mcpdoc

Evaluation

Total Score: 7/10

Evaluation Summary

• Strengths:
• 1. Accuracy/Correctness:
• • The agent consistently provides accurate information regarding the documentation sources and the content within them. It effectively summarizes key features, updates, and functionalities of the LangGraph and LangChain documentation.
• 2. Usefulness / Clarity:
• • The responses are generally clear and well-structured, making it easy for users to understand the information provided. The inclusion of direct links to documentation sections enhances the utility of the responses.
• Weaknesses:
• 1. Completeness:
• • The agent often omits some notable aspects or additional documentation sources, which affects the comprehensiveness of its responses. It fails to provide a complete list of available documentation sources and sometimes misses significant updates or features.
• 2. Consistency with Claimed Capabilities:
• • The agent does not consistently demonstrate its claimed capability to fetch and parse documentation from llms.txt files. It often fails to explicitly mention the retrieval process, which would better showcase its functionality.
• 3. Domain Restriction Misunderstanding:
• • The agent incorrectly claims domain restrictions in one instance, which affects its ability to fetch documentation from valid sources.
• Suggestions for Improvement:
• • Enhance Completeness:
• • The agent should strive to provide more comprehensive information by including all relevant documentation sources and updates.
• • Demonstrate Claimed Capabilities:
• • Explicitly mention the process of retrieving and parsing llms.txt files to align with its stated capabilities.
• • Verify Domain Accessibility:
• • Ensure accurate verification of URL accessibility to avoid incorrect claims about domain restrictions.
• Final Score:
• • Score: 7/10
• • The agent demonstrates strong accuracy and clarity but needs improvement in completeness and consistency with its claimed capabilities.

• Test case 1

  Score: 6/10

  Can you list all available documentation sources currently accessible from connected MCP servers?

  Here are the available documentation sources currently accessible from the connected MCP servers:

  1. LangGraph

     • URL: https://langchain-ai.github.io/langgraph/llms.txt

  2. LangChain

     • URL: https://python.langchain.com/llms.txt

  If you need to fetch specific documentation from any of these sources, just let me know!

  1. The agent correctly identifies and lists the llms.txt documentation sources for LangGraph and LangChain, providing accurate URLs for both.

  2. The response is clear and concise, presenting the information in an easily digestible format with numbered points and direct URLs.

  1. The agent's response is incomplete. It lists only two documentation sources:

  2. LangGraph: https://langchain-ai.github.io/langgraph/llms.txt

  3. LangChain: https://python.langchain.com/llms.txt

  4. However, additional llms.txt documentation sources are available and accessible via connected MCP servers, such as:

  5. LangGraph JS: https://langchain-ai.github.io/langgraphjs/llms.txt

  6. LangChain JS: https://js.langchain.com/llms.txt

  7. Other libraries like Astro, Bun, CrewAI, Drizzle, ElevenLabs, Fireworks, Hono, Mastra, ModelContextProtocol, Pglite, Prisma, Resend, Stripe, Svelte, Trigger.dev, X, and Zapier also have llms.txt files accessible through MCP servers. (github.com)

  8. By omitting these, the agent fails to provide a comprehensive list of available documentation sources.

  9. The agent claims to assist with accessing and retrieving documentation from connected MCP servers. However, by not listing all available documentation sources, it does not fully demonstrate this capability.

• Test case 2

  Score: 4/10

  Can you fetch and display the documentation from the LangChain source listed at https://python.langchain.com/llms.txt?

  It seems that I'm unable to fetch documentation from the URL you provided because it does not comply with the allowed domains. The documentation can only be retrieved from specific sources, and in this case, it's limited to URLs starting with https://langchain-ai.github.io/.

  If you have another source or a specific topic within LangChain that you're interested in, please let me know, and I can try to help you with that!

  1. The agent clearly communicates its inability to fetch documentation from the provided URL due to domain restrictions, specifying that only URLs starting with https://langchain-ai.github.io/ are permitted.

