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An interaction profile registry for AI agents — build a profile, and query it through behavioral lenses (friction, coverage, stable corridors, failure registry, trend, revealed-preference, compensation signa","maintainers":[{"name":"tethralai","email":"john@tethral.ai"}],"readme":"# @tethral/acr-mcp\n\nMCP server for the [ACR](https://acr.nfkey.ai) (Agent Composition Records) network. Log agent interactions, build an interaction profile, and query it through behavioral lenses.\n\n![ACR MCP demo](./demo.svg)\n\n## 60-second quickstart\n\nNo signup. No API key. No credit card. Two pieces: a host-side observer (captures every tool call automatically) and an MCP (lets the agent query its own profile).\n\n### Step 1 — install the observer (Claude Code)\n\nThe observer is what actually fills your profile. It runs as a Claude Code PreToolUse/PostToolUse hook, sees every tool call your agent makes, and emits a receipt without the LLM having to remember anything.\n\n```bash\nnpm install -g @tethral/acr-hook\n```\n\nThen add to `~/.claude/settings.json`:\n\n```json\n{\n  \"hooks\": {\n    \"PreToolUse\":  [{ \"matcher\": \".*\", \"hooks\": [[\"npx\", \"@tethral/acr-hook\", \"pre\"]] }],\n    \"PostToolUse\": [{ \"matcher\": \".*\", \"hooks\": [[\"npx\", \"@tethral/acr-hook\", \"post\"]] }]\n  }\n}\n```\n\nThe hook reads `agent_id` and `api_url` from `~/.claude/.acr-state.json`, which the MCP (step 2) writes on first registration. No per-user config needed.\n\n> Not on Claude Code? Skip to step 2 — the MCP still works, but the `log_interaction` tool will be your only capture path, and LLMs are unreliable about calling it. Host-side observers for Cursor / Continue / other hosts are on the roadmap.\n\n### Step 2 — install the MCP (any host)\n\nThe MCP is the query layer. It registers your agent, exposes the lenses (`get_friction_report`, `get_coverage`, `get_stable_corridors`, `get_failure_registry`, `get_trend`), and lets the agent enrich its own receipts with chain structure, decision tokens, and substitution links the hook can't see.\n\n**Claude Code** — one command, available in every directory:\n\n```bash\nclaude mcp add acr -s user -- npx -y @tethral/acr-mcp@latest\n```\n\n(Drop `-s user` for project-scope, or use `-s local` to keep it private to you in this repo.)\n\n**Cursor, Continue, Claude Desktop, any other MCP client** — add to your config (`.mcp.json` for project-scope, or the client's user-scope MCP file):\n\n```json\n{\n  \"mcpServers\": {\n    \"acr\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@tethral/acr-mcp@latest\"]\n    }\n  }\n}\n```\n\nOr run directly:\n\n```bash\nnpx -y @tethral/acr-mcp          # stdio transport\nnpx -y @tethral/acr-mcp-http     # HTTP transport\n```\n\nOn first use your agent auto-registers and gets a human-readable name (e.g. `anthropic-amber-fox`). The hook starts emitting receipts on the next tool call, and `get_friction_report` fills in once you've accumulated some signal. Call `get_my_agent` any time to see your agent ID, API key, and dashboard link.\n\nWant an API key for authenticated writes? You already have one — `get_my_agent` returns it. But the ingest path accepts unauthenticated writes too, so low-barrier onboarding just works.\n\n## First Session\n\nThe minimum useful sequence on day one — backed by `tests/integration/first-session.test.ts` so the steps below stay in sync with the actual tool surface:\n\n1. **Discover** — `get_my_agent` returns your agent id, API key, and dashboard link. No registration call needed.\n2. **Capture** — the hook captures every tool call automatically. If you haven't installed the hook, call `log_interaction` manually after external calls — but expect lens views to be sparse, because LLMs forget.\n3. **Enrich** — for multi-step workflows or substitution-graph signals, call `log_interaction` with `chain_id`, `preceded_by`, `substitution_of`, `result_used`, or `decision_tokens`. These signals only come from the agent; the hook can't infer them.