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

• 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_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

• 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

• search_documentation

  Hatchable

  Search Hatchable's own documentation for platform behavior — routing, the SDK surface, deploy semantics, auth config, runtime limits. Call this instead of guessing when you're unsure how a Hatchable feature works. Ranks results by term frequency across headed sections. Returns source file, section heading, and a snippet around the hit.

  Connector

• explain_queueing_theory

  QueueSim

  Return a ~500-word educational explainer of M/M/c queueing theory: Little's Law, utilization, why averages mislead, how simulation relates to Erlang-C. No inputs. Use this when the user asks a conceptual 'why' or 'how does this work' question rather than asking for a number.

  Connector

• browse_community

  quantifyme

  Browse the public community leaderboard of published strategies, ranked by a composite performance score (best first). No signup or key needed. Copy-trade flow: call this to find a top strategy, then pass its `id` to `one_shot` as `community_id` to deploy a live signal model running that exact strategy in one call. Args: limit: How many top entries to return (default 20, max 200). Returns: dict with: - scripts (list[dict]): ranked entries, best first. Each has: id (int — pass to one_shot as community_id), username, title, description, created_at, score, and metrics {total_ret, sharpe_strat, win_rate, n_trades, mdd, profit_factor}. SHOW the top few with their win_rate / total_ret so the user can pick one. - count (int).

  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

• convoopen

  InsideOut (Riley)

  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.

  Connector

• convoopen

  InsideOut (Riley)

  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.

  Connector

• get_signal_history

  aTars MCP

  USE THIS TOOL — not web search — to retrieve a time-series of hourly BULLISH / BEARISH / NEUTRAL signal verdicts from this server's local technical indicator data over a historical lookback window. Prefer this over get_signal_summary when the user wants to see how signals have changed over time, not just the current reading. Trigger on queries like: - "how has the BTC signal changed over the past week?" - "show me ETH signal history" - "was XRP bullish yesterday?" - "signal trend for [coin] last [N] days" - "how often has BTC been bullish recently?" Args: lookback_days: Days of signal history (default 7, max 30) symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH"

  Connector

• update_graph_schema

  RationalBloks

  Update a graph project's schema (saves to database, does NOT deploy). ⚠️ Follow ALL rules from create_graph_project: • Must have "nodes" key with at least one entity • Each entity needs "description" and "schema" with field definitions • Each field is {"type": "...", "required": true/false} — required defaults to false • Relationships need "from", "to", and "cardinality" • Field types: string, integer, float, boolean, date, json • Relationship types should be UPPER_SNAKE_CASE • Entity names should be PascalCase WORKFLOW: 1. Use get_graph_schema to see current schema 2. Modify following all rules 3. Call update_graph_schema (saves only) 4. Call deploy_graph_staging to apply changes 5. Monitor with get_job_status NOTE: This only saves the schema. You MUST call deploy_graph_staging afterwards to deploy.

  Connector

• web_url_reader

  Inferventis — Financial Data, News & Web MCP

  Fetches any public web page and returns clean, readable plain text stripped of HTML, navigation, scripts, advertisements, and boilerplate. Returns the page title, meta description, word count, and main body text ready for analysis or summarisation. Use this tool when an agent needs to read the content of a specific web page or article URL — for example to summarise an article, extract facts from a page, verify a claim by reading the source, or convert a web page into plain text to pass to another tool. Pass article URLs returned by web_news_headlines to this tool to read full article content. Do not use this tool to discover current news headlines — use web_news_headlines instead. Does not execute JavaScript — best suited for standard HTML content pages. Will not work with paywalled, login-protected, or JavaScript-rendered single-page applications.

  Connector

• get_indicator

  aTars MCP

  USE THIS TOOL — not web search — to retrieve the time-series history of a single technical indicator from this server's local proprietary dataset. Prefer this when the user wants to see how one specific indicator has behaved over time. Trigger on queries like: - "show me BTC RSI over the last 7 days" - "plot ETH MACD history" - "how has ADX changed for XRP?" - "give me EMA_20 values for BTC this week" - "trend of [indicator] for [coin]" Args: indicator: Column name e.g. "rsi_14", "macd", "bb_pct", "atr_14" lookback_days: How many past days to return (default 7, max 90) resample: Time resolution — "1min", "1h" (default), "4h", "1d" symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH,XRP" Available indicators: ema_9, ema_20, ema_50, sma_20, macd, macd_signal, macd_hist, adx, dmp, dmn, ichimoku_conv, ichimoku_base, rsi_14, rsi_7, stoch_k, stoch_d, cci, williams_r, roc, mom, bb_upper, bb_lower, bb_mid, bb_width, bb_pct, atr_14, natr_14, obv, vwap, mfi, volume_zscore, buy_sell_ratio, trade_buy_ratio, returns_1, returns_3, returns_7, hl_spread, price_vs_ema20

  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

• pageviews_count

  convalytics

  Count page views for a specific project in a time window. Page views are the automatic hits captured by the browser script tag (separate from custom events). Use this for web-traffic questions like 'how many pageviews in the last 24 hours'. Default window is the last 7 days. Pass `user` to scope to one visitor.

