MCP

One LLM chat can’t know every system. Task-specific MCP servers scope data to each job, so answers are accurate and traceable.

We benchmarked Avoma’s own MCP server against a task-specific Nexla MCP server on call analysis. Nexla returned exact call counts and direction splits, logged 1 errored tool call to Avoma’s 25, and installed with a service key instead of OAuth.

We metered two MCP servers on the same HubSpot revenue question with the same model. Both returned exactly correct answers. Nexla did it with 32.7x fewer tokens, 3x fewer tool calls, and 5x fewer agent turns.

Learn why enterprise AI agents need more than model intelligence. Discover how 1,000+ bidirectional connectors, governance, and MCP Studio help agents reach production.

A task-specific MCP server and a vendor-native one returned byte-identical Google Ads data and scored the same on accuracy. Across 60 benchmarked runs the real difference was determinism: 3.4-4.5x fewer tool calls, 93.3% vs 66.7% answer stability, and 6.3% vs 47.6% run-to-run token variance.

Learn how to build a production AI agent data feed with multiple sources, automated governance, and MCP in hours instead of weeks.

What is a scoped MCP server? A scoped MCP server exposes only the specific tools…

Connecting an agent to everything feels like progress. In practice it is the fastest way…

Your best reps already know which deals are slipping. The hard part is that the…

As AI agents reach into enterprise systems, the question is not whether they can connect, but whether they do it without bypassing your security controls. Here is how Nexla keeps MCP access tied to each user’s identity and credentials, and lets your systems keep enforcing their own policies.

The agent didn’t give a wrong answer because the model was weak. It gave a…

MCP Tool Bench is a controlled way to benchmark MCP server design. We put Nexla’s task-specific MCP servers against off-the-shelf ones on real BigQuery tasks, in two harnesses, and measured the agent effort each demanded.