"Octopus Deploy" matching MCP tools:

• tflogs

  InsideOut (Riley)

  MONITORING: Fetch Terraform deployment logs with pagination Fetches logs from a running or completed Terraform deployment job. For **completed jobs**: uses REST endpoint for instant retrieval (supports `tail` for server-side filtering). For **running jobs**: streams via SSE with timeout-based pagination. **PAGINATION** (running jobs only): Use `last_event_id` from the response to fetch more: 1. First call: `tflogs(session_id='...')` → get logs + `last_event_id` 2. Next call: `tflogs(session_id='...', last_event_id='...')` → get NEW logs only 3. Repeat until `complete: true` in response **RESPONSE FIELDS**: - `logs`: Array of log messages collected - `last_event_id`: Pass this back to get more logs (pagination cursor, SSE only) - `complete`: true if job finished, false if more logs may be available - `total_logs`: total log entries before tail truncation REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs), timeout (default 50s, max 55s), last_event_id (for pagination), tail (return only last N entries) ⚠️ CONTEXT WARNING: Deploy logs can be hundreds of lines. Use tail: 50 for completed jobs to avoid blowing up the context window.

  Connector

• tfgenerate

  InsideOut (Riley)

  WORKFLOW: Step 3 of 4 - Generate Terraform files from completed design Generate Terraform files from an InsideOut session that has completed infrastructure design. ⚠️ PREREQUISITE: Only call this AFTER convoreply returns with `terraform_ready=true` in the response metadata. DO NOT call this while convoreply is still running or before terraform_ready is confirmed! If you get 'session has not reached terraform-ready state', wait for convoreply to complete first. 🎯 USE THIS TOOL WHEN: convoreply has returned with terraform_ready=true, OR the user asks to 'see the terraforms', 'generate terraform', 'show me the code', etc. **DEFAULT RESPONSE**: Returns summary table + download URL (keeps code out of LLM context). **FALLBACK**: Set `include_code: true` to get full code inline if curl/unzip fails. **CRITICAL WORKFLOW** (default mode): 1. Call this tool to get file summary and download URL 2. ASK the user: 'Where would you like me to save the Terraform files? Default: ./insideout-infra/' 3. WAIT for user confirmation before running the download command 4. Run the curl/unzip command with the user's chosen directory 5. If curl/unzip FAILS (sandbox, security, platform issues), retry with `include_code: true` **AFTER GENERATION**: Ask user if they want to review the files and then deploy with tfdeploy REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: include_code (boolean) - set true to return full code inline as fallback. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.

  Connector

• gcpinspect_batch

  InsideOut (Riley)

  BATCH INSPECTION: run up to 32 GCP inspect probes in one call. ⚠️ **PREREQUISITE**: Same as gcpinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling. Use this when you need to check more than ~3 resources. The backend fetches Oracle credentials ONCE per batch and fans out probes against a single GCP credentials blob — a 12-resource health check is ~5–8× faster and 12× fewer Oracle round-trips than calling gcpinspect 12 times. BUDGETS: - Up to 32 sub-probes per call (subs array length). - 30s per-sub timeout; 60s total batch wall-clock. - Concurrency cap 8. - 512 KB response cap: subs past the cap keep their envelope (index/service/action/ok) but have result replaced with truncated=true. PARTIAL FAILURE IS EXPECTED. The response is an ordered results array; each entry has {index, service, action, ok, result, error}. Inspect each result — do NOT abort on the first error. A credential fetch failure leaves cred-less probes (list-actions, list-metrics) succeeding anyway. REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: apigateway, bastion, billing, certificatemanager, cloudarmor, cloudbuild, cloudcdn, clouddeploy, clouddns, cloudfunctions, cloudkms, cloudlogging, cloudmonitoring, cloudrun, cloudsql, compute, firestore, gcs, gke, iam, identityplatform, loadbalancer, memorystore, pubsub, secretmanager, vertexai, vpc For a specific service's actions, use gcpinspect (singular) with action="list-actions" — batch is not the place for discovery. Batch responses are always summarized (no detail/raw per-sub); use singular gcpinspect when you need full metadata or raw API output for one resource. EXAMPLES: - gcpinspect_batch(session_id=..., subs=[ {"service":"compute","action":"list-instances"}, {"service":"gke","action":"list-clusters"}, {"service":"cloudsql","action":"list-instances"}]) - gcpinspect_batch(session_id=..., subs=[ {"service":"compute","action":"get-metrics","filters":"{\"hours\":6}"}, {"service":"cloudrun","action":"get-metrics","filters":"{\"hours\":6}"}])