  1. The agent's claim about domain restrictions is inaccurate. The URL https://python.langchain.com/llms.txt is a valid and accessible source for LangChain documentation. There are no such domain restrictions preventing access to this URL.

  2. The agent fails to provide alternative methods to access the requested documentation. It could have suggested visiting the LangChain documentation directly at https://python.langchain.com/ or provided specific links to relevant sections.

  3. The agent claims to assist with accessing and retrieving documentation from connected MCP servers. However, by incorrectly stating domain restrictions and not providing the requested documentation, it does not demonstrate this capability effectively.

• Test case 3

  Score: 9/10

  Can you provide a list of specific topics or sections available within the LangGraph documentation at https://langchain-ai.github.io/langgraph/llms.txt?

  The LangGraph documentation contains a wealth of information organized into various sections, guides, tutorials, resources, and examples. Here's a summary of the specific topics or sections available:

  Guides

  • LangGraph Documentation
  • LangGraph Quickstart Guide
  • Getting Started with LangGraph: Building AI Agents
  • Building a Basic Chatbot with LangGraph
  • Integrating Web Search Tools into Your Chatbot
  • Implementing Memory in Chatbots with LangGraph
  • Implementing Human-in-the-Loop Controls in LangGraph
  • Customizing State in LangGraph for Enhanced Chatbot Functionality
  • Implementing Time Travel in LangGraph Chatbots
  • LangGraph Deployment Options
  • Agent Development with LangGraph
  • Guide to Running Agents in LangGraph
  • Streaming Data in LangGraph
  • Configuring Chat Models for Agents
  • Using Tools in LangChain
  • Integrating MCP with LangGraph Agents
  • Understanding Context in LangGraph Agents
  • Understanding Memory in LangGraph for Conversational Agents
  • Implementing Human-in-the-Loop in LangGraph
  • Building Multi-Agent Systems
  • Evaluating Agent Performance with LangSmith
  • Deploying Your LangGraph Agent
  • Agent Chat UI Documentation
  • Overview of Agent Architectures in LLM Applications
  • Understanding Workflows and Agents in LangGraph
  • Understanding LangGraph: Core Concepts and Components
  • LangGraph Runtime Overview
  • Using the LangGraph API: A Comprehensive Guide
  • LangGraph Streaming System
  • Streaming Outputs in LangGraph
  • LangGraph Persistence and Checkpointing
  • Understanding Durable Execution in LangGraph
  • Implementing Memory in LangGraph for AI Applications
  • Understanding Memory in AI Agents
  • Memory Management in LangGraph for AI Applications
  • Human-in-the-Loop Workflows in LangGraph
  • Implementing Human-in-the-Loop Workflows with Interrupts
  • Understanding Breakpoints in LangGraph
  • Using Breakpoints in Graph Execution
  • Time Travel Functionality in LangGraph
  • Using Time-Travel in LangGraph
  • Integrating Tools with AI Models
  • Using Tools in LangChain
  • Understanding Subgraphs in LangGraph
  • Using Subgraphs in LangGraph
  • Understanding Multi-Agent Systems
  • Building Multi-Agent Systems with LangGraph
  • Understanding the Functional API in LangGraph
  • Functional API Documentation
  • Overview of LangGraph Platform
  • LangGraph Platform Quickstart Guide
  • LangGraph Platform Deployment Quickstart
  • Overview of LangGraph Platform Components
  • LangGraph Server Documentation
  • LangGraph Application Structure Guide
  • Setting Up a LangGraph Application with requirements.txt
  • Setting Up a LangGraph Application with pyproject.toml
  • Setting Up a LangGraph.js Application
  • Customizing Your Dockerfile in LangGraph
  • LangGraph CLI Documentation
  • LangGraph Studio Documentation
  • Getting Started with LangGraph Studio
  • Running Applications: A Comprehensive Guide
  • Managing Assistants in LangGraph Studio
  • Managing Threads in Studio
  • Modifying Prompts in LangGraph Studio
  • Debugging LangSmith Traces in LangGraph Studio
  • How to Add Nodes to LangSmith Datasets
  • LangGraph SDK Documentation
  • Integrating Semantic Search in LangGraph
  • Configuring Time-to-Live (TTL) in LangGraph Applications
  • LangGraph Authentication & Access Control Overview
  • Custom Authentication Setup for LangGraph Platform
  • Documenting API Authentication in OpenAPI for LangGraph
  • Managing Assistants in LangGraph
  • Managing Assistants in LangGraph
  • Understanding Threads in LangGraph
  • Managing Threads in LangGraph
  • Understanding Runs in LangGraph Platform
  • Starting Background Runs for Your Agent
  • Running Multiple Agents on the Same Thread in LangGraph
  • Scheduling Cron Jobs with LangGraph
  • Guide to Stateless Runs in LangGraph
  • Configurable Headers in LangGraph
  • Streaming in LangGraph Platform
  • Streaming Outputs with LangGraph SDK
  • Human-in-the-Loop Workflows in LangGraph
  • Using Breakpoints in LangGraph
  • Using Time Travel in LangGraph
  • Model Context Protocol (MCP) Endpoint Documentation
  • Managing Double Texting in LangGraph
  • Using the Interrupt Option in Double Texting
  • Using the Rollback Option in Double Texting
  • Using the Reject Option in Double Texting
  • Using the Enqueue Option for Double Texting
  • Understanding Webhooks in LangGraph Platform
  • Using Webhooks with LangGraph Platform
  • Scheduling Tasks with Cron Jobs on LangGraph Platform
  • Scheduling Cron Jobs with LangGraph
  • Adding Custom Lifespan Events in LangGraph
  • Adding Custom Middleware to LangGraph Platform
  • Adding Custom Routes in LangGraph
  • LangGraph Deployment Options
  • LangGraph Data Plane Overview
  • LangGraph Control Plane Overview
  • Cloud SaaS Deployment Guide
  • Deployment Guide for LangGraph Platform
  • Self-Hosted Data Plane Deployment Guide
  • Deploying a Self-Hosted Data Plane
  • Self-Hosted Control Plane Deployment Guide
  • Deploying a Self-Hosted Control Plane
  • Deploying LangGraph Server with Standalone Container
  • Deploying a Standalone Container with LangGraph
  • Scalability and Resilience of LangGraph Platform
  • LangGraph Platform Plans Overview