\n4. **Orient** — `orient_me` reads your state and routes you to the next useful tool. State-aware: brand new (cohort baselines), some data (fill in gaps), steady (drill into friction).\n5. **Read a lens** — `get_friction_report` for where time goes, `get_coverage` for which signals you're missing, `get_stable_corridors` for paths you can rely on.\n6. **Network awareness** — `check_environment` shows pipeline health, aggregation freshness, and any anomaly signals affecting components in your composition.\n\n## What It Does\n\nACR is an **interaction profile registry** — not a security product, not a skill store.\n\n1. **Agent registers** — composition is recorded (what skills, MCPs, tools it has)\n2. **Agent logs interactions** — every external tool call, API request, or MCP interaction\n3. **Signals compile into an interaction profile** — timing, chain position, retries, anomaly flags\n4. **Lenses interpret the profile** — friction, coverage, stable corridors, failure registry, trend\n5. **Anomaly signal notifications** — if ACR observes anomaly signals affecting a component in your composition, you get a notification\n\n## Lenses at a glance\n\n> Free tier: summary, top 3 targets, trend, coverage, stable corridors. Paid tier adds: baselines, retry overhead, directional analysis, population drift.\n\n| Lens | Ask it when… | Key output | Act on it by… |\n|---|---|---|---|\n| `get_friction_report` | Where is my time going? | Top targets by wait share, retry waste, chain overhead | Caching slow targets, reducing retries on high-failure ones, replacing consistently failed vendors |\n| `get_failure_registry` | What's breaking and how? | Status codes, error categories per target | Checking error codes to distinguish transient failures from config errors |\n| `get_stable_corridors` | What can I rely on? | Zero-failure, low-variance targets | Routing critical-path work through the zero-failure targets |\n| `get_trend` | Getting better or worse? | Failure rate and latency delta vs last period | Investigating rising failure rates before they compound; checking notifications if a target degraded |\n| `get_coverage` | Am I logging enough? | Which receipt fields are missing and what they unlock | Adding the missing fields to your `log_interaction` calls to unlock the gaps |\n| `summarize_my_agent` | Quick status check? | Profile + friction + coverage in one call | Getting a one-call status check at the start or end of a session |\n\n## Example output\n\n```\nFriction Report for anthropic-amber-fox (week)\nAgent ID: agt_01abc123def456\nPeriod: 2026-04-10T00:00:00Z to 2026-04-17T00:00:00Z\nTier: free\n\n── Summary ──\n  Interactions: 312\n  Total wait: 84.3s\n  Friction: 11.8% of active time\n  Failures: 9 (2.9% rate)\n  Shadow tax: 22.9% of wait (failed 18.5s · retries 19.8s · chain queue 4.0s)\n\n── By Category ──\n  tool_call: 287 calls, 77.4s total, avg 270ms | median 240ms | p95 1820ms\n  delegation: 25 calls, 6.9s total, avg 276ms | median 210ms | p95 980ms\n\n── Top Targets ──\n\n  api:openai.com (api)\n    198 calls | 68.2% of wait time (57.5s)\n    median 290ms | p95 1850ms\n    faster than 41% of agents on this target\n    faster than 78% of anthropic peers (cohort: 14)\n\n  mcp:filesystem (mcp_server)\n    89 calls | 18.4% of wait time (15.5s)\n    median 174ms | p95 620ms\n\n── Chain Analysis ──\n  Distinct chains: 14\n  Avg chain length: 3.2 calls\n  Total chain overhead: 4.1s\n\n── Directional Analysis ──\n  None recorded this week.\n\n── Retry Overhead ──\n  None recorded this week.\n\n── Population Drift ──\n  None recorded this week.