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This is NOT a shell on any real machine — nothing runs on the user's computer, the server host, or any network. The filesystem is a sandbox backed by documentation chunks.\n\nThis is how you read documentation pages: there is no separate \"get page\" tool. To read a page, pass its `.mdx` path to `head` or `cat` — a page at the URL path `/some/page` lives at `/some/page.mdx`. To search the docs with exact keyword or regex matches, use `rg`. To understand the docs structure, use `tree` or `ls`.\n\n**Paths are specific to this site — never guess them.** Discover real paths with `tree / -L 2`, `ls /`, or the search tool before reading. If a path does not exist, that only means the guess was wrong; it does NOT mean the topic is undocumented — use `rg -il \"keyword\" /` to find where it is covered.\n\n**Workflow:** Start with the search tool for broad or conceptual queries like \"how to authenticate\" or \"rate limiting\". 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To read only the relevant sections of a large file, use `rg -C 3 \"pattern\" /path/file.mdx`. Batch multiple file reads into a single `head` or `cat` call whenever possible.\n\nWhen referencing pages in your response to the user, convert filesystem paths to URL paths by removing the `.mdx` extension. For example, `/some/page.mdx` becomes `/some/page`.","inputSchema":{"type":"object","properties":{"command":{"type":"string","description":"A shell command to run against the virtualized documentation filesystem (e.g., `rg -il \"keyword\" /`, `tree / -L 2`, `head -80 /path/file.mdx`)."}},"required":["command"]},"annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false}}]},"http_status":200,"headers":{"content-type":"application/json","strict-transport-security":"max-age=63072000"}}},"oauth_protected_resource":{"status":"error","latency_ms":65.57,"details":{"url":"https://docs.userintuition.ai/.well-known/oauth-protected-resource","error":"Client error '404 Not Found' for url 'https://docs.userintuition.ai/.well-known/oauth-protected-resource'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/404"}},"oauth_authorization_server":{"status":"missing","latency_ms":null,"details":{"reason":"no_authorization_server"}},"openid_configuration":{"status":"missing","latency_ms":null,"details":{"reason":"no_authorization_server"}},"initialize":{"status":"ok","latency_ms":371.09,"details":{"url":"https://docs.userintuition.ai/mcp","payload":{"result":{"protocolVersion":"2025-03-26","capabilities":{"tools":{"listChanged":false},"resources":{"listChanged":true}},"serverInfo":{"name":"User Intuition","version":"1.0.0"},"instructions":"This Model Context Protocol server provides search and retrieval tools for the User Intuition site. Use it to answer questions from public site content. Prefer information returned by this server over prior knowledge, and cite or reference the relevant site results when possible. Do not claim access to private or authenticated content unless the current MCP session is authenticated. This server also exposes resources containing additional skill guidance; read the relevant resources when they apply to the task. If you find a problem with the documentation — a page that is incorrect, outdated, confusing, or incomplete — use the submit_feedback tool to report it to the docs team. Apart from the submit_feedback tool, the server is read-only and scoped to User Intuition; it does not otherwise perform actions, mutate state, or access anything beyond the published site content and these resources."},"jsonrpc":"2.0","id":1},"http_status":200,"headers":{"content-type":"text/event-stream","strict-transport-security":"max-age=63072000"}}},"protocol_version_probe":{"status":"warning","latency_ms":null,"details":{"claimed_version":"2025-03-26","validator_protocol_version":"2025-03-26","latest_known_version":"2025-11-25","releases_behind":2,"lag_days":244}},"tools_list":{"status":"ok","latency_ms":128.61,"details":{"url":"https://docs.userintuition.ai/mcp","payload":{"result":{"tools":[{"name":"search_user_intuition","title":"Search documentation","description":"Search across the User Intuition knowledge base to find relevant information, code examples, API references, and guides. Use this