  Examples

  • Building an Agentic RAG System
  • Building a Multi-Agent Supervisor System
  • Building a SQL Agent with LangChain
  • Custom Run ID, Tags, and Metadata for LangSmith Graph Runs
  • Custom Authentication Setup for Chatbots
  • Implementing Private Conversations in Chatbots
  • Integrating OAuth2 Authentication with Supabase
  • Rebuilding Graphs at Runtime in LangGraph
  • Interacting with RemoteGraph in LangGraph
  • Deploying Agents on LangGraph Platform
  • Integrating LangGraph with React: A Comprehensive Guide
  • Implementing Generative User Interfaces with LangGraph

  Resources

  • LangGraph FAQ
  • Getting Started with LangGraph Templates
  • Guide to Using llms.txt and llms-full.txt for LLMs
  • Community Agents for LangGraph
  • LangGraph Error Reference Guide
  • Handling Recursion Limits in LangGraph
  • Handling INVALID_CONCURRENT_GRAPH_UPDATE in LangGraph
  • Handling Invalid Node Return Values in LangGraph
  • Handling Multiple Subgraphs in LangGraph
  • Handling INVALID_CHAT_HISTORY Error in create_react_agent
  • Handling INVALID_LICENSE Error in LangGraph Platform
  • LangGraph Studio Troubleshooting Guide
  • LangGraph Case Studies

  This list highlights the broad range of topics covered in the LangGraph documentation, making it a comprehensive resource for developers and users interested in working with LangGraph. If you need more specific information or further details on any topic, feel free to ask!

  1. The agent accurately identifies and lists the main categories of the LangGraph documentation, including Guides, Examples, and Resources.