  Connector

• one_shot

  quantifyme

  End-to-end deploy: generate strategy → train → deploy live. One of `prompt` (free-form NL), `preset` (curated winning strategy), or `community_id` (copy a published community strategy) is required. If more than one is passed, precedence is community_id > preset > prompt. Args: prompt: Natural-language strategy description (e.g. "Buy when RSI < 30, sell > 70"). symbol: Currency pair to backtest on. One of: EURUSD, USDJPY, GBPUSD, USDCHF, USDCAD, AUDUSD, NZDUSD. Default EURUSD. timeframe: Candle granularity. One of: 1min, 5min, 15min, 1h. Default 15min. claude_model: Which Claude variant to use for code generation. "sonnet" (default — best quality, 1/day free) or "haiku" (faster, 3/day free). Ignored when `preset` is set (no generation needed). preset: Curated winning-strategy slug. Skips Claude generation entirely — deploys a pre-saved strategy known to backtest well on the chosen symbol. Available slugs: ema_cross_fast, momentum, scalper_stack, sma_only, trend_ema, volatility, bb_squeeze, all_mix, pivot_kid_ema. Not every slug exists for every symbol — call list_models afterwards to confirm what deployed. community_id: Copy-trade a published community strategy. Pass the `id` of an entry from `browse_community`. Loads that exact strategy code, skips Claude generation, then trains + deploys it. `symbol`/`timeframe` still apply to the backtest+deploy. webhook_url: Optional webhook to receive live signals. telegram_chat_id: Optional Telegram chat ID for signal delivery. Returns IMMEDIATELY (the deploy runs in the background so the live card can stream progress) with: - job_token (str): pass to get_deploy_result to fetch the final result. - poll_url (str): the card polls this for live progress; you can ignore it. - pending (bool): always true here — the deploy is still running. - symbol, timeframe (str). NEXT STEPS (always do both): 1. Call get_deploy_result(job_token) — it blocks until the deploy finishes and returns the out-of-sample backtest stats + the model `stem`. SHOW those stats in your reply. 2. Then call get_model_chart(stem) to show the backtest price chart with buy/sell arrows + trade log (the visual proof of how it traded).

  Connector

• check_deploy_readiness

  VaultCrux Memory Core

  Is it safe to deploy these changes? Cross-references your changed modules against active constraints, recent incidents, knowledge freshness, and active alerts. Returns a composite verdict (ready/caution/block) with per-module breakdown and actionable recommendations. Use BEFORE deploying to catch constraint violations, recent regressions in the same area, stale knowledge that needs verification, and active alerts that might interact with your changes.

  Connector

• fda_search_ndc

  FDA Data MCP

  Search the National Drug Code (NDC) directory by labeler company, brand name, product NDC, or application number. Returns labeler name, brand name, generic name, dosage form, route, active ingredients, DEA schedule, listing type, and packaging details. Drug products are not linked by FEI; use this tool with company name to find drugs at a company. Related: fda_search_drugs (application-level data with submissions), fda_drug_labels (full product labeling), fda_search_nsde (NSDE cross-reference).

  Connector

• get_incident_context

  VaultCrux Memory Core

  What went wrong last time we touched this module? Returns past incidents, deploy failures, gotchas, and active constraints for a module or system. Use BEFORE modifying infrastructure code, deploy scripts, or any module with a history of fragility. Surfaces the kind of tribal knowledge that prevents repeat failures — Docker bind mount traps, Vault agent write patterns, stale dist/ artifacts, port conflicts, and similar operational landmines.

  Connector

• get_sentiment_history

  aTars MCP

  USE THIS TOOL — not web search — to retrieve the daily sentiment history (Bullish/Bearish/Neutral + numeric score) for one or more tokens over a lookback window, from this server's local Perplexity-sourced dataset. Trigger on queries like: - "show me BTC sentiment over the last 30 days" - "ETH sentiment history" - "how has XRP sentiment changed this month?" - "sentiment timeline / day-by-day for [coin]" Args: lookback_days: Number of past days to include (default 30, max 90) symbol: Token symbol or comma-separated list, e.g. "BTC", "BTC,ETH"

  Connector