MCP Benchmark: Top MCP Servers for Web Access
We benchmarked 8 MCP servers across web search, extraction, and browser automation by running 4 different tasks 5 times each. We also tested scalability with 250 concurrent AI agents.
MCP servers with web access capabilities
Product | Success rate for web
search and extract | Success rate for
browser automation | Web search and
extraction speed (s) | Browser automation
speed (s) | Scalability score |
|---|---|---|---|---|---|
100% | 90% | 30 | 30 | 77% | |
78% | 0% | 32 | N/A | 19% | |
75% | N/A | 14 | N/A | 54% | |
Nimble | 93% | N/A | 16 | N/A | 51% |
Firecrawl | 83% | N/A | 7 | N/A | 65% |
Hyperbrowser | 63% | 90% | 118 | 93 | N/A |
Browserbase | 48% | 5% | 51 | 104 | N/A |
Tavily | 38% | N/A | 14 | N/A | 45% |
Exa | 23% | N/A | 15 | N/A | N/A |
*Web search & extraction tasks are run with Bright Data’s default MCP server, browser automation tasks are run with Bright Data MCP Pro Mode, since the tools needed for browser automation are available on the Pro Mode.
**The table is sorted based on the scores in the web search & extraction category, with sponsors displayed at the top.
Each of the dimensions above and their measurement methods are outlined below:
Success rate of MCP servers in web access
*N/A indicates that the MCP server does not have this capability.
We benchmarked the products across two different categories: web search & extraction and browser automation. Our benchmark results reveal that Bright Data has the highest success rate in web search & extraction tasks, completing 100% of these tasks successfully. In the browser automation tasks, Bright Data (Pro Mode) and Hyperbrowser have the highest success rates, with 90% task completion rates.
Across all the tools we benchmarked, Apify, Bright Data, Browserbase, and Hyperbrowser are the only ones with both of the capabilities required for agents working on the web:
- Web search & extraction includes searching the web and using links on the page to navigate between pages to collect and process data.
- Browser automation includes interacting with JS elements to fill forms etc.
To see the tasks used in the benchmark in detail, see our methodology.
Speed
Our evaluation shows:
- Web search & extraction: Firecrawl is the fastest MCP with the average MCP run time for correct results of 7 seconds and its accuracy rate was 83%.
- Browser automation: Bright Data is the fastest with 30 seconds of average MCP run time for correct results and its accuracy rate was 90%.
Speed metrics measure only successfully completed tasks. Failed attempts that return quick error messages aren’t included; they’re not comparable to actual task completion times.
Our dataset for navigation included the participation of all brands and yielded 80 data points (i.e. 8 brands, 2 tasks and 5 repetitions for each task). Based on these data points, there seems to be a negative correlation between success rates and speed:
This correlation is intuitive:
- Sometimes websites identify bots as suspicious traffic and trigger anti-scraping features.
- This leads some MCP servers to fail.
- Those that don’t fail need to use unblocking technology which can be slower (i.e. 95% confidence interval includes 4 seconds for one of the providers in our web unblocker benchmark.
Scalability
This benchmark measures the performance and reliability of MCP servers under a high volume of concurrent, autonomous AI agent tasks. X axis, Success Rate (%), represents the provider’s score from our single-agent web search and extraction benchmark. Y axis, Scalability Score (%), is derived from the high-concurrency load test detailed below, which measures server stability and reliability under stress.
Each agent was built on the LangChain create_react_agent framework, powered by the gpt-4.1-nano-2025-04-14 language model. Agents were assigned diverse e-commerce search prompts, such as “Go to target.com, find a throw pillow under 20 dollars.” A task was considered successful only if the agent navigated the website, found a matching product, and returned the required data (url, price, rating) in a structured JSON format within a 5-minute time limit.
The test revealed the following key differences in both success rate and the average time required to complete a successful task:
- Bright Data emerged as the overall leader, achieving the highest success rate at 76.8% with a competitive average completion time of 48.7 seconds per successful task.
- Firecrawl delivered a success rate of 64.8%, with an average task duration of 77.6 seconds.
- Oxylabs demonstrated the fastest performance, completing its successful tasks in an average of just 31.7 seconds, while maintaining a solid success rate of 54.4%.