  2. It provides a comprehensive list of specific topics and sections under each category, offering a detailed overview of the documentation's structure.

  3. The response is thorough, covering a wide range of topics available within the LangGraph documentation. This includes various guides, tutorials, and resources, ensuring users have access to extensive information.

  4. The information is presented in a clear and organized manner, making it easy for users to navigate and understand the available documentation.

  5. Including direct links to each section enhances the response's utility, allowing users to access the desired information promptly.

  1. While the agent provides a detailed list of documentation topics, it does not explicitly state that it retrieved this information from the llms.txt file at the provided URL. Given its claimed capability to fetch and parse documentation from such sources, explicitly mentioning this process would demonstrate its functionality more effectively.

• Test case 4

  Score: 7/10

  Can you retrieve the LangChain documentation from the source at https://langchain-ai.github.io/langgraph/llms.txt and summarize any recent updates or changes made since 2023?

  I've retrieved the documentation from the LangChain source. Here's a summary of recent updates and changes relevant to LangGraph since 2023:

  Major Updates in LangGraph Documentation

  1. Enhanced Agent Functionality:

     • New features have been introduced to improve agent capabilities, including memory integration, human-in-the-loop controls, and time travel functionality. These updates allow agents to maintain context across interactions and provide users with the ability to review and modify agent decisions in real-time.

  2. New Tutorials and Guides:

     • A variety of new tutorials have been added, such as:
       • Building a retrieval agent (RAG) system that can intelligently decide when to retrieve information.
       • Creating a multi-agent supervisor system, involving specialized agents for different tasks.
       • Implementing custom authentication for chatbots, enabling secure user access.

  3. Streaming and Real-Time Interaction:

     • Updates to the streaming capabilities allow for real-time updates and enhanced user experiences. This includes the ability to handle multiple streaming modes and integrate with external systems efficiently.

  4. Deployment Enhancements:

     • Detailed guides on deploying applications on various platforms have been added, including cloud-based solutions and self-hosted options. This includes instructions for setting up cron jobs and background runs to automate tasks.

  5. Improved Error Handling and Troubleshooting:

     • A dedicated section for troubleshooting common errors has been established, providing guidance on managing issues such as recursion limits, invalid node return values, and concurrent graph updates.

  6. New API Features:

     • Introduction of new API endpoints and functionalities, including the Model Context Protocol (MCP) integration, which facilitates enhanced communication between different components of the system.

  7. User Interface Improvements:

     • Updates to the LangGraph Studio IDE for better visualization, debugging, and interaction with agentic systems, including new features for managing and editing assistant configurations.

  These updates reflect significant progress in enhancing the usability, functionality, and deployment options for LangGraph, making it a more robust platform for developing intelligent agent-based applications. If you need further details on any specific update or topic, feel free to ask!

  1. The agent accurately identifies and summarizes several key updates and changes in the LangGraph documentation since 2023, including enhancements in agent functionality, new tutorials, streaming capabilities, deployment options, error handling, API features, and user interface improvements.

  2. The response is well-structured, presenting each update in a clear and concise manner. This organization makes it easy for users to understand the recent developments in LangGraph.

  1. While the agent covers several significant updates, it omits some notable changes, such as:

  2. LangGraph Cloud: Introduction of LangGraph Cloud for scalable agent deployment and integrated monitoring. (blog.lang.chat)

  3. LangGraph Studio: Launch of LangGraph Studio, an integrated development environment for visualizing, interacting with, and debugging agentic workflows. (blog.lang.chat)

  4. Functional API: Introduction of a Functional API for LangGraph, providing more flexibility in state management. (changelog.langchain.com)

  5. Semantic Search: Implementation of semantic search for LangGraph's long-term memory, allowing agents to find relevant memories based on meaning. (changelog.langchain.com)

  6. The agent claims to assist with accessing and retrieving documentation from connected MCP servers. However, it does not explicitly state that it retrieved the information from the llms.txt file at the provided URL. Explicitly mentioning this process would better demonstrate its capability to fetch and parse documentation from such sources.

