPServers-->>ToolLoop: Tool execution results
ToolLoop->>SSEConsumer: emit_mcp_tool_result_events
ToolLoop->>UpstreamAPI: Continue SSE stream consumption
UpstreamAPI-->>ToolLoop: Final message_delta, message_stop
ToolLoop->>SSEConsumer: emit_final_events (usage, stop_reason)
SSEConsumer-->>Client: Complete SSE stream to client
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes Possibly related PRs
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🚥 Pre-merge checks | ✅ 4✅ Passed checks (4 passed)
✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing touches🧪 Generate unit tests (beta)
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Summary of ChangesHello @key4ng, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request significantly enhances the Anthropic Messages API by integrating streaming support for MCP tools. The change enables the gateway to dynamically intercept and process tool calls within an ongoing Server-Sent Event (SSE) stream. It executes these tools and injects their results back into the stream, providing a more interactive and integrated experience for clients utilizing streaming and multi-cloud platform tools. This allows for a continuous, real-time interaction where tool usage and results are part of the same coherent stream. Highlights
Changelog
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Code Review
This pull request introduces streaming support for MCP tool use in the Anthropic Messages API, adding a new module streaming.rs to intercept, process, and generate SSE streams. Critical security concerns regarding Denial of Service (DoS) vulnerabilities due to unbounded resource accumulation from upstream workers have been identified. These cross-cutting security issues, requiring design decisions, should be addressed in dedicated security pull requests. Additionally, there are opportunities to improve code quality by removing unused code, refactoring complex functions for maintainability, and enhancing robustness through better JSON parsing error handling.
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Actionable comments posted: 5
🤖 Fix all issues with AI agents
In `@model_gateway/src/routers/anthropic/messages/streaming.rs`:
- Around line 692-697: emit_final_events currently ignores the Result returned
by send_sse when sending "message_delta" and "message_stop"; modify
emit_final_events to await and check the Result from send_sse for both calls
(the send_sse invocations that send "message_delta" and "message_stop") and, on
Err (e.g., channel closed), log a warning or error via the existing
logger/tracing (include context like "failed to send final SSE event" and which
event type). Ensure you do not change control flow beyond logging (since stream
is ending), but surface the failure for observability.
- Around line 210-211: The current map_err call discards the original error from
handler::select_worker, losing context; change the closure to capture the
original error (e.g., |e| ...) and include its Display/Debug output in the
returned error message so the error preserves details about why selection
failed; update the call to handler::select_worker(&router.worker_registry,
model_id) to propagate or wrap the original error (referencing select_worker,
worker_registry, and model_id) instead of replacing it with a generic string.
- Around line 106-107: The token accumulators total_input_tokens and
total_output_tokens can overflow as u32 (given DEFAULT_MAX_ITERATIONS and large
per-iteration token counts); change their types to u64 and update every
accumulation site in streaming.rs to use u64 arithmetic (or use saturating_add
if you prefer saturating behavior) so additions are safe — ensure any casts
to/from token counts elsewhere are adjusted (e.g., when calling functions or
returning values) to match the new u64 type.
- Around line 523-524: The code silently converts malformed JSON by using
serde_json::from_str(input_json).unwrap_or(...); change this to explicitly
handle the Result from serde_json::from_str(input_json) (e.g., match or if let
Err(e) => ...) so you log a warning that includes the parsing error and the
offending input_json before falling back to Value::Object(Map::new()); update
the use site where input: Value is created to perform that match and emit the
warning (use the existing logger in scope or log::warn!) so malformed tool input
is visible in logs.
- Around line 57-62: The spawned fire-and-forget task can panic and close the
stream without emitting an error; wrap the call to run_tool_loop in a
panic-capture wrapper (use std::panic::catch_unwind with
std::panic::AssertUnwindSafe around the async body) inside the tokio::spawn so
panics are caught, log the panic and call send_sse_error(&tx, &panic_info).await
(or convert the panic payload to a string) instead of letting the task abort;
keep the existing parameters (tx, router, req_ctx, mcp_servers) and ensure both
normal Err(e) and caught panics produce a logged message and an SSE error to the
client.
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Hi @key4ng, the branch Please use one of the following formats:
Allowed types:
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Hi @key4ng, the branch Please use one of the following formats:
Allowed types:
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1 participant
Description
Problem
The Anthropic router supports non-streaming MCP tool loops but passes streaming requests through without MCP support — streaming.rs was empty. When stream: true and MCP tools are present, there was no mechanism to intercept the upstream worker's SSE stream, detect tool_use blocks, execute MCP tools, and emit mcp_tool_use/mcp_tool_result events within a single coherent SSE stream.
Solution
Implement a streaming MCP tool loop that intercepts upstream SSE events, transforms tool_use → mcp_tool_use, executes MCP tools betweeniterations, emits synthetic mcp_tool_result events, and continues the loop — all within one SSE stream to the client. Uses a tokio::mpsc channel with a spawned task to drive the tool loop while returning an Axum streaming response immediately.
Changes
Key behaviors
Test Plan
stream result
event: message_start data: {"type":"message_start","message":{"model":"claude-3-haiku-20240307","id":"msg_01T5ZFUVHU3BhyDpwg6tv59v","type":"message","role":"assistant","content":[],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":691,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"}}}event: content_block_start
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event: content_block_stop
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event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" trading day."}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":"\n- Oracle stock has"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" trade"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":"d in"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" the"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" range of $142"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":"-"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":"$157"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" over"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" the past week."}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":"\n- Oracle"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" has"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" a market capitalization"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" of around $505"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" "}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":"billion.\n-"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" Oracle stock"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" has a"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" P"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":"/E ratio of"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" 30.05"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" "}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":"and a"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" dividend yield of "}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":"1.3%."}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":"\n\nOverall"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":", both Google an"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":"d Oracle appear"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" to be trading at"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" relatively strong"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" levels"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" compare"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":"d to their recent"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" historical prices"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":". Google in"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" particular is seeing"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" bullish sentiment"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" from analysts"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" base"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":"d on its growth"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" prospects, while Oracle"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" remains"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" a steady tech"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" blue"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" chip."}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" The"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" summary"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" highlights the key stock"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" price points an"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":"d financial"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" metrics for"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" each"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":" company"}}
event: content_block_delta
data: {"type":"content_block_delta","index":5,"delta":{"type":"text_delta","text":"."}}
event: content_block_stop
data: {"type":"content_block_stop","index":5}
event: message_delta
data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null},"usage":{"input_tokens":5396,"output_tokens":408}}
event: message_stop
data: {"type":"message_stop"}
Checklist
cargo +nightly fmtpassescargo clippy --all-targets --all-features -- -D warningspassesSummary by CodeRabbit
Release Notes