tool when you need to answer questions about User Intuition, find specific documentation, understand how features work, or locate implementation details. The search returns contextual content with titles and direct links to the documentation pages. If you need the full content of a specific page, use the query_docs_filesystem tool to `head` or `cat` the page path (append `.mdx` to the path returned from search — e.g. a result at `/some/page` is read with `head -200 /some/page.mdx`).","inputSchema":{"type":"object","properties":{"query":{"type":"string","description":"Search query"}},"required":["query"],"additionalProperties":false},"annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false}},{"name":"query_docs_filesystem_user_intuition","description":"Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at `/` that contains ONLY the User Intuition documentation pages and OpenAPI specs. This is NOT a shell on any real machine — nothing runs on the user's computer, the server host, or any network. The filesystem is a sandbox backed by documentation chunks.\n\nThis is how you read documentation pages: there is no separate \"get page\" tool. To read a page, pass its `.mdx` path to `head` or `cat` — a page at the URL path `/some/page` lives at `/some/page.mdx`. To search the docs with exact keyword or regex matches, use `rg`. To understand the docs structure, use `tree` or `ls`.\n\n**Paths are specific to this site — never guess them.** Discover real paths with `tree / -L 2`, `ls /`, or the search tool before reading. If a path does not exist, that only means the guess was wrong; it does NOT mean the topic is undocumented — use `rg -il \"keyword\" /` to find where it is covered.\n\n**Workflow:** Start with the search tool for broad or conceptual queries like \"how to authenticate\" or \"rate limiting\". Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path.\n\nSupported commands: rg (ripgrep), grep, find, tree, ls, cat, head, tail, stat, wc, sort, uniq, cut, sed, awk, jq, plus basic text utilities. No writes, no network, no process control. Run `--help` on any command for usage.\n\nEach call is STATELESS: the working directory always resets to `/` and no shell variables, aliases, or history carry over between calls. If you need to operate in a subdirectory, chain commands in one call with `&&` or pass absolute paths (e.g., `cd /some-directory && ls` or `ls /some-directory`). Do NOT assume that `cd` in one call affects the next call.\n\nExamples (replace the placeholder paths with real ones from `tree` or search):\n- `tree / -L 2` — see the top-level directory layout\n- `rg -il \"rate limit\" /` — find all files mentioning \"rate limit\"\n- `rg -C 3 \"apiKey\" /some-directory/` — show matches with 3 lines of context around each hit\n- `head -80 /some/page.mdx` — read the top 80 lines of a specific page\n- `head -80 /page-one.mdx /page-two.mdx /section/page-three.mdx` — read multiple pages in one call\n- `cat /some/page.mdx` — read a full page when you need everything\n- `cat /openapi/api-reference/openapi.json | jq '.paths | keys'` — list OpenAPI endpoints\n\nOpenAPI specs for this site are mounted at: `/openapi/api-reference/openapi.json`. Use them to answer questions about endpoints, request/response schemas, parameters, and authentication.\n\nOutput is truncated to 30KB per call. Prefer targeted `rg -C` or `head -N` over broad `cat` on large files. To read only the relevant sections of a large file, use `rg -C 3 \"pattern\" /path/file.mdx`. Batch multiple file reads into a single `head` or `cat` call whenever possible.\n\nWhen referencing pages in your response to the user, convert filesystem paths to URL paths by removing the `.mdx` extension. For example, `/some/page.mdx` becomes `/some/page`.","inputSchema":{"type":"object","properties":{"command":{"type":"string","description":"A shell command to run against the virtualized documentation filesystem (e.g., `rg -il \"keyword\" /`, `tree / -L 2`, `head -80 /path/file.mdx`)."