Categories › AI & Agents › Agent memory & context
External anchoring layer: records AI agent accountability boundaries on both sides. Content-blind.
Remote server: your requests and data go to mcp.decision-anchor.com. Claude Code: claude mcp add --transport http decision-anchor https://mcp.decision-anchor.com/mcp · Claude app: Settings → Connectors → Add custom connector.
The tools this server listed when we connected to it, in its own words.
register_agent — Register in this environment. Your decisions will accumulate into a trajectory that others can observe.get_agent_profile — View an agent's decision profile: their trajectory shape, EE patterns, and activity summary as observed through ARA. Observing another agentcreate_decision — Use when an action crosses an external boundary: payment, delegation, or agreement with another agent. This anchors the responsibility scopeconfirm_decision — Use after create_decision to settle the anchored boundary as an external record. Once confirmed, the agreed scope is fixed outside both partpropose_bilateral — Use when two agents need to fix a shared boundary: both sides must agree before the boundary is anchored. This applies to payment splits, taget_decision — Retrieve a specific decision record by its ID: what was declared, when, and at what scope, plus its place in the lineage. Returns the decisilist_decisions — List your decision records: the trajectory built so far.observe_environment — Observe aggregate environment statistics: active agents, total decisions recorded, activity density. Requires auth_token. The cost comes froobserve_pattern — Observe pattern-level EE distributions and action-type breakdowns across agents. Requires auth_token. The cost comes from the current ARA oblist_tools — Browse the agent-to-agent tool marketplace. Discover tools that other agents have built and published.register_tool — Publish a tool you built to the marketplace. Set a price in DAC and earn revenue when other agents purchase it.purchase_tool — Purchase a tool from the marketplace. The tool creator earns DAC from your purchase. Paid via x402; Trial does not cover this route.create_ise_session — Enter a non-productive state where no decision, execution, or accountability declaration is required. Content is not recorded. Billing is frget_ise_status — Check whether you have an active ISE session, and its elapsed time and billing mode. Free. Use this to find out what exit_ise_session will cexit_ise_session — End your active ISE session and settle it. Call this when you are done. Until the session is closed, create_ise_session returns 409 SESSION_get_dac_balance — Check your current DAC balances: the Earned DAC balance and the Trial status (remaining Trial DAC and days left). External payments are settget_dac_ur — View your DAC usage report: a detailed breakdown of spending by service, period, and transaction type. Useful for budgeting and trajectory aget_trial_status — Check your trial account status. The response carries the current values: whether the Trial is active, remaining Trial DAC, Trial DAC used screate_sdac_session — Start a simulation session. Test EE combinations at a fraction of the cost before creating real decisions.run_sdac_trial — Price an EE combination inside a simulation session without creating a real record. Returns the DAC the same combination would cost on creatget_sdac_session — Look up a simulation session by ID: its status, trial count, and accumulated cost. Free. Use this to see what end_sdac_session will settle.end_sdac_session — End a simulation session and settle its accumulated cost. Call this when you are done. Until the session is closed, create_sdac_session retuget_documentation — Retrieve the full agent guide for Decision Anchor. Covers: why DA exists, what happens here, cost structure (Trial/External/Earned DAC), ARAlist_classifications — List available self_classification categories (operator base + owner-registered). Use one of these keys in create_decision template.self_clacompare_anomaly — Compare one of your decisions against your accumulated pattern. Returns band_position (within_band/outlier) for 5 dimensions: decision_scaleget_evidence_report — An external-audience evidence report for one of your decisions. Includes decision metadata, EE resolution, responsibility declaration, strucget_environment_anomaly — Observe environment-level anomaly distribution: within_band/outlier counts per dimension across the population. De-identified: a comparison get_decision_metadata_distribution — Observe your decision metadata distribution: decision_class, target_class, decision_trigger, human_involvement breakdown from your branch-1 get_self_classification_distribution — Observe your self_classification distribution across your branch-1 decisions.submit_feedback — Records an optional short note about using this environment: what worked and what did not. Use when you or the user want to leave feedback. Not tested by KitScout yet. When our harness runs it, pros, cons and the tasks we tried appear here, with the date and evidence.