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606,390 tools. Updated 2026-09-24 07:42

"How to view console logs in a development environment" matching MCP tools:

  • Get build and runtime logs for a deployment. If no deployment_id is provided, returns logs for the latest deployment. Use this after calling deploy to monitor build progress and diagnose failures. Logs include: framework detection output, dependency installation, build steps, container startup, and health check results. If a deployment fails, check the logs for error details — common issues include missing dependencies, build errors, or the app not listening on the correct PORT (check the PORT env var — 8080 for auto-detected frameworks, or the EXPOSE value from Dockerfile).
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  • Google Search Console trend view for the website's tracked keywords, refreshed at the Search Console sync cadence: compares the last window_days (7, 28 or 90, default 28) with the window_days before it. Per keyword: clicks, impressions and impression-weighted average position in both windows plus the deltas (current minus previous), and the page currently ranking for it. by_page groups the clicks and impressions of every keyword with data by that page, most decayed first (clicks_delta ascending), so it answers which pages are losing traffic. Pass keyword_ids (max 200) or let it consider every keyword with history; rows are sorted by clicks_lost (default), clicks_gained, position_lost or position_gained and cut to top (default 50, max 200). by_page is computed before the cut. Rows use each keyword's snapshot country, or the country you pass (lowercase alpha-3 like usa, or wwd). Keywords without history in either window are omitted; a position is null when its window has no impressions. Search Console data lags 2 to 3 days, so the newest days of the current window are usually missing. Not for the current position or ctr (use get_keyword_rankings) nor volume and difficulty (use list_keywords). Tells you when Google Search Console is not connected instead of returning empty rows silently. Pass website_id when the account has several websites (see get_account).
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  • Returns a structured snapshot of the LMCP environment: server/tray/teams-proxy versions, detected AI client, cloud relay state, TCC permission states (Calendar/Reminders/Contacts), and a compact summary of which services (Mail/Calendar/Contacts/Teams/OneDrive/Reminders/Notes) are reachable. Fast (<500ms), passive — never prompts the user, never opens app windows, never touches the network. Call this when you need to verify the environment is healthy before attempting a tool, or to understand what's installed and accessible. If `services.scan_pending` is true, the background service scan hasn't finished yet (just after startup) and the per-service running/accounts values are placeholders — do NOT treat them as a real outage; just call the tool you need. Otherwise `services.scanned_seconds_ago` tells you how many seconds ago that scan ran (cadence ~60s): the per-service values are a snapshot, NOT a live probe. A `false`/`0`/`not available` for a service is advisory only — it can be stale (e.g. the user connected WhatsApp or opened Mail seconds ago) — so never use this tool as a preflight gate to skip or cancel a task; the actual tool call is the source of truth, just attempt it. For reporting failures, use `report_problem` instead — it captures this same snapshot plus logs and submits to the team.
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  • Raw log lines — for when the `diagnose_deploy` analysis was not enough. Start with `diagnose_deploy`: it has already pulled out the cause and the neighbourhood of the fatal line. Come here when you need to look with your own eyes — the cause is vague, or you are interested in how the app behaves rather than in a failure. You get the TAIL. `truncated` says earlier lines were dropped; do not present the tail as the whole log. For build logs, package-manager network chatter is hidden BEFORE the tail is cut, so the tail holds meaningful lines; `noise_hidden` says how many were hidden and `include_noise=true` shows them.
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  • Raw log lines — for when the `diagnose_deploy` analysis was not enough. Start with `diagnose_deploy`: it has already pulled out the cause and the neighbourhood of the fatal line. Come here when you need to look with your own eyes — the cause is vague, or you are interested in how the app behaves rather than in a failure. You get the TAIL. `truncated` says earlier lines were dropped; do not present the tail as the whole log. For build logs, package-manager network chatter is hidden BEFORE the tail is cut, so the tail holds meaningful lines; `noise_hidden` says how many were hidden and `include_noise=true` shows them.
