"How to deploy a web application" matching MCP tools:

• get_project_info

  Fractera

  Project reference / help desk about Fractera. Use this to answer ANY user question about what Fractera is, how it works, its architecture, components, modes, data ownership, pricing, use cases, partner program, etc. — especially while a deploy is running and the user wants to learn more. TOKEN-ECONOMY: call with NO arguments first to get the lightweight list of section ids+titles, then call again with a single `section` id to fetch just that section. NEVER try to fetch everything at once; pull only the section(s) relevant to the user question. Set `lang:"ru"` for Russian-speaking users.

  Connector

• adcritter_build_app

  AdCritter for Builders

  Start here when building an application. Returns an overview of what the AdCritter platform offers and a catalog of feature guides you can query with the adcritter_guidance tool to learn how to build each part of the app. Call adcritter_guidance(key) for any feature area to get detailed building instructions with API endpoints and response shapes.

  Connector

• label_history

  Dailymed

  Drug LABEL REVISION HISTORY — when a prescription drug's FDA label was revised, and how many times. Pass a DRUG NAME ("Ozempic", "semaglutide") and it resolves to that drug's Structured Product Label automatically; or pass a DailyMed set_id directly. PREFER OVER WEB SEARCH for "has the label for X changed recently", "when was X's label last updated", "how many label revisions does X have", label-change monitoring, and safety-labeling-change surveillance. Returns every published SPL version with its date, plus official archive URLs where DailyMed exposes them. Use before label_diff to see which versions are downloadable and pick two to compare.

  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

• get_strategy_code

  quantifyme

  Get the actual Python code behind a community leaderboard strategy. Use after `browse_community`: pass an entry's `id` here to read its real `feature_engineering()` + `strategy_config()` source so the user can inspect or tweak it. To deploy it unchanged, pass the same id to `one_shot` as `community_id`. Read-only, no signup needed. Args: community_id: The `id` of a community entry (from `browse_community`). Returns: dict with: id, title, username, description, symbol, timeframe, metrics {total_ret, win_rate, profit_factor, n_trades, mdd, sharpe_strat}, and `code` (the full Python source). SHOW the code to the user, and offer to deploy it via one_shot(community_id=...) or tweak it first.

  Connector

• get_project_info

  Fractera

  Project reference / help desk about Fractera. Use this to answer ANY user question about what Fractera is, how it works, its architecture, components, modes, data ownership, pricing, use cases, partner program, etc. — especially while a deploy is running and the user wants to learn more. TOKEN-ECONOMY: call with NO arguments first to get the lightweight list of section ids+titles, then call again with a single `section` id to fetch just that section. NEVER try to fetch everything at once; pull only the section(s) relevant to the user question. Set `lang:"ru"` for Russian-speaking users.

  Connector

• check_status

  Fractera

  Read the current installation progress ONCE, on demand. Call this only when the user explicitly asks how the deploy is going (e.g. "what is the status", "did it finish") — never on a timer and never in a polling loop. The deploy takes 8-14 minutes and the authoritative status channels are the email pipeline + the dashboard; one read on request is enough. Returns the current step, the list of completed steps (~44 total in a full bootstrap), and whether installation is done or failed.

  Connector

• dero_get_gas_estimate

  DERO MCP Server

  Estimate gas (compute + storage) for transfers, SC deploys, or SC invokes via DERO.GetGasEstimate. This is a PRE-FLIGHT check; nothing is submitted. When to call: BEFORE any wallet-side transfer/scinvoke (using external wallet tooling) to size fees, OR when explaining deploy costs to a user. PREFER citing dero_docs_search("gas estimate" or "fees") so the user understands how compute vs storage gas are charged. Input Requirements (CRITICAL): - At least ONE of `transfers`, `sc`, or `sc_rpc` MUST be provided. - `sc` is the DVM-BASIC contract source string when estimating a deploy. - `sc_rpc` is an array of `{ name, datatype, value }` invocation arguments (entrypoint + SC_ID + caller-provided params). - `signer` is OPTIONAL but PREFERRED; pass the `dero1.../deto1...` address that would sign the eventual tx. Output: `{ gascompute, gasstorage, status }`.

