MCP Manager

# 🤖 AI Agent Building Checklist Our team has built a wide range of AI agents, from relatively straightforward single-purpose agents to complex agents, and even multi-agent teams. We’ve used all that experience and research to put together this checklist, which will help you to successfully create, deploy, and improve your own AI agents. ## 📝 Author Information Created by the team at: [MCP Manager](https://mcpmanager.ai). [MCP Manager](https://mcpmanager.ai) is an MCP gateway which acts as a safety net for your organization's AI agents and their interactions with MCP servers and connected resources. **⭐Star this repo to stay up to date and avoid missing that guide you know you'll need in the future!** ## 📚 Table of Contents - **Part One:** [Defining Your Agent](#part-one-defining-your-agent) - **Part Two:** [Services, Data, and Tools](#part-two-services-data-and-tools) - **Part Three:** [Implementation](#part-three-implementation) - **Part Four:** [Testing](#part-four-testing) - **Part Five:** [Improvements and Refinement](#part-five-improvements-and-refinements) ## Part One: Defining Your Agent - **Primary Purpose:** What is your agent’s main job? - **Key Capabilities:** What key capabilities does the agent need in order to be able to complete its primary purpose? - **Success Criteria:** Create a list of criteria that will define if the agent has successfully achieved its primary purpose, this can include conditions, such as response times, or task-specific criteria, such as consistently maintaining the correct tone of voice when composing emails. - **Agent Autonomy:** How much autonomy and capability to work independently should your AI agent have - how will you enable/restrict these parameters of behavior? - **Reflex Agent:** Responds to trigger events and completes a simple corresponding action (such as activating the air conditioning when the thermostat reaches a certain temperature) - **Model-Based Reflex Agents:** Respond to inputs based on prior experience and an internal model of the environment. - **Goal-Based Agents:** Have a predefined goal and use their context, algorithms, and heuristics to find the most efficient path to that goal. - **Learning Agents:** Continuously uses new information it obtains from any source in its environment to improve their ability to respond to prompts and achieve their primary purpose. - **Multi-Agent Systems (MAS):** Do you need/would it be optimal to split tasks and capabilities across multiple agents instead of using a single agent? ## Part Two: Services, Data, and Tools - **Required Tools and Data:** What tools and data does your agent need to access in order to achieve its Primary Purpose and Success Criteria? - **Connection Methods:** Will you take the easier route and use an MCP server, or will you add data manually/create custom API connections from scratch? - **Memory Management:** This is too complex a topic to cover in detail here, but you should consider: - Which information will be retained in the agent’s long-term memory? - Which method will be used to store information (for example a persistent memory framework or MCP server)? - Which information must be forgotten/held in short-term memory only - **Authentication Methods:** How will your agent securely connect to tools, MCP servers, and data? How will it access restricted resources? For example, will you use OAuth, and how will you configure it? ## Part Three: Implementation - **Tool Selection:** There are a wide range of tools that you can use to build your AI agent, ranging from no-code GPTs to coding-based Python and JavaScript builders. Choose the right one for your skillset and the capability level your agent needs. - **Training:** Determine if your agent requires, or will perform optimally by being fed training data and what those data should be, (such as examples of marketing emails with performance data, or sales calls with outcomes), or if you prefer to take a knowledge and rules based approach instead. - **Core Functionality:** The essential tasks the AI agent is designed to perform to meet its primary objectives. It includes processing inputs (like language, data, or sensor signals), applying reasoning or learned models to make decisions or predictions, and generating outputs or actions that directly satisfy user or business needs. The core functionality should be focused, reliable, and aligned with the agent’s main purpose. - **Additional Functionality:** Supplementary features that enhance or extend the AI agent's capabilities beyond its core tasks. They may include providing proactive suggestions, supporting multi-modal inputs. - **Fine-Tuning:** Add additional guidelines, constraints, and rules for your agent to fine tune its behavior, methods, and outputs. - **Logging and Monitoring:** Implement a method to capture and and store AI agent interactions with data, tools, and users. If you are using a gateway this should be handled already. - **Security Considerations:** What security vulnerabilities does your agent have and how will you mitigate them? Research AI agent-based attack vectors, determine which could affect your agent, and what proactive mitigation strategies you should use. - **Role-Based-Access-Controls:** You should have some means of limiting which data and tools your AI agent is able to access. The easiest and most reliable way to do this is by using an MCP gateway like MCP Manager. With an MCP gateway you can impose role-based-access-controls on agents, agent-types, and human users of AI agents too. ## Part Four: Testing - **Core Capability Tests:** Devise some specific scenarios to test, based on the agent’s Primary Purpose. - **Intent Recognition:** The agent is able to understand and interpret varied user intents. For example it should be able to correctly interpret both "Reserve a table for two on 7th July” and “Book dinner for a couple tonight” - **Accessing Knowledge Sources:** Check that the agent is pulling information correctly, form the correct and desired sources, and is able to understand and exclude irrelevant or erroneous information and sources. This is a good opportunity to add extra instructions into your agent’s context to automatically ignore and avoid specific sources, or use more detailed criteria to help it identify and use the sources you deem appropriate - **Error Handling** (Test how the agent handles): - Incomplete inputs - Malformed or ambiguous queries - Contradictory commands - **Performance Testing:** Is the agent able to complete its tasks quickly and efficiently? Use logs to investigate causes of slowdown or failure and see if improving the agent design can help it to work faster and with fewer errors. ## Part Five: Improvements and Refinement - **Source Feedback:** Create a way to source and feedback from the people using your agent. - **Create Structured Feedback Logs:** Log your feedback in a structured way that makes it easy to link feedback items according to categories (for example, security or efficiency) and themes (for example, - **Prioritize Feedback Items:** Prioritize feedback according to impact, urgency, effort, and other factors. - **Create an Action Plan:** Create an action plan or roadmap to make the improvements and refinement based on your prioritization framework.