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Route AI responses by confidence and delegate generation to local models.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-confidence" yet — see the docs or source repo.
Connect this QA agent to mcp-confidence: reply directly when confidence is high, mark for verification when confidence is medium, and hand off to a human when confidence is low. Suggest routing thresholds and the response format.
A routing policy with high, medium, and low confidence thresholds, plus example output structures for accept, verify, and ask-a-human.
Design a workflow for my app: use the primary model first; if more controllable generation is needed, delegate through mcp-confidence's MCP server to a local model, while preserving confidence bands and audit logs.
A workflow showing how the primary and local models cooperate, including delegation conditions, confidence bands, log fields, and error-handling guidance.
Use mcp-confidence to create a reliability evaluation plan for a set of model answers, classifying them into accept, verify, and ask-a-human based on token logprobs, and explain how to calibrate thresholds during testing.
A reliability evaluation and testing plan with classification criteria, threshold calibration methods, suggested metrics, and an example result format.
Build, debug, and manage software tasks with natural language across LLMs.
Verify agent identities, check trust scores, and build cross-platform reputation.
Delegate low-risk tasks to a cheaper model with main-agent review.
Access multiple AI providers in one terminal for generation, search, and comparison.
Enable AI agents to communicate, route messages, and collaborate through MCP.
Route prompts across LLM providers with policy-based orchestration and verification.