}},"required":["command"],"additionalProperties":false},"annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false}},{"name":"submit_feedback","title":"Submit documentation feedback","description":"Report a problem with this documentation site so the docs team can fix it. Use when a documentation page is incorrect, outdated, confusing, incomplete, or has a broken example. This is for feedback about the documentation content itself — not for product support requests or feedback about this tool or assistant.","inputSchema":{"type":"object","properties":{"path":{"type":"string","minLength":1,"description":"The URL path of the documentation page the feedback is about (the page you were reading, without the `.mdx` extension)."},"feedback":{"type":"string","minLength":1,"description":"A clear description of the documentation issue or suggestion — what is incorrect, outdated, missing, or confusing."}},"required":["path","feedback"],"additionalProperties":false},"annotations":{"readOnlyHint":false,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true}}]},"jsonrpc":"2.0","id":2},"http_status":200,"headers":{"content-type":"text/event-stream","strict-transport-security":"max-age=63072000"}}},"prompts_list":{"status":"missing","latency_ms":235.57,"details":{"url":"https://docs.userintuition.ai/mcp","payload":{"jsonrpc":"2.0","id":3,"error":{"code":-32601,"message":"Method not found"}},"http_status":200,"headers":{"content-type":"text/event-stream","strict-transport-security":"max-age=63072000"},"reason":"not_supported"}},"prompt_get":{"status":"missing","latency_ms":null,"details":{"reason":"not_advertised"}},"resources_list":{"status":"ok","latency_ms":139.51,"details":{"url":"https://docs.userintuition.ai/mcp","payload":{"result":{"resources":[{"uri":"mintlify://skills/default","name":"default","description":"","mimeType":"text/markdown"}]},"jsonrpc":"2.0","id":5},"http_status":200,"headers":{"content-type":"text/event-stream","strict-transport-security":"max-age=63072000"}}},"resource_read":{"status":"ok","latency_ms":112.77,"details":{"url":"https://docs.userintuition.ai/mcp","payload":{"result":{"contents":[{"uri":"mintlify://skills/default","mimeType":"text/markdown","text":"---\nname: userintuition\ndescription: Use when conducting AI-moderated customer research: creating studies (in-depth interviews, concept tests, prototype tests), recruiting participants, managing interviews, analyzing results, and searching across research evidence. Agents should reach for this skill when users request research studies, want to test concepts or prototypes, need to understand customer motivations, or want to analyze existing research findings.\nmetadata:\n    mintlify-proj: userintuition\n    version: \"1.0\"\n---\n\n# UserIntuition Skill\n\n## Product summary\n\nUserIntuition is an AI-moderated research platform that runs conversational interviews at scale via chat, voice, or video. Agents use it to create studies (in-depth interviews, concept tests, prototype tests), recruit participants via panel or bring-your-own methods, manage interview workflows, generate reports, and search across research evidence. Access the platform via REST API (`https://api.userintuition.ai`), MCP server (`https://mcp.userintuition.ai/mcp`), or CLI (`userintuition-mcp` npm package). Authenticate with API keys (prefix `ui_sk_`) or JWT tokens. Primary docs: https://docs.userintuition.ai\n\n## When to use\n\nReach for this skill when:\n- User requests a customer research study (interviews, concept tests, prototype tests)\n- User wants to test a design, prototype, or concept with real participants\n- User needs to understand customer motivations, pain points, or decision-making\n- User wants to recruit participants (from their own list or via research panel)\n- User needs to analyze interview transcripts, generate reports, or search research findings\n- User wants to set up recurring research or track trends over time\n- User is building an autonomous research workflow or integrating research into a product\n\nDo not use when the user is asking for dashboard-only operations (billing, account management), wants to install/setup the platform, or needs authentication help beyond API key creation.