anthropic: fix error handling and update docs

- Add proper error handling for JSON marshal in StreamConverter to
  prevent corrupted streams when tool arguments cannot be serialized
- Add tests for unmarshalable arguments and mixed validity scenarios
- Fix documentation typo and update recommended models to qwen3-coder

anthropic/anthropic.go

@@ -1,4 +1,3 @@
-// Package anthropic provides core transformation logic for compatibility with the Anthropic Messages API
 package anthropic
 
 import (
@@ -7,6 +6,7 @@ import (
 	"encoding/json"
 	"errors"
 	"fmt"
+	"log/slog"
 	"net/http"
 	"strings"
 	"time"
@@ -673,6 +673,13 @@ func (c *StreamConverter) Process(r api.ChatResponse) []StreamEvent {
 			c.textStarted = false
 		}
 
+		// Marshal arguments first to check for errors before starting block
+		argsJSON, err := json.Marshal(tc.Function.Arguments)
+		if err != nil {
+			slog.Error("failed to marshal tool arguments", "error", err, "tool_id", tc.ID)
+			continue
+		}
+
 		// Start tool use block
 		events = append(events, StreamEvent{
 			Event: "content_block_start",
@@ -689,7 +696,6 @@ func (c *StreamConverter) Process(r api.ChatResponse) []StreamEvent {
 		})
 
 		// Send input as JSON delta
-		argsJSON, _ := json.Marshal(tc.Function.Arguments)
 		events = append(events, StreamEvent{
 			Event: "content_block_delta",
 			Data: ContentBlockDeltaEvent{

anthropic/anthropic_test.go

@@ -665,3 +665,113 @@ func TestStreamConverter_WithToolCalls(t *testing.T) {
 		t.Error("expected input_json_delta event")
 	}
 }
+
+func TestStreamConverter_ToolCallWithUnmarshalableArgs(t *testing.T) {
+	// Test that unmarshalable arguments (like channels) are handled gracefully
+	// and don't cause a panic or corrupt stream
+	conv := NewStreamConverter("msg_123", "test-model")
+
+	// Create a channel which cannot be JSON marshaled
+	unmarshalable := make(chan int)
+
+	resp := api.ChatResponse{
+		Model: "test-model",
+		Message: api.Message{
+			Role: "assistant",
+			ToolCalls: []api.ToolCall{
+				{
+					ID: "call_bad",
+					Function: api.ToolCallFunction{
+						Name:      "bad_function",
+						Arguments: map[string]any{"channel": unmarshalable},
+					},
+				},
+			},
+		},
+		Done:       true,
+		DoneReason: "stop",
+	}
+
+	// Should not panic and should skip the unmarshalable tool call
+	events := conv.Process(resp)
+
+	// Verify no tool_use block was started (since marshal failed before block start)
+	hasToolStart := false
+	for _, e := range events {
+		if e.Event == "content_block_start" {
+			if start, ok := e.Data.(ContentBlockStartEvent); ok {
+				if start.ContentBlock.Type == "tool_use" {
+					hasToolStart = true
+				}
+			}
+		}
+	}
+
+	if hasToolStart {
+		t.Error("expected no tool_use block when arguments cannot be marshaled")
+	}
+}
+
+func TestStreamConverter_MultipleToolCallsWithMixedValidity(t *testing.T) {
+	// Test that valid tool calls still work when mixed with invalid ones
+	conv := NewStreamConverter("msg_123", "test-model")
+
+	unmarshalable := make(chan int)
+
+	resp := api.ChatResponse{
+		Model: "test-model",
+		Message: api.Message{
+			Role: "assistant",
+			ToolCalls: []api.ToolCall{
+				{
+					ID: "call_good",
+					Function: api.ToolCallFunction{
+						Name:      "good_function",
+						Arguments: map[string]any{"location": "Paris"},
+					},
+				},
+				{
+					ID: "call_bad",
+					Function: api.ToolCallFunction{
+						Name:      "bad_function",
+						Arguments: map[string]any{"channel": unmarshalable},
+					},
+				},
+			},
+		},
+		Done:       true,
+		DoneReason: "stop",
+	}
+
+	events := conv.Process(resp)
+
+	// Count tool_use blocks - should only have 1 (the valid one)
+	toolStartCount := 0
+	toolDeltaCount := 0
+	for _, e := range events {
+		if e.Event == "content_block_start" {
+			if start, ok := e.Data.(ContentBlockStartEvent); ok {
+				if start.ContentBlock.Type == "tool_use" {
+					toolStartCount++
+					if start.ContentBlock.Name != "good_function" {
+						t.Errorf("expected tool name 'good_function', got %q", start.ContentBlock.Name)
+					}
+				}
+			}
+		}
+		if e.Event == "content_block_delta" {
+			if delta, ok := e.Data.(ContentBlockDeltaEvent); ok {
+				if delta.Delta.Type == "input_json_delta" {
+					toolDeltaCount++
+				}
+			}
+		}
+	}
+
+	if toolStartCount != 1 {
+		t.Errorf("expected 1 tool_use block, got %d", toolStartCount)
+	}
+	if toolDeltaCount != 1 {
+		t.Errorf("expected 1 input_json_delta, got %d", toolDeltaCount)
+	}
+}

docs/api/anthropic-compatibility.mdx

@@ -4,6 +4,16 @@ title: Anthropic compatibility
 
 Ollama provides compatibility with the [Anthropic Messages API](https://docs.anthropic.com/en/api/messages) to help connect existing applications to Ollama, including tools like Claude Code.
 