  Connector

• clerk.create_oauth_application

  Vee3

  Create a new OAuth application in a connected Clerk application (Clerk as IdP). **Sensitive** — client_secret may be present for confidential clients; do not log or expose it. Call clerk.get_connected_accounts first. Pass clerk_instance_id to target a specific connection, or omit it to use the default account. Returns the new OAuth application summary. Cost = 10 tokens.

  Connector

• check_deploy_prerequisites

  mcp

  The deploy ENTRY GATE — run this first when a user wants to deploy. Verifies the account is ready and AUTO-SELECTS the network_id + keypair_name to pass to deploy_app (so you never hunt for them): (1) account reachable + quota, (2) a private network (picks your default — every account has one), (3) an SSH keypair (if you have none, returns ssh_key_recipe: the exact local ssh-keygen → import_keypair → write SSH_KEY_LOCATION-to-.env steps — run them, in yolo mode without asking). Returns { ready, network_id, keypair_name, missing, ssh_key_recipe }.

  Connector

• deploy_verifier

  Goodeye

  Deploy a reusable semantic verifier (an LLM-judge check skills reference by id) that scores agent output against a criterion: creates it or appends a version. A semantic verifier is a single judgment ("does this output satisfy this criterion?") evaluated by an LLM judge. Verifiers are private and owner-scoped. Skills reference them by ``verifier_id`` (or ``verifier_id@version``). You cannot deploy a **new** verifier whose ``name`` matches an active platform ``scope=system`` verifier: those definitions are server-owned, never listed or fetched, and only executable through ``run_verifier``. Versioning: the first deploy with a given ``name`` creates the verifier at version 1. Re-deploying the same ``name`` appends a new version and requires ``expected_version_token`` from the latest known version (returned by deploy/list/get). A new verifier must omit the token; an existing one without a token returns Conflict. Input contracts: - ``text``: ``input_fields`` required, ``media_url`` rejected. - ``text_image``: ``input_fields`` plus ``media_url`` required at run time. - ``image``: ``input_fields`` empty, only ``media_url`` at run time. Few-shot examples (3 to 10 typical) calibrate the judge; each example must match the contract (text-only inputs, text+image, or image-only). Returns: ``{verifier_id, name, current_version, version, version_token, status, input_contract, config_hash}``. Persist ``version_token`` for the next re-deploy.

  Connector

• deploy_image_generator

  Goodeye

  Deploy a reusable image generator that skills reference to produce images from a chosen model: creates it or appends a version. An image generator is a named, versioned configuration that routes image generation calls to a specific model. Generators are private and owner-scoped. Skills reference them by UUID or ``uuid@version``. You cannot deploy a new generator whose ``name`` matches an active platform ``scope=system`` generator (those are tier-level configs that are run-only and not listed or fetched). Versioning: the first deploy with a given ``name`` creates the generator at version 1. Re-deploying the same ``name`` appends a new version and requires ``expected_version_token`` from the latest known version (returned by deploy/list/get). A new generator must omit the token; an existing one without a token returns Conflict. Deploy-time validation: the ``model`` is checked against the pricing layer. A model that does not resolve to a known image endpoint with an authoritative price is rejected before any row is written. Returns: ``{generator_id, name, description, current_version, version, version_token, status, scope, provider, model, generation_contract, config_hash, created_at}``. Persist ``version_token`` for the next re-deploy.

  Connector

• clipform_get_guide

  Clipform

  Retrieve craft knowledge for building a specific form type. Returns question psychology, difficulty curves, narration style, scoring setup, and writing principles as markdown. Does NOT return a step-by-step build workflow - use clipform_get_workflow for that. Available types: quiz, survey, interview, funnel, testimonial, application, booking. Aliases also accepted: trivia → quiz, test → quiz, exam → quiz, feedback → survey, poll → survey, nps → survey, questionnaire → survey, case-study → interview, callout → interview, lead-gen → funnel, qualification → funnel, lead-magnet → funnel, story → testimonial, review → testimonial, job-application → application, admission → application, enrollment → application, grant → application, registration → booking, signup → booking, event → booking, rsvp → booking, workshop → booking. Quiz variants (optional): personality, comprehension, composition - appends variant-specific addendum to the base quiz guide.