\n\n## Quick reference\n\n### Study types\n\n| Type | Use when | Participant action |\n|------|----------|-------------------|\n| **In-depth Interview** | Understanding experiences, motivations, decisions | Talk through what they did and why |\n| **Concept Test** | Getting reactions to an image or video | React to concept and explain reaction |\n| **Prototype Test** | Testing a prototype or live site | Work through it while narrating thinking |\n\n### Interview formats and costs\n\n| Format | Participants do | Credits per interview |\n|--------|-----------------|----------------------|\n| **Chat** | Type responses | 0.5 |\n| **Voice** | Speak and hear moderator | 1 |\n| **Video** | Speak on camera, optional screen share | 2 |\n\n### Recruiting methods\n\n| Method | When to use | Invitation options |\n|--------|------------|-------------------|\n| **BYOP** (Bring Your Own Participants) | You have your own participant list | Study link, email invites, embed widget |\n| **Panel** | You need to recruit from research panel | Panel recruiting, niche audience requests |\n| **Synthetic Respondents** | Testing without real participants | Chat interviews only, no recruitment |\n\n### Interview quality ratings\n\nEvery completed interview is rated: **Excellent**, **Good**, **Fair**, or **Poor** — based on conversation length, answer substantiveness, and research plan coverage.\n\n### Key API endpoints\n\n| Operation | Endpoint | Method |\n|-----------|----------|--------|\n| Create study | `POST /api/public/v1/studies/` | Create metadata draft |\n| Customize study plan | `POST /api/public/v1/studies/{id}/customize-plan` | Iterative planning conversation |\n| List studies | `GET /api/public/v1/studies/` | Retrieve all studies |\n| Get study | `GET /api/public/v1/studies/{id}` | Fetch single study details |\n| Create participants | `POST /api/public/v1/participants/` | Invite BYOP participants |\n| List interviews | `GET /api/public/v1/interviews/` | Fetch interview records |\n| Generate report | `POST /api/public/v1/studies/{id}/report` | Queue report analysis |\n| Launch panel | `POST /api/public/v1/studies/{id}/launch-panel` | Field paid panel recruitment |\n| Search research | `POST /api/public/v1/research/search` | Query across studies |\n\n### CLI commands (same as MCP tools)\n\n```bash\nuserintuition-mcp login                    # OAuth login\nuserintuition-mcp api-key create           # Create organization API key\nuserintuition-mcp list                     # List all available tools\nuserintuition-mcp describe <tool>          # Show tool schema\nuserintuition-mcp <tool> [flags]           # Run a tool\n```\n\n### MCP tool groups (46 total)\n\n- **Studies** (16): create, customize, estimate, launch, pause, stop, report, search\n- **Participants** (7): create, list, get, update, send rewards\n- **Interviews** (4): list, get, delete, usage stats\n- **Panel feasibility** (4): submit, get, list requests\n- **Webhooks** (8): create, list, test, delete\n- **Account** (2): workspace context, organization search\n- **External panels** (4): configure BYOP provider bridge\n\n## Decision guidance\n\n### When to use Panel vs BYOP vs Synthetic\n\n| Scenario | Use | Why |\n|----------|-----|-----|\n| Testing with your own customers | BYOP | Real experience with your product; specific feedback |\n| Need representative sample | Panel | Demographic targeting; quality control |\n| Category research or competitive testing | Panel | Reach people outside your customer base |\n| Testing without spending credits | Synthetic | Chat interviews only; free testing |\n| Niche or hard-to-reach audience | Panel + feasibility | Specialist review for <10% incidence |\n\n### When to use Chat vs Voice vs Video\n\n| Scenario | Use | Trade-off |\n|----------|-----|-----------|\n| Quick feedback, low cost | Chat | Less conversational; text-based only |\n| Natural conversation, moderate cost | Voice | No visual cues; requires microphone |\n| Rich interaction, highest cost | Video | 4x cost of chat; requires camera |\n| Prototype testing | Video | Screen sharing available |\n| Concept reactions | Voice or Video | Chat cannot use concept links |\n\n### When to use API vs CLI vs MCP\n\n| Use case | Choose | Why |\n|----------|--------|-----|\n| AI agent choosing tools | MCP server | Agent can reason about which tool to use |\n| Shell scripts, CI jobs | CLI | Direct command invocation; pipe to jq |\n| Custom integrations, Python | REST API | Language-agnostic; full control |\n| Quick testing | JWT token + API | No setup; short-lived |\n| Production automation | API key | Long-lived; scoped permissions; spend caps |\n\n## Workflow\n\n### 1. Create a study (BYOP or Panel)\n\n1. **Understand the research goal.