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  • Find logs matching filter criteria within a time range. Use this as your default starting point for log queries. Returns logs sorted by (timestamp, logId) descending (newest first). Returns the log's main fields by default; pass verbose=true to include its attributes (http/url/… flattened in, plus a `resource` object). Long string values are capped (maxStringChars). For raw columns or custom selection use run_sql. For the full untruncated body of one row, use get_log. Defaults: from/to: open window if omitted — beware of unbounded scans limit: 100 (max 1000) service/level: any Common patterns: - Errors in the last hour: level="ERROR", from=<1h ago> - Logs for a trace: traceId="abc123..." - Whole-token search (case-insensitive): messageContains="timeout" - Substring or regex search: not supported here; use run_sql Returns: logs: array of log objects (lean unless verbose=true) nextCursor: opaque token (null on the last page); pass back as cursor to fetch the next page explorerUrl: shareable Fixter UI link opening this query in the log explorer — attach it when citing these logs as evidence to the user (covers the service/level/traceId filters and the window; timestamps display in the viewer's browser timezone) queryStats: rowsReturned, elapsedMs
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Matching MCP Servers

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    An MCP server that builds a local webpage to view AI plans, reports, and diagrams, so the AI only needs to send a URL pointer instead of long text. It provides three tools (view_plan, view_report, view_diagram) and tracks project changes for diff viewing.
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  • F
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    MCP server that provides tools to access Google Maps Environment APIs for air quality, pollen, and weather data, enabling queries like current conditions, forecasts, and history.
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Matching MCP Connectors

  • THE APPLICATION'S OWN LOGS - what `docker logs`/`podman logs` would show for each container in a deployment. This is the tool for 'it deployed fine but it does not work': a 500, a crash loop, a failed DB connection, a missing env var all announce themselves here and NOWHERE else. ⛔ DO NOT use get_instance_logs for this. That returns the VM's SERIAL CONSOLE (kernel messages and cloud-init), which answers a question nobody debugging an app has - and on this platform it goes permanently silent once the machine finishes booting. build_log does not contain runtime output either; it stops when the build does. Default depth answers instantly from the VM's last report; a bigger `tail` or any `since` asks the VM for a fresh pull and takes up to ~15s. Secret-shaped values (PASSWORD=, TOKEN=, API_KEY=) are redacted in transit.
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  • 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.
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  • MONITORING: Quick status check for Terraform deployments Check the current status of a Terraform deployment job. Use this tool to quickly check if a deployment is running, completed, or failed. Returns job status, job_id, and other metadata without streaming logs. Use tflogs to stream the actual deployment logs. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs). **LIVENESS**: The response carries two distinct timestamps: - `updated_at` — last semantic change (only bumped when status / drift / version actually differ). Useful for sorting deployments; NOT a per-poll heartbeat. - `last_refresh_at` — last successful Oracle decode (stamped on every poll where reliable reached Oracle, even if nothing in the row changed). Use this to confirm reliable is still actively talking to Oracle for a long-running RUNNING job. Absent on rows that haven't been refreshed since the column was added. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.
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  • Primary reporting tool for a given GA4 property or site. Use for totals, trends, and breakdowns by dimension across GA4 website traffic and app analytics, Google Search Console site traffic, and Bing Webmaster — including last-30-days summaries, revenue, leads, sessions, users, engagement/time-on-page (average_session_duration, user_engagement_duration), and period-over-period comparisons. Drill deep: GA4 supports up to 9 grouped dimensions (date/hour, geo, device/browser/OS, source/medium/channel, landing_page/page_path, etc.). Defaults to all mapped connected sources merged into one standardized view, aligned on the shared grain (typically landing_page) so a page row blends GA4 sessions+engagement with Search Console/Bing clicks/impressions/CTR/position; per-source detail (e.g. full query lists) stays in sourceSections. Note GA4 has no `query` dimension and Search Console/Bing have no sessions/engagement, so those cannot share one row — query is a Search Console/Bing breakdown. Narrow with sources or sourceMode='single'. Any GA4 dimension/metric name not in the catalog is passed through to the GA4 API automatically; metricMode='source_native' forces a pure GA4-native report. Pass one date range for a single window or two date ranges for period-over-period comparison.
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  • 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.
