MCP docs

docs/mint.json

@@ -17,7 +17,6 @@
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@@ -60,25 +59,16 @@
       "url": "https://agentops.ai/contact"
     }
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-  "versions": [
-    "v2",
-    "v1",
-    "v0"
-  ],
+  "versions": ["v2", "v1", "v0"],
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-      "pages": [
-        "v1/quickstart",
-        "v1/examples/examples"
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@@ -140,17 +130,12 @@
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-      "pages": [
-        "v2/introduction"
-      ],
+      "pages": ["v2/introduction"],
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     {
       "group": "Getting Started",
-      "pages": [
-        "v2/quickstart",
-        "v2/examples/examples"
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+      "pages": ["v2/quickstart", "v2/examples/examples"],
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@@ -193,6 +178,7 @@
         "v2/usage/dashboard-info",
         "v2/usage/sdk-reference",
         "v2/usage/typescript-sdk",
+        "v2/usage/mcp-server",
         "v2/usage/advanced-configuration",
         "v2/usage/context-managers",
         "v2/usage/tracking-llm-calls",

docs/v2/examples/openai.mdx

@@ -1,6 +1,6 @@
 ---
 title: 'OpenAI'
-description: 'Load the dataset (ensure you're logged in with huggingface-cli if needed)'
+description: "Load the dataset (ensure you're logged in with huggingface-cli if needed)"
 ---
 {/*  SOURCE_FILE: examples/openai/multi_tool_orchestration.ipynb  */}
 
@@ -322,7 +322,7 @@ Finally, the tool call and its output are appended to the conversation, and the
 
 ### Multi-tool orchestration flow
 
-Now let us try to modify the input query and the system instructions to the responses API in order to follow a tool calling sequence and generate the output. 
+Now let us try to modify the input query and the system instructions to the responses API in order to follow a tool calling sequence and generate the output.
 
 
 ```python
@@ -427,10 +427,10 @@ agentops.end_trace(tracer, end_state="Success")
 ```
 
 
-Here, we have seen  how to utilize OpenAI's Responses API to implement a Retrieval-Augmented Generation (RAG) approach with multi-tool calling capabilities. It showcases an example where the model selects the appropriate tool based on the input query: general questions may be handled by built-in tools such as web-search, while specific medical inquiries related to internal knowledge are addressed by retrieving context from a vector database (such as Pinecone) via function calls. Additonally, we have showcased how multiple tool calls can be sequentially combined to generate a final response based on our instructions provided to responses API. Happy coding! 
+Here, we have seen  how to utilize OpenAI's Responses API to implement a Retrieval-Augmented Generation (RAG) approach with multi-tool calling capabilities. It showcases an example where the model selects the appropriate tool based on the input query: general questions may be handled by built-in tools such as web-search, while specific medical inquiries related to internal knowledge are addressed by retrieving context from a vector database (such as Pinecone) via function calls. Additonally, we have showcased how multiple tool calls can be sequentially combined to generate a final response based on our instructions provided to responses API. Happy coding!
 
 
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 <script type="module" src="/scripts/scroll-img-fadein-animation.js"></script>
 <script type="module" src="/scripts/button_heartbeat_animation.js"></script>
-<script type="module" src="/scripts/adjust_api_dynamically.js"></script>
+<script type="module" src="/scripts/adjust_api_dynamically.js"></script>

docs/v2/usage/mcp-server.mdx

@@ -0,0 +1,88 @@
+---
+title: "MCP Server"
+description: "MCP server for accessing AgentOps trace and span data"
+---
+
+<iframe
+  width="100%"
+  height="300"
+  src="https://www.youtube.com/embed/lTa3Sk8C4f0?si=3r7GO8N1Csh0P9C5RR"
+  title="AgentOps MCP Server"
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+  allowFullScreen
+></iframe>
+
+# MCP Server
+
+AgentOps provides a [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) server that exposes the Public API as a set of tools for AI assistants. This allows AI models to directly query your AgentOps data during conversations and debug AI agents with greater context.
+
+### Configuration & Installation
+
+Add the AgentOps MCP to your MCP client's configuration file.
+
+**npx configuration:**
+```json
+{
+  "mcpServers": {
+    "agentops": {
+      "command": "npx",
+      "args": [
+        "agentops-mcp"
+      ],
+      "env": {
+        "AGENTOPS_API_KEY": ""
+      }
+    }
+  }
+}
+```
+
+**Cursor Deeplink:**
+
+Add the AgentOps MCP to Cursor with Deeplink.
+
+[![Install MCP Server](https://cursor.com/deeplink/mcp-install-dark.svg)](https://cursor.com/install-mcp?name=agentops&config=eyJjb21tYW5kIjoibnB4IGFnZW50b3BzLW1jcCIsImVudiI6eyJBR0VOVE9QU19BUElfS0VZIjoiIn19)
+
+**Smithery:**
+
+To install agentops-mcp for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@AgentOps-AI/agentops-mcp):
+
+```bash
+npx -y @smithery/cli install @AgentOps-AI/agentops-mcp --client claude
+```
+
+### Available Tools
+
+The MCP server exposes the following tools that mirror the Public API endpoints:
+
+#### `auth`
+Authorize using an AgentOps project API key.
+- **Parameters**: `api_key` (string) - Your AgentOps project API key
+- **Usage**: The server will automatically prompt for authentication when needed
+
+#### `get_project`
+Get details about the current project.
+- **Parameters**: None
+- **Returns**: Project information including ID, name, and environment
+
+#### `get_trace`
+Get trace information by ID.
+- **Parameters**: `trace_id` (string) - The trace identifier
+- **Returns**: Trace details and metrics
+
+#### `get_span`
+Get span information by ID.
+- **Parameters**: `span_id` (string) - The span identifier
+- **Returns**: Span attributes and metrics
+
+#### `get_complete_trace`
+Get complete trace information by ID.
+- **Parameters**: `span_id` (string) - The trace identifier
+- **Returns**: Complete trace and associated span details.
+
+### Environment Variables
+
+The MCP server supports the following environment variables:
+
+- `AGENTOPS_API_KEY`: Your AgentOps project API key
+- `HOST`: API endpoint (defaults to `https://api.agentops.ai`)