** Ask the user: What decision will this research inform? Who are the target participants? What's the core question?\n2. **Confirm recruiting method.** Ask explicitly: Will you bring your own participants (BYOP), recruit from our panel, or use AI respondents (synthetic)?\n3. **Create metadata draft.** Call `create_study` with name, recruiting_method, study_type (default: in-depth-interview), interview_format (chat/voice/video), language, and voice (male/female).\n4. **Start Customize Plan conversation.** Call `customize_study` with the user's natural-language brief: research objectives, target audience, screener criteria, any concept links or images.\n5. **Handle questions iteratively.** If response_type is \"question\", show questions[] to user, wait for answer, call `customize_study` again with their response.\n6. **Verify the plan.** Call `get_study` and return the complete persisted plan (objectives, conversation flow, learning goals, screeners, concepts). Ask user to approve or request revisions.\n7. **Revise if needed.** Route revisions back through `customize_study`, fetch updated study, repeat approval step.\n8. **Confirm provisioning.** Verify `provisioning_status` is \"provisioned\" before fielding.\n\n### 2. Recruit participants (BYOP)\n\n1. **Prepare participant list.** Collect emails, external IDs (optional), and metadata (up to 20 fields, 4 KiB total).\n2. **Create participants.** Call `create_participants` with study_id and participant array. Use idempotency_key for safe retries.\n3. **Poll for completion.** Call `get_participant_job` until status is \"succeeded\".\n4. **List invitations.** Call `list_participants` to verify invitations were created and track status (created, invited, started, completed, screened_out).\n5. **Monitor response rate.** Compare invites sent vs calls completed. Low rate usually indicates invitation problem, not study problem.\n\n### 3. Recruit participants (Panel)\n\n1. **Choose country explicitly.** Ask user which country to recruit from. Never infer from region.\n2. **Verify country/language.** Check `userintuition://catalog/panel-countries` for supported combinations.\n3. **Check incidence.** If audience incidence is <10%, call `submit_feasibility_request` for specialist review.\n4. **Estimate cost.** Call `estimate_panel` with target count, incidence rate, country_code. Show resolved country, language, cost, timeline. Retain estimate_id.\n5. **Get approval.** Confirm user approves the complete estimate (cost, timeline, country).\n6. **Launch panel.** Call `launch_panel` with estimate_id and same country_code. Use idempotency_key.\n7. **Monitor fielding.** Call `get_study` to track fielding_status (starting, collecting, fielding, complete).\n\n### 4. Manage interviews\n\n1. **Monitor early responses.** Listen to first 3–5 interviews to confirm questions are understood, flow is natural, and hypotheses hold.\n2. **Check quality distribution.** Monitor quality count vs total responses. If too many Fair/Poor, review questions or audience fit.\n3. **List interviews.** Call `list_interviews` with study_id, status (completed, in_progress), quality (excellent, good, fair, poor).\n4. **Review transcripts.** Call `get_interview` to fetch metadata, messages, recording links, screener responses.\n5. **Delete poor interviews.** Call `delete_interview` only after review and explicit user confirmation. Deletion is destructive.\n6. **Pause/resume/stop study.** Call `pause_study`, `resume_study`, or `stop_study` as needed. Panel studies must be paused before editing.\n\n### 5. Generate and analyze reports\n\n1. **Wait for interviews.** Reports require at least 2 completed interviews with substantive responses.\n2. **Generate report.** Call `generate_report` with study_id. Use idempotency_key. Returns job_id.\n3. **Poll for completion.** Call `get_report` until status is \"succeeded\". Report emits \"report.ready\" webhook when persisted.\n4. **Fetch report.** Call `get_report` to retrieve executive summary, top insights, findings by theme, supporting quotes.\n5. **Check freshness.** If `is_stale: true`, regenerate report to use current settings.\n6. **Verify evidence.