  Connector

• errors.timeline

  Clamp Analytics

  Error count over time, bucketed hourly or daily. Optionally scoped to one fingerprint. Use this to spot regressions ("when did this bug start firing?") or post-deploy spikes ("did the deploy at 14:00 break something?"). Returns a series of { bucket, count } rows. The bucket field is an ISO timestamp aligned to the start of the interval. Buckets with no errors are omitted (sparse series); the agent should treat missing buckets as zero. Examples: - "error rate over the last week" → period="7d" (returns hourly buckets) - "did errors spike after yesterday's deploy" → period="2d", interval="hour" - "is this specific fingerprint getting worse" → fingerprint="<hex>", period="30d" - "yearly error trend" → period="365d", interval="day" Limitations: interval defaults to "hour" for periods ≤ 7 days, "day" otherwise. Override only if the default produces too few or too many buckets for the question. No fingerprint filter returns the total error rate across all bugs. Pairs with: `errors.groups` (find which fingerprint is worth charting); `traffic.timeseries` (compare error count to traffic count to see if the rate is real or just a traffic surge); `traffic.compare` (week-over-week or before-after-deploy comparison).

  Connector

• ateam_redeploy

  ateam-mcp

  Re-deploy skills WITHOUT changing any definitions. ⚠️ HEAVY OPERATION: regenerates MCP servers (Python code) for every skill, pushes each to A-Team Core, restarts connectors, and verifies tool discovery. Takes 30-120s depending on skill count. Use after connector restarts, Core hiccups, or stale state. For incremental changes, prefer ateam_patch (which updates + redeploys in one step).

  Connector

• publish_website

  Yeetit - POST HTML, get a URL. No account needed.

  Publish HTML content to a live URL instantly. No account or API key required. Returns a public URL that anyone can visit. Sites expire after 24 hours unless the owner claims them. Use this when a user asks you to build, create, or deploy a website, landing page, invitation, portfolio, report, or any HTML content they want to share as a link. Supports bundled assets (CSS, JS, images) and multi-page sites (include additional .html files in assets, accessible at /{slug}/{pagename}). IMPORTANT: After publishing, always share the live URL and the claim URL with the user. The claim URL lets them take permanent ownership of the site. Store the edit_key from the response silently — do not show it to the user — you will need it if they ask you to make changes to the site later. If you lose the edit_key, ask the user to claim the site first (via the claim URL in the page footer), then provide you with their API key from the dashboard — you can use that instead.

  Connector

• send_message

  Dock

  Send a direct message to another agent or human in the messaging substrate. Wires through cue.dock.svc, the same path the /live UI uses, so the recipient sees this message in their drawer (and, once they have a Dock-connected agent worker running, their agent harness's inbox). Address format is `<agent_slug>@<user_slug>`: `flint@socrates` targets the `flint` agent owned by user `socrates`; `self@<user_slug>` targets a human's synthetic self-agent (use this to message a human directly when you don't know which of their agents to ping). Use this when an agent legitimately needs to ask a teammate (human or agent) for help, hand off work, or follow up async; don't use it as a chat-ops side-channel for things that belong in workspace events. Sender identity follows the caller: agent callers send AS themselves, user callers send AS their self-agent (`self@<their_slug>`). Body cap is 32,000 chars. Returns `{ messageId, threadId, to }` on success. The recipient is resolved against the substrate's identity space, NOT against your accessible workspace set, this is messaging, not workspace write access. Pre-cue.dock.svc-deploy environments return `cue_not_configured` (caller treats as 'messaging not deployed yet').

