diff --git a/docs/docs/concepts/structured_outputs.mdx b/docs/docs/concepts/structured_outputs.mdx
index d6d31a5d215..2c0d31d632b 100644
--- a/docs/docs/concepts/structured_outputs.mdx
+++ b/docs/docs/concepts/structured_outputs.mdx
@@ -29,6 +29,22 @@ model_with_structure = model.with_structured_output(schema)
 structured_output = model_with_structure.invoke(user_input)
 ```
 
+:::warning[Tool Order Matters]
+
+When combining structured output with additional tools, bind tools **first**, then apply structured output:
+
+```python
+# Correct
+model_with_tools = model.bind_tools([tool1, tool2])
+structured_model = model_with_tools.with_structured_output(schema)
+
+# Incorrect - will cause tool resolution errors
+structured_model = model.with_structured_output(schema)
+broken_model = structured_model.bind_tools([tool1, tool2])
+```
+
+:::
+
 ## Schema definition
 
 The central concept is that the output structure of model responses needs to be represented in some way.
diff --git a/docs/docs/how_to/structured_output.ipynb b/docs/docs/how_to/structured_output.ipynb
index ae647d70781..fa2fe54b1f1 100644
--- a/docs/docs/how_to/structured_output.ipynb
+++ b/docs/docs/how_to/structured_output.ipynb
@@ -998,6 +998,91 @@
     "\n",
     "chain.invoke({\"query\": query})"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "xfejabhtn2",
+   "metadata": {},
+   "source": [
+    "## Combining with Additional Tools\n",
+    "\n",
+    "When you need to use both structured output and additional tools (like web search), note the order of operations:\n",
+    "\n",
+    "**Correct Order**:\n",
+    "```python\n",
+    "# 1. Bind tools first\n",
+    "llm_with_tools = llm.bind_tools([web_search_tool, calculator_tool])\n",
+    "\n",
+    "# 2. Apply structured output\n",
+    "structured_llm = llm_with_tools.with_structured_output(MySchema)\n",
+    "```\n",
+    "\n",
+    "**Incorrect Order**:\n",
+    "\n",
+    "```python\n",
+    "# This will fail with \"Tool 'MySchema' not found\" error\n",
+    "structured_llm = llm.with_structured_output(MySchema)\n",
+    "broken_llm = structured_llm.bind_tools([web_search_tool])\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "653798ca",
+   "metadata": {},
+   "source": [
+    "**Why Order Matters:**\n",
+    "`with_structured_output()` internally uses tool calling to enforce the schema. When you bind additional tools afterward, it creates a conflict in the tool resolution system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1345f4a4",
+   "metadata": {},
+   "source": [
+    "**Complete Example:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0835637b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pydantic import BaseModel, Field\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "\n",
+    "class SearchResult(BaseModel):\n",
+    "    \"\"\"Structured search result.\"\"\"\n",
+    "\n",
+    "    query: str = Field(description=\"The search query\")\n",
+    "    findings: str = Field(description=\"Summary of findings\")\n",
+    "\n",
+    "\n",
+    "# Define tools\n",
+    "search_tool = {\n",
+    "    \"type\": \"function\",\n",
+    "    \"function\": {\n",
+    "        \"name\": \"web_search\",\n",
+    "        \"description\": \"Search the web for information\",\n",
+    "        \"parameters\": {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\"query\": {\"type\": \"string\", \"description\": \"Search query\"}},\n",
+    "            \"required\": [\"query\"],\n",
+    "        },\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "# Correct approach\n",
+    "llm = ChatOpenAI()\n",
+    "llm_with_search = llm.bind_tools([search_tool])\n",
+    "structured_search_llm = llm_with_search.with_structured_output(SearchResult)\n",
+    "\n",
+    "# Now you can use both search and get structured output\n",
+    "result = structured_search_llm.invoke(\"Search for latest AI research and summarize\")"
+   ]
   }
  ],
  "metadata": {
diff --git a/docs/docs/integrations/chat/anthropic.ipynb b/docs/docs/integrations/chat/anthropic.ipynb
index 6e2c0283862..bcd6637ce5e 100644
--- a/docs/docs/integrations/chat/anthropic.ipynb
+++ b/docs/docs/integrations/chat/anthropic.ipynb
@@ -1240,6 +1240,58 @@
     "response = llm_with_tools.invoke(\"How do I update a web app to TypeScript 5.5?\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "kloc4rvd1w",
+   "metadata": {},
+   "source": [
+    "#### Web search + structured output\n",
+    "\n",
+    "When combining web search tools with structured output, it's important to **bind the tools first and then apply structured output**:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "rjjergy6ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pydantic import BaseModel, Field\n",
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "\n",
+    "# Define structured output schema\n",
+    "class ResearchResult(BaseModel):\n",
+    "    \"\"\"Structured research result from web search.\"\"\"\n",
+    "\n",
+    "    topic: str = Field(description=\"The research topic\")\n",
+    "    summary: str = Field(description=\"Summary of key findings\")\n",
+    "    key_points: list[str] = Field(description=\"List of important points discovered\")\n",
+    "\n",
+    "\n",
+    "# Configure web search tool\n",
+    "websearch_tools = [\n",
+    "    {\n",
+    "        \"type\": \"web_search_20250305\",\n",
+    "        \"name\": \"web_search\",\n",
+    "        \"max_uses\": 10,\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "llm = ChatAnthropic(model=\"claude-3-5-sonnet-20241022\")\n",
+    "\n",
+    "# Correct order: bind tools first, then structured output\n",
+    "llm_with_search = llm.bind_tools(websearch_tools)\n",
+    "research_llm = llm_with_search.with_structured_output(ResearchResult)\n",
+    "\n",
+    "# Now you can use both web search and get structured output\n",
+    "result = research_llm.invoke(\"Research the latest developments in quantum computing\")\n",
+    "print(f\"Topic: {result.topic}\")\n",
+    "print(f\"Summary: {result.summary}\")\n",
+    "print(f\"Key Points: {result.key_points}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1478cdc6-2e52-4870-80f9-b4ddf88f2db2",