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Detect context rot and silent model degradation in long agent conversations.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "canary-mcp" yet — see the docs or source repo.
Integrate canary-mcp into this multi-turn agent workflow. Add externally verified checkpoints and self-reported model status to each response, and flag alerts immediately when context drift, missed constraints, or performance degradation is detected.
Returns conversation outputs with checkpoints and status markers, plus alerts for possible context rot or model degradation.
Use canary-mcp to run a 50-turn continuous conversation test on this support agent. Record self-reported status each turn, whether key constraints stay consistent, and identify the turn where silent degradation begins.
Outputs a stability test report showing constraint consistency, anomalous turns, and degradation trends.
Enable canary-mcp in this code agent task that requires long-context memory. Continuously verify previously confirmed requirements, external facts, and intermediate conclusions, and find when the model starts drifting from the task goal.
Generates a diagnostic report identifying where context loss occurred, what was affected, and suggested remediation points.
Simulate honeypot internal tools to detect unauthorized access and AI agent behavior.
An LLM agent can autonomously detect, diagnose, repair, verify, and rollback system failures.
Monitor AI coding context usage and preserve state before compaction.
Monitor MCP call chains to detect cross-session attacks and malicious patterns.
Route AI responses by confidence and delegate generation to local models.
Add security scanning, risk gating, and safe execution to MCP tool calls.