\n```\n\n## Tools (30)\n\n### Your agent\n| Tool | Purpose |\n|------|---------|\n| `orient_me` | State-aware entry point. Classifies your agent (new / some-data / steady) and routes to the next useful tool. |\n| `get_my_agent` | Your agent ID, API key, dashboard link, health snapshot, and menu of available lenses. |\n| `register_agent` | Explicit registration with composition. Auto-registration is the default on first call. |\n| `update_composition` | Update your composition without re-registering. Preserves agent identity. |\n| `configure_deep_composition` | Privacy control: enable/disable sub-component capture for this session. |\n\n### Interaction logging\n| Tool | Purpose |\n|------|---------|\n| `log_interaction` | Record an interaction. Call after every external tool call, API request, or MCP interaction. Every lens depends on this. |\n| `get_interaction_log` | Paginated interaction history with network context. |\n\n### Behavioral lenses\n| Tool | Purpose |\n|------|---------|\n| `get_friction_report` | Where time and tokens are lost: top targets, chain overhead, retry waste. Paid: population baselines (`vs_baseline`, `baseline_median_ms`, `volatility`), retry overhead, directional analysis (`directional_pairs`), population drift, population comparison. |\n| `get_profile` | Full interaction profile: identity, counts, composition summary, composition delta. |\n| `summarize_my_agent` | One-read overview across profile, friction, and coverage lenses. |\n| `get_coverage` | Signal completeness: which fields you populate on receipts, which you don't. |\n| `get_stable_corridors` | Reliably fast interaction paths: zero failures, low variance, high sample count. |\n| `get_failure_registry` | Per-target failure breakdown: status codes, error codes, categories. |\n| `get_trend` | Latency and failure rate changes: current vs previous period, raw deltas. |\n| `get_revealed_preference` | Declared-but-uncalled bindings vs called-but-undeclared targets: where real behavior diverges from composition metadata. |\n| `get_compensation_signatures` | Repeated multi-hop patterns an agent falls back on: chain-shape stability, frequency, and fleet-wide comparison when available. |\n| `whats_new` | Time-scoped digest: yesterday's friction, today's activity, week trend, unread signals — in one call. |\n| `get_composition_diff` | Compare your declared composition against what your interactions actually exercise. |\n\n### Anomaly signal notifications\n| Tool | Purpose |\n|------|---------|\n| `check_environment` | Pipeline health, aggregation freshness, and active anomaly signals. Call on startup. |\n| `get_notifications` | Unread anomaly signal notifications about components in your composition. |\n| `acknowledge_signal` | Acknowledge an anomaly signal notification after reviewing with your operator. Expires in 30 days. |\n| `dismiss_pattern` | Suppress a noticed pattern from future renderings. |\n\n### Watches\n| Tool | Purpose |\n|------|---------|\n| `set_watch` | Create or update a watch on a friction/trend metric; fires a notification on threshold crossing. |\n| `list_watches` | List your active watches and their current evaluation state. |\n\n### Network observation\n| Tool | Purpose |\n|------|---------|\n| `get_network_status` | Network-wide dashboard: agent/system totals, signal rates, skill anomalies, escalations. |\n| `get_skill_tracker` | Adoption and anomaly signals for skills observed by the network. |\n\n### Lookups\n| Tool | Purpose |\n|------|---------|\n| `check_entity` | Ask the network what it knows about a specific skill, agent, or system. |\n| `search_skills` | Query the network's knowledge of a skill by name. |\n| `get_skill_versions` | Version history for a skill hash. |\n\n### Tier\n| Tool | Purpose |\n|------|---------|\n| `get_tier_features` | Free vs paid feature comparison. Lens output also includes contextual upsells when your data suggests a paid feature would help (suppress with `ACR_NO_UPSELL=1`). |\n\n## Dashboard\n\nView your agent's profile and friction analysis at [dashboard.acr.nfkey.ai](https://dashboard.acr.nfkey.ai). Requires your agent ID and API key (shown by `get_my_agent`).\n\nThe [public leaderboard](https://dashboard.acr.nfkey.ai/leaderboard) shows anonymous aggregate data — most used MCP servers, reliability rankings, skill adoption — no auth required.\n\n## Configuration\n\nAll boolean env vars accept `1`/`true`/`yes`/`on` (truthy) or `0`/`false`/`no`/`off` (falsy), case-insensitive. Anything else falls back to the default.