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Runtime Context via MCP

Runtime Verification
For AI Agents

Give Cursor, Claude, and Windsurf real execution data via MCP. AI agents see actual SQL queries, method parameters, and call chains before coding. Then they verify their own changes by comparing before/after traces.

75%
Faster MTTR
3x
AI Precission
62%
Bug Prevention
AI Agent Compatibility
Cursor - AI agent compatible with BitDive MCPCursor
Claude Code - AI agent compatible with BitDive MCPClaude Code
Windsurf - AI agent compatible with BitDive MCPWindsurf
GitHub Copilot - AI agent compatible with BitDive MCPGitHub Copilot
MCP Protocol - AI agent compatible with BitDive MCPMCP Protocol
DeepSeek - AI agent compatible with BitDive MCPDeepSeek
Instruction GroundingAI reasons over real execution facts
Zero-Knowledge MocksAI validates behavior without infra
MCP Runtime ServerLive context feed for all AI Agents
CAPABILITIES

From Hallucination to Precision

Bridging the gap between static code and execution reality.

Root Cause Analysis

AI explores full call chains with inputs/outputs to pinpoint exact issues

AI-Powered Fixes

Give AI tools complete runtime context for precise, relevant code suggestions

Live Context Replay

Re-run real call flows across versions to validate behavior changes

Regression Validation

Ensure behavior matches pre- and post-deployment with actual data

MCP Runtime Feed

Stream live variable states and SQL results directly into LLM prompts

Context-Aware Testing

Create realistic tests that reflect actual system usage patterns

AI Agent Hallucination - Professional developer expressing frustration when AI coding agents fail without Java runtime context
THE GAP

AI Agents are Flying Blind

Static code analysis is not enough.

Tools like Cursor and Claude see your logic, but they can't see your Data State. Without knowing what SQL returned or which branch was taken in production, AI guesses.

  • ✕62% Bug Rate: AI-generated code often misses edge cases.
  • ✕Blind Optimizations: Slower code recommended by LLMs.
  • ✕Detection Delay: Bugs found only after deployment.
JVM Runtime Data via MCP Schematic - Technical diagram showing SQL results, state, and traces feeding into BitDive and then to AI agents
THE PROTOCOL

Real Runtime Data via MCP

Real execution facts streamed to AI agents.

Stream recorded variable states, SQL query results, and external API payloads directly into the AI's reasoning context.

  • ✓Context-Aware: AI sees parameters and return values.
  • ✓Validated Fixes: Verify changes before shipping.
  • ✓No Infrastructure: AI tests logic via replay.
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AI-NATIVE WORKFLOW

The Autonomous Quality Loop

Secure your development lifecycle with deterministic evidence.

01 / CONTEXT

Discovery & Real-Knowledge

AI agents fetch real execution traces via MCP before coding. Analyze live payloads and SQL queries to eliminate hallucinations and build an accurate implementation plan.

02 / BASELINE

Secure the Starting Point

Establish a behavioral baseline. Run existing replay tests (`mvn test`) to confirm current logic is stable. Then capture a baseline trace by triggering the target scenario via a real API call.

03 / MODIFICATION

High-Precision Fixes

Implement changes with context awareness. Because the agent understands the real data flow, modifications are precise, surgical, and significantly less error-prone.

04 / SELF-CHECK

Dual-Trace Inspection

Compare "Before" vs "After" traces. Verify that business rules changed exactly as intended, while ensuring external invariants remained untouched.

05 / VERIFICATION

Universal Regression

Run a global verification cycle. Detect regressions in distant modules instantly. If a fix breaks an unrelated flow, the agent detects it and iterates automatically.

06 / EVIDENTIAL PROOF

Mathematical Verification

Provide Trace Diffs as proof of correctness. Ship with confidence, knowing every logic change is backed by deterministic evidence from real runtime data.

SUPPORT

Frequently Asked Questions

Common questions about the platform.

BitDive provides real runtime data (variable states, SQL results, API payloads) via MCP, so agents like Cursor and Claude can see exactly how the code executes instead of guessing.
We support any tool that implements the Model Context Protocol (MCP), including Cursor, Claude Desktop, and Windsurf.
No. BitDive runs locally or on your private infrastructure. The data is only shared with the AI agent you explicitly connect to via your local MCP client.
Yes. Agents can trigger a "Before vs After" trace comparison to verify that their changes produced the expected behavior and didn't introduce regressions.

Give Your AI Agents Real Runtime Data

Connect Cursor, Claude, or Windsurf to real Java execution traces via MCP.

Book a Demo