  Connector

• tfgenerate

  InsideOut (Riley)

  WORKFLOW: Step 3 of 4 - Generate Terraform files from completed design Generate Terraform files from an InsideOut session that has completed infrastructure design. ⚠️ PREREQUISITE: Only call this AFTER convoreply returns with `terraform_ready=true` in the response metadata. DO NOT call this while convoreply is still running or before terraform_ready is confirmed! If you get 'session has not reached terraform-ready state', wait for convoreply to complete first. 🎯 USE THIS TOOL WHEN: convoreply has returned with terraform_ready=true, OR the user asks to 'see the terraforms', 'generate terraform', 'show me the code', etc. **DEFAULT RESPONSE**: Returns summary table + download URL (keeps code out of LLM context). **FALLBACK**: Set `include_code: true` to get full code inline if curl/unzip fails. **CRITICAL WORKFLOW** (default mode): 1. Call this tool to get file summary and download URL 2. ASK the user: 'Where would you like me to save the Terraform files? Default: ./insideout-infra/' 3. WAIT for user confirmation before running the download command 4. Run the curl/unzip command with the user's chosen directory 5. If curl/unzip FAILS (sandbox, security, platform issues), retry with `include_code: true` **AFTER GENERATION**: Ask user if they want to review the files and then deploy with tfdeploy REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: include_code (boolean) - set true to return full code inline as fallback. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.

  Connector

• awsinspect_batch

  InsideOut (Riley)

  BATCH INSPECTION: run up to 32 AWS inspect probes in one call. ⚠️ **PREREQUISITE**: Same as awsinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling. Use this when you need to check more than ~3 resources. The backend fetches Oracle credentials ONCE per batch and fans out probes against a single AWS config — for a 12-resource health check this is ~5–8× faster and 12× fewer Oracle round-trips than calling awsinspect 12 times. BUDGETS: - Up to 32 sub-probes per call (subs array length). - 30s per-sub timeout; 60s total batch wall-clock. - Concurrency cap 8 — sub-probes run in parallel but never saturate AWS. - 512 KB response cap: subs past the cap keep their envelope (index/service/action/ok) but have result replaced with truncated=true. PARTIAL FAILURE IS EXPECTED. The response is an ordered results array; each entry has {index, service, action, ok, result, error}. Inspect each result — do NOT abort on the first error. A credential fetch failure leaves cred-less probes (list-actions, list-metrics) succeeding anyway. REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: account, acm, alb, apigateway, apprunner, backup, bedrock, cloudfront, cloudwatchlogs, cognito, cost-explorer, dynamodb, ebs, ec2, ecs, eks, elasticache, kms, lambda, msk, opensearch, rds, route53, s3, sagemaker, secretsmanager, sqs, vpc, waf For a specific service's actions, use awsinspect (singular) with action="list-actions" — batch is not the place for discovery. Batch responses are always summarized (no detail/raw per-sub); use singular awsinspect when you need full metadata or raw API output for one resource. EXAMPLES: - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"describe-instances"}, {"service":"rds","action":"describe-db-instances"}, {"service":"vpc","action":"describe-vpcs"}, {"service":"s3","action":"list-buckets"}]) - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"get-metrics","filters":"{\"hours\":6}"}, {"service":"rds","action":"get-metrics","filters":"{\"hours\":6}"}])

  Connector

• gcpinspect_batch

  InsideOut (Riley)

