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Record accountability boundaries for AI agents through an external anchoring layer.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "decision-anchor-mcp" yet — see the docs or source repo.
Before and after an automated task, use decision-anchor-mcp to record the AI agent's accountability boundaries and decision anchors without storing the task content itself.
A recorded accountability boundary or anchor result serving as an external record around the agent run.
When an AI agent issues an approval recommendation, use decision-anchor-mcp to record accountability boundaries before and after the recommendation for later traceability.
An external anchor record tied to the approval recommendation for accountability and traceability.
Developers or platform teams can place this tool around agent execution to record accountability boundaries. It fits cases where agent behavior needs separate external traceability records.
When multiple AI agents participate in a workflow, this tool can act as a thin adapter to a public HTTP API and help standardize boundary records. It is content-blind and does not depend on the specific task content.
It is an external anchoring layer used to record accountability boundaries on both sides of an AI agent. It is also a thin MCP adapter over the Decision Anchor public HTTP API.
The provided description explicitly says it is content-blind. In other words, it focuses on recording boundaries rather than processing the content itself.
The available information only shows that it works through the Decision Anchor public HTTP API and is used as an MCP adapter. For exact installation steps, configuration, or key requirements, see the source repository.
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