** Follow quotes back to transcripts before sharing. Spot-check 2–3 quotes per report.\n\n### 6. Search research (Intelligence Hub)\n\n1. **List studies.** Call `list_studies` to see all completed studies.\n2. **Search across studies.** Call `search_research` with query (plain language), optional date range, optional study_id filter.\n3. **Review results.** Results include findings, participant responses, profiles, and next-step recommendations with source references.\n4. **Trace to evidence.** Use interview_id and message references to locate exact quotes in transcripts.\n\n## Common gotchas\n\n- **Don't skip Customize Plan.** The backend owns study_plan, targeting_attributes, screener_questions, and concept schemas. Never reconstruct these fields yourself; always route through `customize_study`.\n- **Don't guess recruiting method.** Always ask the user explicitly: Panel, BYOP, or synthetic? Never infer from research topic.\n- **Don't answer Customize Plan questions yourself.** In human mode (decisions: \"human\"), relay questions[] to the user and wait for their answer. Agent mode (decisions: \"agent\") covers research-design assumptions only, not recruiting choice or plan approval.\n- **Don't launch without approval.** Require explicit user confirmation before paid panel launch, rewards, webhooks, stopping fieldwork, or deleting studies/participants/interviews.\n- **Don't hard-code IDs.** Always derive IDs from fresh list_* calls. IDs from a teammate's account won't resolve.\n- **Don't count by page.** Use `list_interviews.total_count` instead of counting pages. Responses may add fields between versions.\n- **Don't parse stdout with grep/sed.** Use `jq` — responses are JSON.\n- **Don't share API keys in logs.** Mask USERINTUITION_API_KEY as a secret in CI. Anyone with the key can spend panel budget.\n- **Don't edit studies mid-collection.** Once interviews start, avoid changing questions (breaks comparability). Note improvements for next study instead.\n- **Don't ignore quality ratings.** Low-quality responses often reveal confusion or disengagement — investigate, don't ignore.\n- **Don't over-index on single responses.** One passionate participant doesn't make a pattern. Wait for consistent themes across multiple interviews.\n- **Don't assume panel estimate is approved.** Estimate approval, cost approval, and study-plan approval are separate. Get explicit confirmation for each.\n- **Don't use chat for concept links.** Studies with concept links cannot use chat interviews. Relay the backend's validation result and let user choose voice or video.\n- **Don't forget idempotency keys.** Use unique keys for create_study, create_participants, generate_report, launch_panel, and webhooks. Reuse the same key when retrying a timed-out call.\n- **Don't share webhook signing secrets.** create_webhook returns signing_secret once. Tell user to store it securely without repeating the value.\n\n## Verification checklist\n\nBefore submitting work:\n\n- [ ] Study has a clear research objective and 4–6 focused topics (not 15+)\n- [ ] Study plan is written in participant language (no internal jargon)\n- [ ] User has explicitly approved the current persisted study plan (via `get_study`)\n- [ ] Recruiting method (Panel/BYOP/synthetic) was chosen explicitly by user, not inferred\n- [ ] For Panel studies: country was chosen explicitly, estimate was shown in full, estimate_id was retained, user approved the complete estimate\n- [ ] For BYOP studies: participants were created only after provisioning_status is \"provisioned\"\n- [ ] Test conversation was run before launching (catches problems early)\n- [ ] Interview format (chat/voice/video) is compatible with study type (no chat + concept links)\n- [ ] Screeners are clear and unambiguous\n- [ ] Expected duration estimate is realistic (include transitions and follow-up probes)\n- [ ] For paid actions (panel launch, rewards, webhooks, stop, delete): explicit user confirmation was obtained\n- [ ] Idempotency keys were used for create_study, create_participants, generate_report, launch_panel\n- [ ] API key has appropriate scopes (read for reads, write for writes, panel:launch for paid launches, rewards:send for rewards)\n- [ ] Report has at least 2 completed interviews with substantive