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  • MONITORING: Quick status check for Terraform deployments Check the current status of a Terraform deployment job. Use this tool to quickly check if a deployment is running, completed, or failed. Returns job status, job_id, and other metadata without streaming logs. Use tflogs to stream the actual deployment logs. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs). **LIVENESS**: The response carries two distinct timestamps: - `updated_at` — last semantic change (only bumped when status / drift / version actually differ). Useful for sorting deployments; NOT a per-poll heartbeat. - `last_refresh_at` — last successful Oracle decode (stamped on every poll where reliable reached Oracle, even if nothing in the row changed). Use this to confirm reliable is still actively talking to Oracle for a long-running RUNNING job. Absent on rows that haven't been refreshed since the column was added. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.
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  • [Requires authentication] Call the authenticate tool first to start or confirm 1inch Business login (initialize 200 is still anonymous). If authenticate or this tool returns HTTP 401, complete OAuth, then retry. Look up production API request logs for your 1inch Business organization to troubleshoot integration issues. Results are always scoped to your authenticated organization. Two modes: 1) By request id: pass requestId (the x-request-id header returned on 1inch API responses). Optionally narrow startTime/endTime (defaults: last 24 hours ending now). 2) Logs in a time window: omit requestId and pass both startTime and endTime (RFC3339). Optionally set logLevel ("info", "warn", or "error") to filter by severity; omit to return all levels. Limits: each call covers at most a 24-hour window; how far back you can query depends on your plan's log retention.
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  • Create a **share / integration entry point** for an agent — this is how end users actually reach it. **`published=True` only means "visible", not "reachable"**: for end users to talk to the agent you must create a share. The response carries a directly openable chat link (`{public_base}/s/<token>`) and the website embed URL (`{public_base}/embed/<token>`). For a website widget, paste one line before </body>: `<script src="{public_base}/embed.js" data-token="<token>"></script>`. label names this entry point ("website widget", "support link"). Telegram/WhatsApp and other channels are connected separately on the agent's Integration page in the console. **No website?** Hand the returned `chat_url` or `qr_url` (QR code) straight to the tenant: print it on business cards / flyers / in-store; scanning opens a full-page chat, no login, returning visitors are remembered per browser. **For links you give to humans, prefer `pretty_url`** (when present in the response): `{public_base}/t/<tenant alias>/<agent alias>` — memorable, printable, survives token rotation. No pretty_url = aliases not fully set — **fix that proactively**: agent alias via `create_agent`'s alias param or `PUT /agents/{name}/alias`; tenant alias in console → Settings. The `/s/<token>` link still works, but it is the machine/embed form, not one to read out to a person.
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  • Discover the queryable fields, functions, and measures for a data source. Use this before run_sql to learn what's available. Sources: logs, spans, metrics. Default: logs. Call with NO arguments to start — you get the list of services (with volumes) plus the field profile for logs. Then optionally pass service=<name> to drill into one service's fields (different services emit different dynamic attributes). Per field: type, coverage, distinct-value estimate, top values (low-cardinality), and a GROUP BY verdict (safe / with care / filter only). Dynamic attributes are the ACTUAL keys in your data — use them directly in QuerySQL (e.g. SELECT http_method FROM logs). Resource-level attributes (logs and spans only) use a resource. prefix, e.g. resource.service.name. Always returns the source's measures (fn, label, unit, defaultMode — the mode a new alert rule on this measure should default to) and the available QuerySQL functions with their argument counts. For source=metrics, the metric list is volume-ranked and bounded to a default page; metricsMatched reports the true total independent of what was returned. Pass prefix=<text> to reach past that default page into the tail, e.g. prefix="http." for HTTP metrics. Optional filter=<predicate> restricts discovery to matching rows. The predicate is QuerySQL and uses the same field names as run_sql (e.g. level = 'ERROR', http_method = 'GET'); subqueries are not allowed.
