McPeval open source makes factors at the protocol level of delivery and operation test

Companies I started to adopt McP context protocol (MCP) primarily to facilitate identifying and directing the use of the agent tool. However, researchers from Salesforce Discover another way to use MCP technology, this time to help evaluate the artificial intelligence agents themselves.

Researchers have unveiled McPeval, a new method and an open source tools group based on the structure of the MCP system that tests the performance of the agent when using tools. They pointed out that the current evaluation methods of factors are limited that these “often depend on fixed and pre -determined tasks, and thus failed to capture the interactive workflow of the real world.”

“McPeval goes beyond the measures of traditional success/failure by collecting detailed tasks and detailed reaction data systematically, creating an unprecedented vision in the agent’s behavior and generating valuable data groups for repetitive improvement,” said researchers. In the paper. “In addition, since both tasks and verifying fully authenticated, the resulting high paths can be used immediately for rapid stabilization and continuous improvement of factors models. Comprehensive evaluation reports created by McPeval also provide visible visions towards healthy communication with factors at a loved level.”

McPeval distinguishes itself by being a fully automatic process, which researchers claimed to allow the rapid assessment of new MCP tools and servers. Both collect information on how agents interact with tools within the MCP server, create artificial data and create a database for standard factors. Users can choose MCP servers and tools inside these servers to test the agent’s performance.

Shelby Hink, chief artificial intelligence research manager at Salesforce and one of the authors of the paper, Venturebeat was difficult to obtain accurate data on the agent’s performance, especially for agents in the roles of the field.

“We have reached the point that if you look at it through the technology industry, many of us have discovered how to publish it. We now need to know how to evaluate it properly,” Henick said. “MCP is a very new idea, a very new model. So, it is great for agents to be able to access the tools, but we again need to evaluate agents on these tools. This is exactly what is about McPeval.”

How to work

The McPeval framework takes the generation of tasks, verification and form evaluation design. Take advantage of multiple large language models (LLMS) so that users can choose to work with models that are more familiar with them, agents can be evaluated through a variety of LLMS available in the market.

Institutions can access McPeval through an open source tool set by Salesforce. Through the dashboard, users confirm the server by identifying a model, which automatically creates the agent’s tasks to follow the chosen MCP server.

Once the user checks from the tasks, McPeval takes the tasks and determines the tool’s necessary calls as a ground reality. These tasks will be used as a basis for the test. Users choose any model they prefer to run the evaluation. McPeval can create a report on the quality of the agent’s work and the test model in accessing and using these tools.

Heinecke said that McPeval not only collects data on standard agents, but can also determine the gaps in the agent’s performance. Information collected by evaluating agents through McPeval works not only for performance test but also to train agents for future use.

“We see McPeval growing to a comprehensive store to evaluate and repair it,” said Hink.

She added that what makes McPeval protrudes by other residents is that it brings the test to the same environment in which the agent will work. The agents are evaluated for the quality of access to tools within the MCP server that is likely to be published.

The paper noticed that in experiments, GPT-4 models often provide the best evaluation results.

Agent performance evaluation

the You need institutions to start Test and monitoring Performance of the agent It led to a breakthrough of frameworks and techniques. Some platforms provide a test and several other ways to assess the performance of the short -term and long -term agent.

Artificial intelligence agents will do tasks on behalf of users, often without You need to demand a person they. Until now, agents have proven that they are useful, but they can Get exhaustion Through a huge amount of tools at her disposal.

GalileoAn emerging company, offers The framework of that Institutions enable the quality to choose the agent tool and determine errors. Salesforce launched capabilities to her agent The dashboard to test the agents. Researchers from the University of Singapore issued the administration Agents to achieve and monitor The reliability of the agent. Several academic studies have also been published on MCP evaluation, including MCP-gradar and McPworld.

MCP-Radar, developed by researchers from the University of Massachusetts Amharees and Xi’an Jiaotong University, is focused on more public world skills, such as software engineering or mathematics. This framework gives priority to efficiency and accuracy of the teacher.

On the other hand, McPworld from Beijing University for Jobs and Wireless Communications brings measuring to the user’s graphic interfaces, a application programming interface and other computer use agents.

Henick ultimately said, how the agents are evaluated will depend on the company and the state of use. However, what is important is that companies choose the most appropriate evaluation framework to meet their specific needs. For institutions, I suggested looking at a special framework for the field to test how agents work in real world scenarios.

“There is value in all of these evaluation frameworks, and these are great starting points because they give some early signs of the strength of the bond,” Henick said. “But I believe that the most important evaluation is your field evaluation and the evaluation data that reflects the environment in which the agent will work.”

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