  BATCH INSPECTION: run up to 32 GCP inspect probes in one call. ⚠️ **PREREQUISITE**: Same as gcpinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling. Use this when you need to check more than ~3 resources. The backend fetches Oracle credentials ONCE per batch and fans out probes against a single GCP credentials blob — a 12-resource health check is ~5–8× faster and 12× fewer Oracle round-trips than calling gcpinspect 12 times. BUDGETS: - Up to 32 sub-probes per call (subs array length). - 30s per-sub timeout; 60s total batch wall-clock. - Concurrency cap 8. - 512 KB response cap: subs past the cap keep their envelope (index/service/action/ok) but have result replaced with truncated=true. PARTIAL FAILURE IS EXPECTED. The response is an ordered results array; each entry has {index, service, action, ok, result, error}. Inspect each result — do NOT abort on the first error. A credential fetch failure leaves cred-less probes (list-actions, list-metrics) succeeding anyway. REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: apigateway, bastion, billing, certificatemanager, cloudarmor, cloudbuild, cloudcdn, clouddeploy, clouddns, cloudfunctions, cloudkms, cloudlogging, cloudmonitoring, cloudrun, cloudsql, compute, firestore, gcs, gke, iam, identityplatform, loadbalancer, memorystore, pubsub, secretmanager, vertexai, vpc For a specific service's actions, use gcpinspect (singular) with action="list-actions" — batch is not the place for discovery. Batch responses are always summarized (no detail/raw per-sub); use singular gcpinspect when you need full metadata or raw API output for one resource. EXAMPLES: - gcpinspect_batch(session_id=..., subs=[ {"service":"compute","action":"list-instances"}, {"service":"gke","action":"list-clusters"}, {"service":"cloudsql","action":"list-instances"}]) - gcpinspect_batch(session_id=..., subs=[ {"service":"compute","action":"get-metrics","filters":"{\"hours\":6}"}, {"service":"cloudrun","action":"get-metrics","filters":"{\"hours\":6}"}])

  Connector

• deltasignal_readiness

  DeltaSignal ATLAS-7

  Use this read-only tool before analysis to verify that the DeltaSignal ATLAS-7 data plane is live, fresh, and safe to query. It returns service readiness, active source dates, issuer coverage, quality coverage, debt coverage, live-price status, market regime, and tower-coherence diagnostics. Parameters: none; call it exactly as-is when the user asks if DeltaSignal is ready or whether data freshness is acceptable. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, does not write external systems, and does not handle secrets or payments itself. Use it at the start of an agent workflow, after a deploy, or whenever results should be gated on freshness; use daily_changes for what changed and issuer tools for company-specific analysis.

  Connector

• awsinspect_batch

  InsideOut (Riley)

  BATCH INSPECTION: run up to 32 AWS inspect probes in one call. ⚠️ **PREREQUISITE**: Same as awsinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling. Use this when you need to check more than ~3 resources. The backend fetches Oracle credentials ONCE per batch and fans out probes against a single AWS config — for a 12-resource health check this is ~5–8× faster and 12× fewer Oracle round-trips than calling awsinspect 12 times. BUDGETS: - Up to 32 sub-probes per call (subs array length). - 30s per-sub timeout; 60s total batch wall-clock. - Concurrency cap 8 — sub-probes run in parallel but never saturate AWS. - 512 KB response cap: subs past the cap keep their envelope (index/service/action/ok) but have result replaced with truncated=true. PARTIAL FAILURE IS EXPECTED. The response is an ordered results array; each entry has {index, service, action, ok, result, error}. Inspect each result — do NOT abort on the first error. A credential fetch failure leaves cred-less probes (list-actions, list-metrics) succeeding anyway. REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: account, acm, alb, apigateway, apprunner, backup, bedrock, cloudfront, cloudwatchlogs, cognito, cost-explorer, dynamodb, ebs, ec2, ecs, eks, elasticache, kms, lambda, msk, opensearch, rds, route53, s3, sagemaker, secretsmanager, sqs, vpc, waf For a specific service's actions, use awsinspect (singular) with action="list-actions" — batch is not the place for discovery. Batch responses are always summarized (no detail/raw per-sub); use singular awsinspect when you need full metadata or raw API output for one resource. EXAMPLES: - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"describe-instances"}, {"service":"rds","action":"describe-db-instances"}, {"service":"vpc","action":"describe-vpcs"}, {"service":"s3","action":"list-buckets"}]) - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"get-metrics","filters":"{\"hours\":6}"}, {"service":"rds","action":"get-metrics","filters":"{\"hours\":6}"}])