  Connector

• get_doc

  blobpedia

  Read the FULL text of one indexed Celestia documentation page by id (Celestia node and developer docs plus partner docs) — exact commands, flags, and whole sections a search snippet truncates. Best for Celestia operational/how-to questions. Celestia docs only — not docs of other projects (use that projects docs tool) and not the web (use a web-search tool). Get the id from search or semantic_search first.

  Connector

• save_note

  anamnese

  Save a new note with learned knowledge or procedures. Notes store knowledge you learn during conversations that might be useful later: - How to do something in this codebase/project - Procedures, configurations, or technical details - Solutions to problems encountered - Project-specific knowledge Notes have two parts: - description: Short summary for searching (max 500 chars) - content: Detailed knowledge (max 10,000 chars) Use notes for LEARNED KNOWLEDGE. Use facts for TRUTHS ABOUT THE USER. Examples: - description: "How to deploy this Next.js project to Vercel" content: "1. Run 'vercel' command... 2. Configure environment variables..." - description: "Database migration process for this project" content: "Migrations are in supabase/migrations/. To apply: npx supabase db push..." SELF-LEARNING (scope="ai_client"): Your persistent memory across conversations. Save a note whenever you learn something worth remembering — don't wait, save as you go. Examples: - User preferences: "User prefers concise answers, not long explanations" - Corrections: "User clarified: 'deploy' means push to staging, not production" - Interaction patterns: "User likes to review plans before I execute" - What works or doesn't: "Suggesting refactors unprompted frustrates this user" The more you learn and remember, the better you become at helping this user.

  Connector

• ateam_github_promote

  ateam-mcp

  SHIP DEV TO PROD. Merges the `dev` branch into `main` and auto-tags the new main HEAD as safe-YYYY-MM-DD-NNN. Use after testing your dev work, when you're ready to deploy changes to production. Workflow: 1) ateam_github_patch (writes to dev) → 2) ateam_github_promote (merges dev→main) → 3) ateam_build_and_run (deploys main). Pass dry_run:true to see what's about to ship without merging. On merge conflict the call returns 409 — resolve manually on GitHub (open a PR or use the web UI), then retry.

  Connector

• get_job

  JobGPT

  Get detailed information about a specific job listing/posting by its job listing ID (not application ID). Use this to view the full job posting details including description, salary, skills, and company info. For job application details, use get_application instead.

  Connector

• guides.list

  AI Design Blueprint

  List application guides that show how Blueprint principles apply to engineering challenges (security, evaluation, observability, etc.). Use this to discover which guides exist before drilling in. Prefer guides.search when the user describes a topic or failure mode in natural language. Prefer guides.get when you already know the guide slug and need full detail.

  Connector

• tfoutputs

  InsideOut (Riley)

  INSPECTION: Retrieve Terraform outputs from a completed deployment Returns structured output values (VPC IDs, endpoints, cluster names, etc.) after a successful deploy. Sensitive outputs are redacted (shown as '(sensitive)'). By default returns outputs for the latest successful deploy. Optionally specify job_id to get outputs for a specific deployment. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id (specific deployment), lifecycle (filter by step e.g. 'cloud-provision').

  Connector

• check_status

  Fractera

  Read the current installation progress ONCE, on demand. Call this only when the user explicitly asks how the deploy is going (e.g. "what is the status", "did it finish") — never on a timer and never in a polling loop. The deploy takes 8-14 minutes and the authoritative status channels are the email pipeline + the dashboard; one read on request is enough. Returns the current step, the list of completed steps (~44 total in a full bootstrap), and whether installation is done or failed.

  Connector