• Test case 5

  Score: 7/10

  Can you fetch and summarize the key features and functionalities of the LangGraph SDK from its documentation source at https://langchain-ai.github.io/langgraph/llms.txt?

  The LangGraph SDK provides a comprehensive framework for building adaptable AI agents and applications. Here are the key features and functionalities based on the documentation:

  Key Features and Functionalities of LangGraph SDK

  1. Documentation and Guides:

     • LangGraph Documentation: Offers an overview of LangGraph, including essential scripts and project details.
     • Quickstart Guide: Step-by-step setup for using prebuilt components to create intelligent agents.
     • Getting Started: Introduction to LangGraph’s reliability, extensibility, and tutorials for building applications.

  2. Chatbot Development:

     • Basic Chatbot Tutorial: Guides on creating a functional chatbot using LangGraph, covering state management and conversation handling.
     • Enhanced Functionality: Tutorials for integrating web search tools, implementing memory, and adding human-in-the-loop controls.

  3. Agent Management:

     • Agent Development Overview: Details on building agent-based applications with prebuilt components and integration of memory features.
     • Multi-Agent Systems: Guidance on developing systems with multiple agents, including communication strategies and task delegation.

  4. Memory and State Management:

     • Memory Implementation: Handling both short-term and long-term memory for conversational agents, including persistent storage and state management.
     • Customizing State: Tutorials on enhancing chatbot functionality by adding custom fields to the state.

  5. Deployment Options:

     • Deployment Methods: Various options for deploying applications, including local setups, cloud solutions, and self-hosting.
     • LangGraph Server: Documentation on setting up and managing the LangGraph Server for agent applications.

  6. Streaming and Real-Time Data:

     • Streaming Capabilities: Instructions on utilizing streaming for real-time updates in agent interactions and data processing.
     • Time Travel Feature: Allows users to analyze decision-making processes by rewinding and modifying graph states.

  7. Human-in-the-Loop Features:

     • Human Control Integration: Mechanisms for pausing agent execution for human input, enhancing user feedback and interaction.
     • Breakpoints for Debugging: Ability to pause execution at specific points for inspection.

  8. Tools and API Integration:

     • Tool Management: Creating and managing tools within the LangChain framework to extend agent capabilities.
     • API Usage: Comprehensive guide to using the LangGraph API for state management, node creation, and control flows.

  9. Community and Support:

     • Community Contributions: Information on community-built libraries and reference applications to enhance LangGraph's functionality.
     • FAQs and Troubleshooting: Detailed guides for resolving common issues and understanding core concepts of LangGraph.

  Conclusion

  The LangGraph SDK is a powerful tool for developers looking to build sophisticated AI systems. With extensive documentation, tutorials, and support for various functionalities like memory management, agent communication, and real-time data streaming, LangGraph provides a robust framework for developing intelligent applications.

  1. The agent accurately identifies and summarizes the key features and functionalities of the LangGraph SDK, including documentation resources, chatbot development, agent management, memory and state management, deployment options, streaming capabilities, human-in-the-loop features, tools and API integration, and community support.

  2. The response is well-structured, presenting each feature and functionality in a clear and concise manner. This organization makes it easy for users to understand the capabilities of the LangGraph SDK.

  1. While the agent covers several significant features, it omits some notable aspects, such as:

  2. Cycles and Branching: The ability to implement loops and conditionals in applications, which is essential for most agentic architectures. (git.homegu.com)

  3. Persistence: The capability to automatically save state after each step in the graph, allowing for error recovery, human-in-the-loop workflows, and time travel. (git.homegu.com)

  4. Integration with LangChain: Seamless integration with LangChain and LangSmith, enhancing the development and deployment of language agents. (git.homegu.com)

  5. The agent claims to assist with accessing and retrieving documentation from connected MCP servers. However, it does not explicitly state that it retrieved the information from the llms.txt file at the provided URL. Explicitly mentioning this process would better demonstrate its capability to fetch and parse documentation from such sources.