  Connector

• ateam_build_and_run

  ateam-mcp

  DEPLOY THE CURRENT MAIN BRANCH TO A-TEAM CORE. ⚠️ HEAVIEST OPERATION (60-180s): validates solution+skills → deploys all connectors+skills to Core (regenerates MCP servers) → health-checks → optionally runs a warm test → auto-pushes to GitHub. 🌳 DEV/PROD WORKFLOW: 1. Edit files → ateam_github_patch (writes to `dev` branch by default) 2. (Optional) Preview what's about to ship → ateam_github_diff 3. Ship dev → main → ateam_github_promote (merges + auto-tags `prod-YYYY-MM-DD-NNN`) 4. Deploy main to Core → ateam_build_and_run This tool ALWAYS deploys the `main` branch — there is no `ref` parameter. To deploy in-progress dev work, first promote it. AUTO-DETECTS GitHub repo: if you omit mcp_store and a repo exists, connector code is pulled from main automatically. First deploy requires mcp_store. After that, edit via ateam_github_patch + promote, then build_and_run. For small changes prefer ateam_patch (faster, incremental). Requires authentication.

  Connector

• update_schema

  RationalBloks

  Update a project's schema (saves to database, does NOT deploy). ⚠️ CRITICAL: Follow ALL rules from create_project: • FLAT format (no 'fields' nesting) • string: MUST have max_length • decimal: MUST have precision + scale • Use "datetime" NOT "timestamp" • DON'T define: id, created_at, updated_at • NEVER create users/customers/employees tables (use app_users) ⚠️ MIGRATION RULES: • New fields MUST be "required": false OR have "default" value • Cannot add required field without default to existing tables • Safe: {new_field: {type: "string", max_length: 100, required: false}} WORKFLOW: 1. Use get_schema to see current schema 2. Modify following ALL rules 3. Call update_schema (saves only) 4. Call deploy_staging to apply changes 5. Monitor with get_job_status NOTE: This only saves the schema. You MUST call deploy_staging afterwards to apply changes.

  Connector

• convoreply

  InsideOut (Riley)

  WORKFLOW: Step 2 of 4 - Continue infrastructure design conversation Send a user message to the active InsideOut session and receive the assistant reply. The response contains a clean message from Riley - display it to the user. ⚠️ CRITICAL: DO NOT answer Riley's questions yourself! Forward questions to the user and wait for their response. NEVER fabricate or assume the user's answer, even if you think you know what they would say. Examples of questions Riley asks that YOU MUST forward to the user: - 'Any questions or tweaks to these details?' - 'Ready for the cost estimate?' - 'Do you want to change the stack/config?' - 'Ready to proceed to Terraform?' When Riley asks ANY question, STOP and wait for the user's answer! 📋 WORKFLOW PHASES: The typical flow is conversation → tfgenerate → tfdeploy When terraform_ready=true appears in THIS tool's response, THEN you can call tfgenerate. ⚠️ DO NOT call tfgenerate until this tool returns! Wait for the response first. 🎯 KEY SIGNALS IN RESPONSE: - `[TERRAFORM_READY: true]` → NOW you can call tfgenerate - `[[BUTTON_TF_APPLY: ...]]` → Deployment is ready! Ask user if they want to deploy, then use tfdeploy - `[[BUTTON_TF_DESTROY: ...]]` → User confirmed destroy intent! Ask user to confirm, then use tfdestroy - `[[BUTTON_TF_PLAN: ...]]` → User wants to preview changes! Use tfplan to run a plan, then tfdeploy with plan_id to apply REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: timeout (integer) - seconds to wait for response. For Cursor, use 50 (default). Max 55. OPTIONAL: project_context (string) - Only pass genuinely NEW project details the user shares after convoopen. Do NOT resend context already provided in convoopen — Riley remembers it. Do NOT scan files or directories to gather this — only use what the user explicitly tells you. Example: user reveals a new constraint like 'we also need HIPAA compliance' mid-conversation. 💡 TIP: Use convostatus to check progress anytime. Examine workflow.usage prompt for more guidance.

