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261,118 tools. Last updated 2026-07-05 10:33

"Docker container and image management with IDE integration" matching MCP tools:

  • Update LLM instructions at the specified level. Required: level ('brain'|'personal_root'|'container'|'team'), instructions (string). Optional: id (integer, required for 'container' and 'team'), mode ('replace' default|'append'). The 'container' level updates personal containers only; to set instructions for a team, use level 'team' (team owners only). In 'replace' mode (default), the provided text overwrites existing instructions. In 'append' mode, the text is appended to existing instructions with a newline separator. Always read current instructions first before replacing to avoid losing existing content.
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  • Generate an AI image using Avocado AI. Returns a jobId immediately; image generation completes in 10-60 seconds. After calling, use the check_job tool with the returned jobId to retrieve the result, once complete, check_job returns the image inline so it renders directly in chat. Run models_list to see available models. Costs 1-4 credits per image depending on model and quality.
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  • MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token. Best: add it to this server's config as a header `Authorization: Bearer <token>` for a permanent, non-expiring connection. Or paste it here for a session-only login: call with { token: "<jwt>" } after the user pastes, or with no args to get the link.
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  • DESTRUCTIVE: Permanently delete an app, its Docker service, volume, and all data including version history. This cannot be undone. You MUST confirm with the user before calling this tool.
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  • WORKFLOW: Step 1 of 4 - Start infrastructure design conversation Open an InsideOut V2 session and receive the assistant's intro message. The response contains a clean message from Riley (the infrastructure advisor) - display it to the user. ⚠️ Riley will ask questions - forward these to the user, DO NOT answer on their behalf. CRITICAL: This tool returns a session_id in the response metadata. You MUST use this session_id for ALL subsequent tool calls (convoreply, tfgenerate, tfdeploy, etc.). ⚠️ The session_id includes a ?token=... suffix (format: sess_v2_xxx?token=yyy) which is part of the session credential — without it, downstream tools fall back to a tokenless connect URL that 401s. Always pass session_id verbatim to subsequent tools and to the user; do NOT shorten, paraphrase, or strip the ?token= portion when summarizing the session in chat or in your own scratch notes. Use when the user mentions keywords like: 'setup my cloud infra', 'provision infrastructure', 'deploy infra', 'start insideout', 'use insideout', or similar intent to begin infra setup. OPTIONAL: project_context (string) - General tech stack summary so Riley can skip discovery questions and jump to recommendations. The agent should confirm this with the user before sending. Include whichever apply: language/framework, databases/services, container usage, existing IaC, CI/CD platform, cloud provider, Kubernetes usage, what the project does. Example: 'Next.js 14 + TypeScript, PostgreSQL, Redis, Docker Compose, deployed to AWS ECS, GitHub Actions CI/CD, ~50k MAU'. NEVER include credentials, secrets, API keys, PII, source code, or internal URLs/IPs -- only general metadata summaries useful to a cloud architect agent. IMPORTANT: source (string) - You MUST set this to identify which IDE/tool you are. Auto-detect from your environment: 'claude-code', 'codex', 'antigravity', 'kiro', 'vscode', 'web', 'mcp'. If unsure, use the name of your IDE/tool in lowercase. Do NOT omit this — it controls the 'Open {IDE}' button on the credential connect screen. OPTIONAL: github_username (string) - GitHub username for deploy commit attribution. Pre-populates the GitHub username field on the connect page. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.
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  • MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token. Best: add it to this server's config as a header `Authorization: Bearer <token>` for a permanent, non-expiring connection. Or paste it here for a session-only login: call with { token: "<jwt>" } after the user pastes, or with no args to get the link.
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Matching MCP Servers

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  • Security audit for docker-compose.yml — 25 checks: secrets, privileges, network, volumes, images.

  • Plan optimal container & truck loads: 3D layouts, utilization, centre of gravity, crush checks.

  • MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token. Best: add it to this server's config as a header `Authorization: Bearer <token>` for a permanent, non-expiring connection. Or paste it here for a session-only login: call with { token: "<jwt>" } after the user pastes, or with no args to get the link.
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  • MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token. Best: add it to this server's config as a header `Authorization: Bearer <token>` for a permanent, non-expiring connection. Or paste it here for a session-only login: call with { token: "<jwt>" } after the user pastes, or with no args to get the link.
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  • MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token. Best: add it to this server's config as a header `Authorization: Bearer <token>` for a permanent, non-expiring connection. Or paste it here for a session-only login: call with { token: "<jwt>" } after the user pastes, or with no args to get the link.
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  • MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token. Best: add it to this server's config as a header `Authorization: Bearer <token>` for a permanent, non-expiring connection. Or paste it here for a session-only login: call with { token: "<jwt>" } after the user pastes, or with no args to get the link.
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  • MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token. Best: add it to this server's config as a header `Authorization: Bearer <token>` for a permanent, non-expiring connection. Or paste it here for a session-only login: call with { token: "<jwt>" } after the user pastes, or with no args to get the link.
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  • MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token. Best: add it to this server's config as a header `Authorization: Bearer <token>` for a permanent, non-expiring connection. Or paste it here for a session-only login: call with { token: "<jwt>" } after the user pastes, or with no args to get the link.
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  • Copy an image that already exists on one output onto another cell, instant and free (no regeneration, no credits). Use this when the user wants 'the same image' on a second surface ('use the LinkedIn image on X', 'same picture on the newsletter') instead of niche_render_image_card (which generates a new image and costs credits). Both cells must already exist on the session (add the target via niche_add_output first if needed) and the source must have a rendered image. Copies the source's static_urls onto the target so it publishes with that image. Idempotent: source==target is a no-op.
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  • [Docker] List the public OctoPerf Cloud Docker providers shared across all workspaces. Use these when `list_docker_providers_by_workspace` returns an empty list. Returns each provider's id, name, type (always PUBLIC), available regions, and enabled flag. The `url` deep-link is empty because public providers are not bound to a specific workspace. Pick one to feed into `validate_virtual_user` (providerId + region).
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  • MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token. Best: add it to this server's config as a header `Authorization: Bearer <token>` for a permanent, non-expiring connection. Or paste it here for a session-only login: call with { token: "<jwt>" } after the user pastes, or with no args to get the link.
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  • Upload an image and return the hosted image record. ``data`` must be the image bytes encoded as standard base64 (RFC 4648). Accepted image formats are PNG, JPEG, WebP, and GIF. ``visibility`` controls who can access the served URL: ``"public"`` makes it accessible to anyone with the link; ``"private"`` (default) requires the owner's credentials. Accepted values: ``"public"``, ``"private"``. ``ttl_seconds`` sets an expiry relative to now (positive integer). Omit to create a permanent image. Returns: ``{id, token, url, visibility, expires_at, size_bytes, content_type}``.
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  • [Runtime] Snapshot of the Docker container logs produced by an OctoPerf load test — the same lines the web UI streams on the running-bench logs panel. Use this to diagnose launch failures (image pull errors, provider quotas, missing project files, agent boot crashes) when `get_bench_result` reports `state=ERROR` or the run is stuck in PREPARING / INITIALIZING. Returns one entry per log line with `date`, `level` (INFO / WARN / ERROR), and `message`. The server resolves the BenchResult's batchId internally — pass the `benchResultId`, not the batchId. No incremental cursor; the upstream API always returns the full log set, sort/filter client-side.
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  • Deploy a project to the staging environment. This triggers: (1) Schema validation, (2) Docker image build, (3) GitHub commit, (4) Kubernetes deployment, (5) Database migrations. The operation is ASYNCHRONOUS - it returns immediately with a job_id. Use get_job_status with the job_id to monitor progress. Deployment typically takes 2-5 minutes depending on schema complexity. If deployment fails, check: (1) Schema format is FLAT (no 'fields' nesting), (2) Every field has a 'type' property, (3) Foreign keys reference existing tables, (4) No PostgreSQL reserved words in table/field names. Use get_project_info to see if the deployment succeeded.
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  • Restore and enhance faces in an image using GFPGAN. Detects all faces via RetinaFace, restores quality (fixes blur, noise, compression artifacts), and pastes them back. Optionally enhances the background using Real-ESRGAN. GPU-accelerated, sub-3s latency. Args: image_base64: Base64-encoded image data containing faces (PNG, JPEG, WebP). upscale: Output upscale factor -- 1 to 4 (default: 2). enhance_background: Whether to enhance background with Real-ESRGAN (default: true). Returns: dict with keys: - image (str): Base64-encoded restored image - format (str): Output image format - width (int): Output width - height (int): Output height - upscale (int): Scale factor applied - processing_time_ms (float): Processing time in milliseconds
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  • Get Container Freight Station (CFS) handling tariffs — charges for LCL (Less than Container Load) cargo consolidation and deconsolidation at port warehouses. Use this for LCL shipments to estimate warehouse handling costs. Returns per-unit handling rates, minimum charges, and storage fees at the specified port. Not relevant for FCL (Full Container Load) shipments. PAID: $0.05/call via x402 (USDC on Base or Solana). Without payment, returns 402 with payment instructions. Returns: Array of { facility, service_type, cargo_type, rate_per_unit, unit, minimum_charge, currency }.
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