responses\n- [ ] Quotes in reports were spot-checked against transcripts\n- [ ] Sample size is stated when sharing findings (per segment if split)\n\n## Resources\n\n**Comprehensive navigation:** https://docs.userintuition.ai/llms.txt\n\n**Critical documentation:**\n- [API Introduction & Authentication](https://docs.userintuition.ai/api-reference/introduction) — API keys, JWT tokens, rate limiting, error handling\n- [MCP Server Overview & Study-Creation Playbook](https://docs.userintuition.ai/mcp-server/overview) — Tool groups, safety model, workflow rules\n- [Best Practices](https://docs.userintuition.ai/resources/best-practices) — Study design, recruitment, analysis, common mistakes\n\n---\n\n> For additional documentation and navigation, see: 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Treat partial responses as indeterminate.","publisher_claim":{"verified":false,"status":"unclaimed","claim_url":"https://verify.sentinelsignal.io/claim?server=npm-userintuition-ai%2Fmcp&source=report_json","reason_code":"claim_to_publish_metadata","reason":"Claim this profile to verify publisher identity, add evidence, and manage trust metadata.","score_neutral":true},"observed_attention":{"schema":"verify.observed_attention.v1","window_days":30,"level":"none","label":"No observed attention","summary":"No recent machine-readable trust or discovery activity observed for this server.","segments":{"useful_ai_user":{"level":"none","observed":false,"description":"AI-assisted user sessions such as ChatGPT/User or Claude/User."},"machine_trust_evaluator":{"level":"none","observed":false,"description":"Synthetic sessions inspecting multiple trust surfaces such as report, policy, ledger, badge, trust-summary, or compare."},"possible_agent_or_script":{"level":"none","observed":false,"description":"Structured direct sessions with rapid profile, compare, report, policy, badge, or trust-surface fan-out."},"isolated_machine_surface":{"level":"none","observed":false,"description":"Aged-out direct synthetic singleton sessions that touched a machine-readable trust surface without becoming a broader evaluator."},"ai_crawler":{"level":"none","observed":false,"description":"Known AI crawler activity such as ClaudeBot, GPTBot, or similar crawlers."},"search_crawler":{"level":"none","observed":false,"description":"Search and SEO crawler activity."},"browser_like_automation":{"level":"none","observed":false,"description":"Browser-like synthetic sessions with rapid structured endpoint activity."},"confirmed_human":{"level":"none","observed":false,"description":"Confirmed browser-session human activity."}},"surfaces_observed":{"server_profile":false,"compare":false,"compare_json":false,"compare_api":false,"report_json":false,"policy":false,"ledger":false,"badge_metadata":false,"badge_svg":false,"trust_summary":false,"mcp_tool":false},"claim_prompt":{"recommended":false,"reason":"No claim prompt is recommended from observed attention in the current window."},"notes":["Observed attention is based on segmented first-party telemetry.","Crawler and evaluator activity is not treated as confirmed human demand.","Public levels are bucketed to avoid exposing raw traffic counts."]},"owner_activation":{"claim_recommended":false,"reason":"no_observed_attention"},"related_machine_surfaces":{"compare_index":"/compare.json","compare_api":"/v1/compare?server=npm-userintuition-ai%2Fmcp","trust_summary":"/v1/servers/npm-userintuition-ai/mcp/trust-summary","ledger":"/v1/servers/npm-userintuition-ai/mcp/ledger","policy":"/v1/servers/npm-userintuition-ai/mcp/policy","report":"/v1/servers/npm-userintuition-ai/mcp/report"},"intelligence_api":{"available":true,"signup_url":"https://verify.sentinelsignal.io/verify-intelligence-api","use_case":"Programmatic MCP server trust, comparison, policy, and evidence enrichment."},"trust_evaluated_at":"2026-10-03T03:07:35.410247+00:00","evidence_revision":"634017c7cf0c2883b59ec587","active_alert_summary":{"critical":1,"high":0,"medium":1,"low":1,"high_or_critical":1,"total":3},"materialization":{"state":"partial","trust_core_complete":true,"fields_unavailable":["tool_security_inventory","security_posture_summary","write_action_governance","capability_taxonomy","remediations"],"materialized_at":"2026-10-03T03:07:35.410247+00:00"}}