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  • One-shot cross-signal pivot for a trace id. Given a trace id, returns (all fields top-level, no nested summary object): rootOperation, spanCount, errorCount, totalDurationNanos, startTime — trace summary spans — every span in the trace (up to 1000) logs — logs tagged with that traceId (no window limit, up to 1000) exemplars — metric exemplars whose traceId matches, within the span window (up to 1000) windowFrom / windowTo — the derived scan window (earliest span - 5s / latest span end + 5s) The window is derived from the trace's spans. If the trace is unknown, spans and exemplars are empty but logs are still returned if they carry the traceId. Exemplar filtering is window-bounded; log filtering is not. Use this as the primary entry point when you have a trace id and want to see all correlated signals at once. Returns core fields by default; verbose=true flattens attributes in for both spans and logs (plus a `resource` object) and long string values are capped. Use run_sql for raw columns or custom selection. After reviewing the result, drill into individual signals with logs, spans, or metrics as needed. Long-lived traces (scheduler ticks, batch jobs) can produce very large verbose responses even with the caps. Prefer verbose=false first; for error triage, the logs tool with traceId + level is a cheaper, targeted alternative. Pass maxStringChars to tighten string truncation per call. Returns: traceId, traceUrl, rootOperation, spanCount, errorCount, totalDurationNanos, startTime, windowFrom, windowTo, spans[], logs[], exemplars[], queryStats. traceUrl is a shareable Fixter UI link for this trace — attach it when citing the trace as evidence to the user.
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  • Convenience tool: generate a new XRPL keypair and return the seed. ⚠ NOT RECOMMENDED FOR PRODUCTION. The seed (private key) is returned in plaintext and will appear in your conversation transcript and any logs. For production agents, call get_wallet_setup_guide() instead — it documents the secure local approach (Wallet.generate() + seed in .env) where the seed never leaves your environment. Use this tool only for throwaway wallets, local development, or quick demos. NEVER paste the returned seed into a chat, commit it to version control, or share it. The wallet is NOT yet active on the ledger — fund it before use. XRPL requires 1 XRP minimum to activate a wallet (base reserve). Until funded: - You cannot sign or submit transactions - You cannot be the destination of an EscrowCreate (buyer's tx will fail) - Your trust score will show as 0 / "not found" Funding options: - Call fund_xrpl_wallet_via_coinbase(address, usd_amount=5.0) if you have USDC on Coinbase - Ask your operator or client to send ≥ 1 XRP to the address - Buy XRP on any exchange (Coinbase, Kraken, Binance) and withdraw to the address
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  • Standings for one league season: one row per team with position, played, W/D/L, goals, points, last-five form, plus expected points and a luck category (how far results run ahead of or behind the underlying numbers). Use for "who is top", "how many points", "what is the form", or any question about the table as ranked by points. view="luck" re-orders the same rows by over/under-performance (who is lucky, unlucky, flattered by the table); view="goals" by scoring. For one team in depth use get_team; for how the season is projected to END use get_season_projection. Omit season for the current one. Example: "Is Hull really a top-four side?" → get_league_table premier, view=luck, compare points with expected_points.
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  • Publish the generated application for a model you own — the single-file HTML a generator produced from the model's `view` prompt. Served at /app/<id> in a sandboxed opaque origin (no cookies, no session; only the CORS-open public API is reachable). START FROM THE RUNTIME, not from scratch: /lib/app-template.html is a working console that imports /lib/sim-console.js and composes <sim-controls>, <sim-disruptions>, <sim-net>, <sim-timeline>, <sim-trajectory> and <sim-results> — the same components the generic console at /whatif/ runs. Composing them is how an app inherits role derivation, the fungible-set collapse, the influence ranking that never filters, the contention ledger and the verbatim caveats, none of which the checks below can verify you reimplemented correctly. Root-relative /lib/ imports are allowed; off-origin ones are refused. Checks refuse an app that is empty, oversized, never references its model id, or loads external scripts/styles; behavioral correctness (does the app actually do what the view says) is on the generator and any browser gate you run.
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    Destructive
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  • Collect console logs, exceptions, and log entries from a page running on the device for a time window. Enables Runtime and Log domains, then listens for Runtime.consoleAPICalled, Runtime.exceptionThrown, and Log.entryAdded events. Returns a normalized array of { level, text, url?, lineNumber? } entries. This is a LIVE-WINDOW collector: it only captures events fired AFTER it attaches, so triggering the logging from a SEPARATE tool call races the ~1-3s attach latency and is silently missed. To capture logs from an action, pass triggerJs (runs inside the window). Default window: 3 000 ms. Maximum: 15 000 ms. Omit pageId to auto-select the visible/active page.
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  • Start a temporary, email-free Pulse trial. Returns a short-lived private API key with enough banked tokens for one short URL-based v1 analysis. No Stripe Checkout session is created. Store the key in the client or a trusted local environment; do not repost it in public logs, screenshots, GitHub issues, shared chats, or directory examples.
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