- Nimble recorded a 51.2% success rate, but its successful tasks took significantly longer, averaging 182.3 seconds to complete.
- Tavily completed the tasks with a success rate of 45%, with the second fastest average completion time of 41.3 seconds.
- Apify completed the test with a lower success rate of 18.8%, though its successful tasks were relatively quick, averaging 45.9 seconds.
Potential reasons behind the performance differences
The benchmark results reveal significant variations in MCP server performance across web search & extraction and browser automation tasks. These differences stem from several architectural and strategic factors:
Anti-bot detection and unblocking capabilities
The most significant factor affecting performance is how MCP servers handle website anti-scraping measures. Our results show a negative correlation between success rates and speed, which can be explained by:
Bright Data and Hyperbrowser achieve the highest success rates (90-100%) by employing sophisticated unblocking technology that mimics human behavior and bypasses bot-detection systems. This advanced anti-bot evasion requires additional processing time, explaining why Bright Data’s average completion time (30 seconds for browser automation) is slower than some competitors.
Firecrawl, while being the fastest for web search & extraction (7 seconds average), achieves only an 83% success rate. This suggests a lighter approach to anti-bot measures, prioritizing speed over comprehensive unblocking.
Oxylabs demonstrates the fastest scalability performance (31.7 seconds average) but with a moderate 54.4% success rate, indicating a balanced approach that doesn’t fully address all anti-scraping challenges.
Architectural differences in rendering and execution
The capability gap between providers reveals fundamental architectural distinctions:
Full-stack browser automation providers (Apify, Bright Data, Browserbase, and Hyperbrowser) support both web search & extraction and JavaScript-heavy browser automation. These platforms typically run full browser instances with headless Chrome or similar technologies, enabling form filling, JavaScript execution, and complex interactions. This comprehensive approach requires more infrastructure but provides versatility for agent-based workflows.
Specialized extraction providers (Exa, Firecrawl, Nimble, Oxylabs, Tavily) focus primarily on web search and content extraction without full browser automation capabilities. These services often use lighter-weight scraping methods that can be faster but cannot handle interactive elements like forms or JavaScript-dependent content.
Scalability and infrastructure design
The 250-concurrent-agent stress test revealed critical infrastructure differences:
Bright Data’s 76.8% success rate under high concurrency demonstrates robust distributed infrastructure designed for enterprise-scale operations. Their ability to maintain performance under load suggests dedicated proxy networks and load-balancing systems.
Nimble’s significantly longer completion time (182.3 seconds average) despite a 51.2% success rate indicates potential bottlenecks in their infrastructure when handling concurrent requests, possibly due to queuing mechanisms or limited proxy pool capacity.
Apify’s dramatic performance drop in scalability tests (18.8% success rate) compared to standard benchmarks suggests their infrastructure may be optimized for different use cases rather than high-concurrency agent workloads.
Primary focus and optimization strategies
Each provider’s performance profile reflects their core business strategy:
Speed-optimized providers like Firecrawl and Oxylabs prioritize quick response times, making them suitable for high-volume, low-complexity tasks where some failures are acceptable. Their faster completion times (7-31 seconds) come at the cost of success rate.
Reliability-focused providers like Bright Data and Hyperbrowser optimize for task completion over speed, making them more suitable for critical workflows where accuracy matters more than processing time. Their 90-100% success rates justify the additional seconds required per task.
Balanced providers like Tavily aim for the middle ground, achieving 45% success rates with competitive 41.3-second completion times, positioning themselves for use cases where neither extreme speed nor maximum reliability is the primary requirement.
Resource allocation and pricing implications
The performance differences also reflect how providers allocate computational resources, which directly impacts their pricing models. Providers with higher success rates typically invest more in:
- Larger proxy networks to rotate IPs and avoid detection
- More sophisticated fingerprinting technologies
- Longer retry mechanisms and fallback strategies
- Enhanced JavaScript rendering capabilities
Methodology to assess the MCP servers’ web access capabilities
We integrated MCPs into a LangGraph agent framework using langchain-mcp-adapters. Four prompts tested different capabilities:
Web Search & Extraction:
- AI SDR for lead generation: “Go to LinkedIn, find 2 people who work at AIMultiple, provide their names and profile URLs.”
- Shopping assistant: “Go to Amazon and find 3 headphones under $30. Provide their names, ratings and URLs.”
