The adoption of interoperability requirements, such because the Mannequin Context Protocol (MCP), can present enterprises with insights into how brokers and fashions perform exterior their walled confines. Nonetheless, many benchmarks fail to seize real-life interactions with MCP.
Salesforce AI Analysis developed a brand new open-source benchmark it calls MCP-Universe, which goals to trace LLMs as these work together with MCP servers in the actual world, arguing that it’s going to paint a greater image of real-life and real-time interactions of fashions with instruments enterprises really use. In its preliminary testing, it discovered that fashions like OpenAI’s not too long ago launched GPT-5 are robust, however nonetheless don’t carry out as properly in real-life situations.
“Existing benchmarks predominantly focus on isolated aspects of LLM performance, such as instruction following, math reasoning, or function calling, without providing a comprehensive assessment of how models interact with real-world MCP servers across diverse scenarios,” Salesforce mentioned in a paper.
MCP-Universe captures mannequin efficiency via device utilization, multi-turn device calls, lengthy context home windows and enormous device areas. It’s grounded on present MCP servers with entry to precise knowledge sources and environments.
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Junnan Li, director of AI analysis at Salesforce, instructed VentureBeat that many fashions “still face limitations that hold them back on enterprise-grade tasks.”
“Two of the biggest are: Long context challenges, models can lose track of information or struggle to reason consistently when handling very long or complex inputs,” Li mentioned. “And, Unknown tool challenges, models often aren’t able to seamlessly use unfamiliar tools or systems in the way humans can adapt on the fly. This is why it’s crucial not to take a DIY approach with a single model to power agents alone, but instead, to rely on a platform that combines data context, enhanced reasoning, and trust guardrails to truly meet the needs of enterprise AI.”
MCP-Universe joins different MCP-based proposed benchmarks, resembling MCP-Radar from the College of Massachusetts Amherst and Xi’an Jiaotong College, in addition to the Beijing College of Posts and Telecommunications’ MCPWorld. It additionally builds on MCPEvals, which Salesforce launched in July, which focuses primarily on brokers. Li mentioned the most important distinction between MCP-Universe and MCPEvals is that the latter is evaluated with artificial duties.
The way it works
MCP-Universe evaluates how properly every mannequin performs a collection of duties that mimic these undertaken by enterprises. Salesforce mentioned it designed MCP-Universe to embody six core domains utilized by enterprises: location navigation, repository administration, monetary evaluation, 3D design, browser automation and net search. It accessed 11 MCP servers for a complete of 231 duties.
Location navigation focuses on geographic reasoning and the execution of spatial duties. The researchers tapped the Google Maps MCP server for this course of.
The repository administration area seems at codebase operations and connects to the GitHub MCP to show model management instruments like repo search, difficulty monitoring and code enhancing.
Monetary evaluation connects to the Yahoo Finance MCP server to judge quantitative reasoning and monetary market decision-making.
3D design evaluates the usage of computer-aided design instruments via the Blender MCP.
Browser automation, linked to Playwright’s MCP, exams browser interplay.
The net looking area employs the Google Search MCP server and the Fetch MCP to examine “open-domain information seeking” and is structured as a extra open-ended process.
Salesforce mentioned that it needed to design new MCP duties that replicate actual use instances. For every area, they created 4 to 5 sorts of duties that the researchers assume LLMs can simply full. For instance, the researchers assigned the fashions a purpose that concerned route planning, figuring out the optimum stops after which finding the vacation spot.
Every mannequin is evaluated on how they accomplished the duties. Li and his workforce opted to observe an execution-based analysis paradigm relatively than the extra widespread LLM-as-a-judge system. The researchers famous the LLM-as-a-judge paradigm “is not well-suited for our MCP-Universe scenario, since some tasks are designed to use real-time data, while the knowledge of the LLM judge is static.”
Salesforce researchers used three varieties of evaluators: format evaluators to see if the brokers and fashions observe format necessities, static evaluators to evaluate correctness over time and dynamic evaluators for fluctuating solutions like flight costs or GitHub points.
“MCP-Universe focuses on creating challenging real-world tasks with execution-based evaluators, which can stress-test the agent in complex scenarios. Furthermore, MCP-Universe offers an extendable framework/codebase for building and evaluating agents,” Li mentioned.
Even the massive fashions have hassle
To check MCP-Universe, Salesforce evaluated a number of fashionable proprietary and open-source fashions. These embody Grok-4 from xAI, Anthropic’s Claude-4 Sonnet and Claude 3.7 Sonnet, OpenAI’s GPT-5, o4-mini, o3, GPT-4.1, GPT-4o, GPT-oss, Google’s Gemini 2.5 Professional and Gemini 2.5 Fkash, GLM-4.5 from Zai, Moonshot’s Kimi-K2, Qwen’s Qwen3 Coder and Qwen3-235B-A22B-Instruct-2507 and DeepSeek-V3-0304 from DeepSeek. Every mannequin examined had at the very least 120B parameters.
In its testing, Salesforce discovered GPT-5 had the most effective success charge, particularly for monetary evaluation duties. Grok-4 adopted, beating all of the fashions for browser automation, and Claude-4.0 Sonnet rounds out the highest three, though it didn’t submit any efficiency numbers greater than both of the fashions it follows. Amongst open-source fashions, GLM-4.5 carried out the most effective.
Nonetheless, MCP-Universe confirmed the fashions had problem dealing with lengthy contexts, particularly for location navigation, browser automation and monetary evaluation, with effectivity falling considerably. The second the LLMs encounter unknown instruments, their efficiency additionally drops. The LLMs demonstrated problem in finishing greater than half of the duties that enterprises sometimes carry out.
“These findings highlight that current frontier LLMs still fall short in reliably executing tasks across diverse real-world MCP tasks. Our MCP-Universe benchmark, therefore, provides a challenging and necessary testbed for evaluating LLM performance in areas underserved by existing benchmarks,” the paper mentioned.
Li instructed VentureBeat that he hopes enterprises will use MCP-Universe to achieve a deeper understanding of the place brokers and fashions fail on duties in order that they will enhance both their frameworks or the implementation of their MCP instruments.
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