  Connector

• tfdeploy

  InsideOut (Riley)

  WORKFLOW: Step 4 of 4 - Deploy infrastructure to the cloud Deploy infrastructure by starting a Terraform job for an InsideOut session. This tool initiates the actual deployment process after Terraform files have been generated. IMPORTANT: This starts a long-running job (15+ minutes). Use tfstatus to monitor progress. SINGLE-FLIGHT: only one TF job (apply/plan/destroy/drift) runs per session at a time. If another job is already in flight, tfdeploy returns tf_job_conflict with the live job_id — attach with tfstatus/tflogs instead of retrying, or pass force_new=true to override. Returns confirmation that the deployment has started. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: plan_id (string) — Apply a previously created plan from tfplan. Preview-then-apply workflow: tfplan → tflogs (review) → tfdeploy(plan_id=...). OPTIONAL: sandbox (boolean, default false) — deploys real generated Terraform. Set to true for cheap sandbox template (testing only). OPTIONAL: ignore_drift (boolean, default false) - when true, proceeds with deploy even if infrastructure drift is detected. By default, deploys fail on drift. Use after reviewing drift details via tfdrift or tflogs. OPTIONAL: force_new (boolean, default false) - bypass the session-level single-flight guard. Use only when the existing run is provably wedged. CREDENTIAL FLOW (if credentials are missing): 1. Response includes a connect_url — present it to the user 2. Call credawait(session_id=...) to poll for credentials 3. When credawait returns success, retry tfdeploy Do NOT call credawait without first showing the connect URL to the user.

  Connector

• convoreply

  InsideOut (Riley)

  WORKFLOW: Step 2 of 4 - Continue infrastructure design conversation Send a user message to the active InsideOut session and receive the assistant reply. The response contains a clean message from Riley - display it to the user. ⚠️ CRITICAL: DO NOT answer Riley's questions yourself! Forward questions to the user and wait for their response. NEVER fabricate or assume the user's answer, even if you think you know what they would say. Examples of questions Riley asks that YOU MUST forward to the user: - 'Any questions or tweaks to these details?' - 'Ready for the cost estimate?' - 'Do you want to change the stack/config?' - 'Ready to proceed to Terraform?' When Riley asks ANY question, STOP and wait for the user's answer! 📋 WORKFLOW PHASES: The typical flow is conversation → tfgenerate → tfdeploy When terraform_ready=true appears in THIS tool's response, THEN you can call tfgenerate. ⚠️ DO NOT call tfgenerate until this tool returns! Wait for the response first. 🎯 KEY SIGNALS IN RESPONSE: - `[TERRAFORM_READY: true]` → NOW you can call tfgenerate - `[[BUTTON_TF_APPLY: ...]]` → Deployment is ready! Ask user if they want to deploy, then use tfdeploy - `[[BUTTON_TF_DESTROY: ...]]` → User confirmed destroy intent! Ask user to confirm, then use tfdestroy - `[[BUTTON_TF_PLAN: ...]]` → User wants to preview changes! Use tfplan to run a plan, then tfdeploy with plan_id to apply REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: timeout (integer) - seconds to wait for response. For Cursor, use 50 (default). Max 55. OPTIONAL: project_context (string) - Only pass genuinely NEW project details the user shares after convoopen. Do NOT resend context already provided in convoopen — Riley remembers it. Do NOT scan files or directories to gather this — only use what the user explicitly tells you. Example: user reveals a new constraint like 'we also need HIPAA compliance' mid-conversation. 💡 TIP: Use convostatus to check progress anytime. Examine workflow.usage prompt for more guidance.