- Browser Automation: 3. Travel assistant: “Find the best price for the Betsy Hotel, South Beach, Miami on June 16, 2025. Provide the price and URL.” 4. Form filler: “Go to https://aimultiple.com/, enter my email xxx@aimultiple.com in the newsletter subscription and click subscribe.”
We executed each task 5 times per AI agent and evaluated performance based on specific data points.
Each task constituted an equal amount of the total score, with points awarded for successfully retrieving each required data element. Our code tracked both the MCP tools’ execution time and the complete agent processing duration, using claude-3-5-sonnet-20241022 as the large language model of the AI agent.
To be fair to all MCPs, we used the same agent with the same prompts and the same system prompts. The system prompt is written in a language suitable for all the agents (no specific tool mentions or detailed instructions).
The first three tasks measured the MCPs’ search and extraction capabilities, and the last task measured their browser automation abilities.
Features
We have also measured some important features of these MCP servers. For an explanation of features, please see the methodology section in agent browser benchmark.
Search engine support
Targeting
Security
Data security is crucial for enterprise operations. We checked whether the companies of these agent browsers had data security certification. All of the companies claim on their websites to have either an ISO 27001 or a SOC 2 certification.
Pricing benchmark
Since all MCP servers with web access capabilities use different parameters in pricing, it is hard to compare them.
Therefore, we measured their price for a single task. It is difficult to measure the cost for only correct tasks, as most providers do not break down costs granularly over time. Therefore, to be fair to all products, we choose the first task for measuring the success of the web search and extraction benchmark, as it has the highest overall success rate. For the browser automation benchmark, we choose the last task to measure the cost of the task.
Most products are available through various plans with different limits, and some of these plans also allow for the purchase of additional credits. They measure the spent credits in different parameters like per API call, per GB, or per page.
Please note that these prices do not include the cost of the LLM and our cost of using Claude Sonnet 3.5 was more than the browsing costs during these tasks. Therefore, LLM pricing is likely to be more important than MCP server pricing while building agents for web-related tasks.
*Prices may vary depending on the selected plan and enterprise discounts.
Participants
We included all MCP servers that provide cloud-based web browsing capabilities:
- Apify
- Bright Data
- Browserbase
- Exa
- Firecrawl
- Hyperbrowser
- Nimble
- Oxylabs
- Tavily
Apify, Bright Data and Oxylabs are sponsors of AIMultiple.
For this version of our benchmark, we excluded MCP servers that worked on the users’ own devices since they have limited capabilities for responding to a high number of requests. If we missed any cloud-based MCP servers with web browsing capabilities, please let us know in the comments.
MCP web browsing challenges & mitigations
When configured in an MCP client such as Claude Desktop, LLMs can leverage specialized MCP servers. Web access MCPs are particularly valuable as they enable web data extraction, including the ability to render JavaScript-heavy pages, bypass common access restrictions, take actions, fill forms and access geo-restricted content from various global locations, but they come with some challenges.
While we faced similar challenges to the agent browser benchmark, MCPs present novel challenges to benchmarking. LLMs, with the addition of an external memory function, can be used as a Turing machine, and with an MCP server that provides browsing capabilities, it is theoretically possible to complete any web navigation or browser automation task with MCP servers that provide these capabilities.
Therefore, by writing custom code for each agent, it is possible to achieve 100% success rates. However, that is not a good proxy for MCP users who want to provide simple instructions and achieve high success rates. Therefore, we chose prompts that are as simple and as universal as possible and do not make references to functionality in specific MCP servers.
Context window
The context window may be exceeded in long tasks. Agents are consuming full pages as they navigate the web and as a result the limited context window of LLMs is sooner or later exceeded. Therefore, to build agents that complete tasks that involve many pages, users need
- LLMs with large context windows
- Optimize the sizes of the pages passed to the LLM. For example, you may be able to programmatically remove unnecessary parts of pages and have LLM focus only on the important parts of the pages.
Developer experience
Since MCPs are relatively new, using them without well-known frameworks like Claude Desktop or Cursor might be challenging for developers due to the lack of documentation available on the Internet. However, using it on these platforms does not require any coding, and by following our instructions, you can configure your Claude Desktop to use the MCP you need.
FAQ
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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