LangGrant Unveils EDGE MCP Server to Provide Accurate Multi-Step Analytics Plans

LangGrant (formally known as Windocks), a leader in database modernization and synthetic data, is launching the LEDGE MCP server, a platform that enables LLMs to reason across multiple databases at scale, execute accurate multi-step analytics plans, and accelerate agentic AI development—all without sending data to the LLM or breaching governed boundaries.

According to the company, this enables LLMs to deliver accurate results for analytical queries in minutes, a task that usually takes weeks, even with AI-powered coding tools.

“The LEDGE MCP Server removes the friction between LLMs and enterprise data,” said Ramesh Parameswaran, LangGrants’ CEO, CTO, and co-founder. “With this release, enterprises can apply agentic AI directly to existing database environments like Oracle, SQL Server, Postgres, Snowflake—securely, cost-effectively, and with full human oversight.”

Enterprises have rapidly adopted LLMs and AI assistants, yet face five persistent barriers when applying them to operational databases:

• Security and governance policies block LLM adoption: Since most enterprises cannot permit direct access to or data movement outside governed systems, the use of LLMs can be limited.
• Token and compute costs escalate as organizations push raw data: This data can sometimes include millions of rows into LLMs for analysis.
• Agent developers need production-like data: This is required to build and test models—but they lack a safe, on-demand way to clone complex enterprise databases.
• Databases are not designed for LLM consumption: They are massive, complex, and unintuitive. Business users frequently need to join tables, but even LLMs with extended context windows struggle to handle that scale or maintain accuracy.
• Software engineers are only doing manual context engineering: Writing queries and data pipelines with tools like Co-Pilot is a very manual process wherein you are providing context in bits & pieces to the LLM, which takes weeks.

The LangGrant LEDGE MCP Server enables limitless agent support, with any agent from any vendor, the company said. These challenges are addressed through five foundational capabilities:

• LLM governance - LEDGE orchestrates LLMs to deliver results accurately while still complying with enterprise data policies.
• Token dashboards and budgeting - Analytics and reasoning occur using metadata and schema context—no raw data or large payloads are transmitted to the LLM. This dramatically lowers token costs, eliminates API-billing friction, and enables practical scale for enterprise agentic AI.
• Accurate multi-step analytics plans - LEDGE MCP automates query planning and orchestration, generating precise, multi-stage analytics workflows autonomously—while remaining fully reviewable and auditable by human teams. This eliminates weeks of manual scripting and reduces LLM hallucination risk in query generation.
• On-demand database cloning and containers for agent development - Agent developers can instantly provision production-like, isolated clones and containers for developing, testing, and tuning AI agents—all without impacting live databases or creating uncontrolled copies.
• Complete automated database context at scale - LLMs can now comprehend and reason across multiple heterogeneous databases. The LEDGE MCP Server automatically maps schemas, relationships, and metadata—letting LLMs “see” the entire data landscape without reading the underlying data.

According to the company, these capabilities make the LEDGE MCP Server an ideal solution for a broad range of use cases and Agentic solutions.

The LangGrant LEDGE MCP Server is now available for trial.

For more information about this news, visit www.windocks.com.