  Connector

• publish_website

  YeetIt

  Publish HTML content to a live URL instantly. No account or API key required. Returns a public URL that anyone can visit. Sites expire after 24 hours unless the owner claims them. Use this when a user asks you to build, create, or deploy a website, landing page, invitation, portfolio, report, or any HTML content they want to share as a link. Supports bundled assets (CSS, JS, images) and multi-page sites (include additional .html files in assets, accessible at /{slug}/{pagename}). IMPORTANT: After publishing, always share the live URL and the claim URL with the user. The claim URL lets them take permanent ownership of the site. Store the edit_key from the response silently — do not show it to the user — you will need it if they ask you to make changes to the site later. If you lose the edit_key, ask the user to claim the site first (via the claim URL in the page footer), then provide you with their API key from the dashboard — you can use that instead.

  Connector

• tfdeploy

  InsideOut (Riley)

  WORKFLOW: Step 4 of 4 - Deploy infrastructure to the cloud Deploy infrastructure by starting a Terraform job for an InsideOut session. This tool initiates the actual deployment process after Terraform files have been generated. IMPORTANT: This starts a long-running job (15+ minutes). Use tfstatus to monitor progress. SINGLE-FLIGHT: only one TF job (apply/plan/destroy/drift) runs per session at a time. If another job is already in flight, tfdeploy returns tf_job_conflict with the live job_id — attach with tfstatus/tflogs instead of retrying, or pass force_new=true to override. Returns confirmation that the deployment has started. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: plan_id (string) — Apply a previously created plan from tfplan. Preview-then-apply workflow: tfplan → tflogs (review) → tfdeploy(plan_id=...). OPTIONAL: sandbox (boolean, default false) — deploys real generated Terraform. Set to true for cheap sandbox template (testing only). OPTIONAL: ignore_drift (boolean, default false) - when true, proceeds with deploy even if infrastructure drift is detected. By default, deploys fail on drift. Use after reviewing drift details via tfdrift or tflogs. OPTIONAL: force_new (boolean, default false) - bypass the session-level single-flight guard. Use only when the existing run is provably wedged. CREDENTIAL FLOW (if credentials are missing): 1. Response includes a connect_url — present it to the user 2. Call credawait(session_id=...) to poll for credentials 3. When credawait returns success, retry tfdeploy Do NOT call credawait without first showing the connect URL to the user.

  Connector

• perspective_create

  Perspective AI

  Creates a new perspective in DRAFT status from a natural-language description and starts the design agent. Returns immediately with a job_id and status "pending"; long-poll perspective_await_job with that job_id to receive the generated outline or follow-up question. Behavior: - Creates a new perspective on every call — not safe to retry blindly. Identical input produces a new perspective each time. - If workspace_id is omitted, the user's default workspace is used; errors with "No default workspace found..." if none exists. - Tip: use workspace_list to see all workspaces with their descriptions, then pick the best-matching workspace_id based on context. - Title is auto-generated from the description. - The design agent runs in the background and may take seconds to a minute. Resolve via perspective_await_job; terminal states are "ready" (outline generated, share/direct/preview URLs returned) or "needs_input" (follow-up question requires the user's answer). - description can reference research goals, source URLs, or audience details. Examples: "understand why trial users aren't converting", "convert the form at https://example.com/contact", "talk to churned customers from Q3". - agent_context selects the agent role: 'research' = Interviewer (default; deep qualitative interviews), 'form' = Concierge (replaces static forms with conversational flow), 'survey' = Evaluator (turns surveys into engaging conversations), 'advocate' = Advocate (listens, then responds from a brand/cause playbook). When to use this tool: - The user wants to create a new perspective from a brief. - You're starting the design conversation that may iterate via perspective_respond. When NOT to use this tool: - The perspective already exists and the user wants to change it — use perspective_update. - The agent already asked a follow-up question — use perspective_respond with the user's answer. - Listing or finding existing perspectives — use perspective_list. Typical flow: 1. perspective_create → start design (returns job_id) 2. perspective_await_job → long-poll until "ready" or "needs_input" 3. perspective_respond → if "needs_input", answer and re-poll 4. perspective_get_preview_link → test 5. perspective_update → refine 6. perspective_get_embed_options → deploy

  Connector