+## Recommended models
+
+For coding use cases, models like `qwen3-coder` are recommended.
+
+Pull a model before use:
+```shell
+ollama pull qwen3-coder
+ollama pull glm-4.7:cloud
+```
+
 ## Usage
 
 ### Environment variables
@@ -28,7 +38,7 @@ client = anthropic.Anthropic(
 )
 
 message = client.messages.create(
-    model='llama3.2:3b',
+    model='qwen3-coder',
     max_tokens=1024,
     messages=[
         {'role': 'user', 'content': 'Hello, how are you?'}
@@ -46,7 +56,7 @@ const anthropic = new Anthropic({
 });
 
 const message = await anthropic.messages.create({
-  model: "llama3.2:3b",
+  model: "qwen3-coder",
   max_tokens: 1024,
   messages: [{ role: "user", content: "Hello, how are you?" }],
 });
@@ -60,7 +70,7 @@ curl -X POST http://localhost:11434/v1/messages \
 -H "x-api-key: ollama" \
 -H "anthropic-version: 2023-06-01" \
 -d '{
-  "model": "llama3.2:3b",
+  "model": "qwen3-coder",
   "max_tokens": 1024,
   "messages": [{ "role": "user", "content": "Hello, how are you?" }]
 }'
@@ -81,7 +91,7 @@ client = anthropic.Anthropic(
 )
 
 with client.messages.stream(
-    model='llama3.2:3b',
+    model='qwen3-coder',
     max_tokens=1024,
     messages=[{'role': 'user', 'content': 'Count from 1 to 10'}]
 ) as stream:
@@ -98,7 +108,7 @@ const anthropic = new Anthropic({
 });
 
 const stream = await anthropic.messages.stream({
-  model: "llama3.2:3b",
+  model: "qwen3-coder",
   max_tokens: 1024,
   messages: [{ role: "user", content: "Count from 1 to 10" }],
 });
@@ -117,7 +127,7 @@ for await (const event of stream) {
 curl -X POST http://localhost:11434/v1/messages \
 -H "Content-Type: application/json" \
 -d '{
-  "model": "llama3.2:3b",
+  "model": "qwen3-coder",
   "max_tokens": 1024,
   "stream": true,
   "messages": [{ "role": "user", "content": "Count from 1 to 10" }]
@@ -139,7 +149,7 @@ client = anthropic.Anthropic(
 )
 
 message = client.messages.create(
-    model='llama3.2:3b',
+    model='qwen3-coder',
     max_tokens=1024,
     tools=[
         {
@@ -170,7 +180,7 @@ for block in message.content:
 curl -X POST http://localhost:11434/v1/messages \
 -H "Content-Type: application/json" \
 -d '{
-  "model": "llama3.2:3b",
+  "model": "qwen3-coder",
   "max_tokens": 1024,
   "tools": [
     {
@@ -199,7 +209,7 @@ curl -X POST http://localhost:11434/v1/messages \
 [Claude Code](https://docs.anthropic.com/en/docs/claude-code) can be configured to use Ollama as its backend:
 
 ```shell
-ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY=ollama claude --model llama3.2:3b
+ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY=ollama claude --model qwen3-coder
 ```
 
 Or set the environment variables in your shell profile:
@@ -212,9 +222,13 @@ export ANTHROPIC_API_KEY=ollama
 Then run Claude Code with any Ollama model:
 
 ```shell
-claude --model llama3.2:3b
+# Local models
-claude --model qwen3:8b
+claude --model qwen3-coder
-claude --model deepseek-r1:14b
+claude --model gpt-oss:20b
+
+# Cloud models
+claude --model glm-4.7:cloud
+claude --model minimax-m2.1:cloud
 ```
 
 ## Endpoints
@@ -277,18 +291,33 @@ claude --model deepseek-r1:14b
 
 ## Models
 
-Before using a model, pull it locally with `ollama pull`:
+Ollama supports both local and cloud models.
+
+### Local models
+
+Pull a local model before use:
 
 ```shell
-ollama pull llama3.2:3b
+ollama pull qwen3-coder
 ```
 
+Recommended local models:
+- `qwen3-coder` - Excellent for coding tasks
+- `gpt-oss:20b` - Strong general-purpose model
+
+### Cloud models
+
+Cloud models are available immediately without pulling:
+
+- `glm-4.7:cloud` - High-performance cloud model
+- `minimax-m2.1:cloud` - Fast cloud model
+
 ### Default model names
 
 For tooling that relies on default Anthropic model names such as `claude-3-5-sonnet`, use `ollama cp` to copy an existing model name:
 
 ```shell
-ollama cp llama3.2:3b claude-3-5-sonnet
+ollama cp qwen3-coder claude-3-5-sonnet
 ```
 
 Afterwards, this new model name can be specified in the `model` field: