Model Context Protocol
Model Context Protocol connects AI applications to outside tools and data. Hosts use clients to talk with servers offering tools, resources, and prompts. It is open, and both sides first agree on available capabilities.
Another example
A desktop AI host connects to a local design server and a remote issue server. It asks permission before writes and shares only needed context.
Main parts
- 01Host
- 02Client connection
- 03Server capabilities
Use it when
Use MCP when AI applications need discoverable tools across systems. Define connection life, transport, permission, and isolation.
Do not use it when
Do not trust every server, expose whole chats, skip capability checks, or treat connectivity as permission.
Name used in code
host ↔ client ↔ server; tools/resources/prompts Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Model Context Protocol to make AI behavior measurable and tied to evidence. User context: A desktop AI host connects to a local design server and a remote issue server. It asks permission before writes and shares only needed context. AI method or concept: Model Context Protocol. Why it fits: Use MCP when AI applications need discoverable tools across systems. Define connection life, transport, permission, and isolation. Do not use it when: Do not trust every server, expose whole chats, skip capability checks, or treat connectivity as permission. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): A formal document may still be a draft, release candidate, or final version. Check its status and browser or platform support. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Model Context Protocol. Definition: Model Context Protocol connects AI applications to outside tools and data. Hosts use clients to talk with servers offering tools, resources, and prompts. It is open, and both sides first agree on available capabilities. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.