๐ฅ reShapr vs Official GitHub MCP: What happens when you optimize tool calls for AI agents? In our latest benchmark, we put the official @github.com Model Context Protocol (MCP) tools head-to-head with a purpose-built reShapr action to measure pull request velocity on microsoft/vscode. The results?
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reshapr.ioreShapr vs Official GitHub MCP ๐ฅ | reShaprA live, curl-reproducible benchmark showing how reShapr turns a 31-call GitHub MCP pull-request workflow into one predictable agent action, then trims the response further with output filters andโฆ
โ Official @github.com MCP: 31 separate tool calls (1 list + 30 follow-ups) โ reShapr: 1 predictable, task-shaped agent action By shaping the API exactly to the agent's needs, we eliminated unnecessary API loops, resulting in a ๐ต๐ณ.๐ฎ% ๐ฟ๐ฒ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป ๐ถ๐ป ๐ฟ๐ฒ๐๐ฝ๐ผ๐ป๐๐ฒ ๐ฏ๐๐๐ฒ๐ and a drastic improvement in latency.
reshapr.ioreShapr vs Official GitHub MCP ๐ฅ | reShaprA live, curl-reproducible benchmark showing how reShapr turns a 31-call GitHub MCP pull-request workflow into one predictable agent action, then trims the response further with output filters andโฆ
Stop context overload and make your AI agents predictable and reproducible. Check out the full benchmark and methodology here: ๐ reshapr.io/blog/reshapr... #AI #AgenticAI #MCP #GitHub #LLM @aaif.io @linuxfoundation.org ๐
reshapr.ioreShapr vs Official GitHub MCP ๐ฅ | reShaprA live, curl-reproducible benchmark showing how reShapr turns a 31-call GitHub MCP pull-request workflow into one predictable agent action, then trims the response further with output filters andโฆ
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