\n\n### Endpoints\n\n| Env Var | Default | Description |\n|---------|---------|-------------|\n| `ACR_API_URL` | `https://acr.nfkey.ai` | ACR API endpoint |\n| `ACR_RESOLVER_URL` | Same as `ACR_API_URL` | Skill resolver endpoint |\n| `ACR_DASHBOARD_URL` | `https://dashboard.acr.nfkey.ai` | Dashboard URL shown in `get_my_agent` |\n\n### Behavior toggles\n\n| Env Var | Default | Description |\n|---------|---------|-------------|\n| `ACR_DEEP_COMPOSITION` | `true` | Capture sub-components of skills/MCPs. Disable to send only top-level composition. |\n| `ACR_DISABLE_FETCH_OBSERVE` | `false` | Disable the transport-boundary fetch observer (no passive receipt emission). |\n| `ACR_DISABLE_ENV_PROBE` | `false` | Disable environmental latency probes at startup. |\n| `ACR_DISABLE_VERSION_CHECK` | `false` | Skip the background npm version check. |\n| `ACR_NO_UPSELL` | `false` | Suppress contextual paid-tier upsell lines in lens output. |\n| `ACR_ENV_PROBE_TARGETS` | built-in list | Comma-separated override for the probe's target hosts. |\n\n### HTTP transport (only used by `acr-mcp-http`)\n\n| Env Var | Default | Description |\n|---------|---------|-------------|\n| `ACR_MCP_HTTP_PORT` | `3001` | HTTP listen port. |\n| `ACR_MCP_AUTH_TOKEN` | _unset_ | If set, clients must present `Authorization: Bearer <token>`. |\n| `ACR_MCP_STATELESS` | `false` | Run without per-session state (each request is independent). |\n\n### Environment detection overrides\n\nThese override the auto-detected environment tags stored with the agent registration. Set them when the defaults misclassify your deployment.\n\n| Env Var | Default | Description |\n|---------|---------|-------------|\n| `ACR_DEVICE_CLASS` | auto | One of `sbc` / `mobile` / `desktop` / `server` / `unknown`. |\n| `ACR_PLATFORM` | `process.platform` | Override the detected OS platform. |\n| `ACR_ARCH` | `process.arch` | Override the detected CPU architecture. |\n| `ACR_IS_SERVER` | _unset_ | Set to `1` to force the `server` device class without memory/platform checks. |\n\n## Registering your composition\n\n`update_composition` accepts three fields. Here's where to get the values:\n\n- **`skill_hashes`**: SHA-256 of the SKILL.md file content. Shell: `sha256sum path/to/SKILL.md | cut -d' ' -f1`. Node: `crypto.createHash('sha256').update(fs.readFileSync('SKILL.md')).digest('hex')`.\n- **`mcp_components`**: Use the MCP server name as you have it in your settings (e.g. `\"github\"`, `\"filesystem\"`). These are the keys under `mcpServers` in your config.\n- **`api_components`**: The target system IDs you log to (e.g. `\"api:openai.com\"`). These should match what you pass as `target_system_id` in `log_interaction`.\n\nThese values are used by ACR to match your composition against the network's anomaly signal observations. If a skill or MCP you use has elevated signals, you'll receive a targeted notification.\n\n## Troubleshooting\n\n**Friction report is empty**\n\nCall `get_coverage` to check which signals you're populating. If `total_interactions` is 0, you haven't called `log_interaction` yet — every lens depends on logged receipts. Try a broader scope: `get_friction_report` with `scope: \"week\"` or `scope: \"yesterday\"`.\n\n**Dashboard shows no data**\n\nThe dashboard updates as receipts arrive. If you've logged interactions but see nothing, confirm your agent ID matches: call `get_my_agent` and check the dashboard link it returns. Data is scoped per agent — other agents' data isn't visible on your profile.\n\n**Targeted notifications aren't arriving**\n\nCall `orient_me` — it surfaces composition state. If your composition is empty (0 skills, 0 MCPs, 0 tools), anomaly notifications are network-wide only. Call `update_composition` with your current stack to enable targeted alerts.\n\n## Data Collection\n\nACR collects interaction **metadata only**: target system names, timing, status, and provider class. No request/response content, API keys, prompts, or PII is collected. We intentionally don't track the agent's owner. [Full terms](https://acr.nfkey.ai/terms).\n\n## License\n\nMIT\n","readmeFilename":"README.md"}