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524,441 tools. Updated 2026-09-06 15:49

"How to run a Windows command" matching MCP tools:

  • WRITE to the Knowledge Base. This tool has TWO modes: **MODE 1 — SAVE a new card**: Provide `content` with full Markdown following the ACTIONABLE schema below. **MODE 2 — REPORT OUTCOME**: Provide `kb_id` + `outcome` ('success' or 'failure'). WHEN TO USE: - Mode 1: After successfully fixing a bug IF no existing KB card covered it. - Mode 2: ALWAYS after applying a solution from `read_kb_doc` and running verification. INPUT: - `content`: (Mode 1) Full Markdown KB card content — follow the EXACT template below. - `overwrite`: (Mode 1) Set to True to update an existing card. - `kb_id`: (Mode 2) ID of the card to report outcome for. - `outcome`: (Mode 2) 'success' or 'failure'. - `enrichment`: (Mode 2, optional) Additional context to merge into the card when outcome is 'failure'. ━━━ CARD TEMPLATE (Mode 1) — copy this structure EXACTLY ━━━ ``` --- kb_id: "[PLATFORM]_[CATEGORY]_[NUMBER]" # e.g. WIN_TERM_001, CROSS_DOCKER_002 title: "[Short Title — max 5 words]" category: "[terminal|devops|supabase|fastmcp|network|database|...]" platform: "[windows|linux|macos|cross-platform]" technologies: [tech1, tech2] complexity: [1-10] criticality: "[low|medium|high|critical]" created: "[YYYY-MM-DD]" tags: [tag1, tag2, tag3] related_kb: [] --- # [Short Title — max 5 words] > **TL;DR**: [One sentence — what's the problem + solution] > **Fix Time**: ~[X min] | **Platform**: [Windows/Linux/macOS/All] --- ## 🔍 This Is Your Problem If: - [ ] [Symptom 1 — specific symptom or error message] - [ ] [Symptom 2 — specific error code or log line] - [ ] [Symptom 3 — environment/version condition] **Where to Check**: [console / logs / env / task manager / etc.] --- ## ✅ SOLUTION (copy-paste) ### 🎯 Integration Pattern: [Global Scope] / [Inside Init] / [Event Handler] ```[language] # [One-line comment — what this code does] [depersonalized code WITHOUT specific paths, use __VAR__ for things to replace] ``` ### ⚡ Critical (won't work without this): - ✓ **[Critical Point 1]** — [why it's essential] - ✓ **[Critical Point 2]** — [common mistake to avoid] ### 📌 Versions: - **Works**: [OS/library versions where confirmed working] - **Doesn't Work**: [OS/library versions where known broken] --- ## ✔️ Verification (<30 sec) ```bash [single command to verify the fix worked] ``` **Expected**: ✓ [Specific output or behavior that confirms success] **If it didn't work** → see Fallback below ⤵ --- ## 🔄 Fallback (if main solution failed) ### Option 1: [approach name] ```bash [command] ``` **When**: [condition to use this option] | **Risks**: [what might break] ### Option 2: [alternative approach] ```bash [command] ``` **When**: [condition] | **Risks**: [what might break] --- ## 💡 Context (optional) **Root Cause**: [1 sentence — why this problem occurs] **Side Effects**: [what might change after applying the fix] **Best Practice**: [how to avoid this in future — 1 point] **Anti-Pattern**: ✗ [what NOT to do — common mistake] --- **Applicable**: [OS, library versions, conditions] **Frequency**: [rare / common / very common] ``` ━━━ END OF TEMPLATE ━━━ RULES for ACTIONABLE cards: 1. Solution FIRST — after diagnosis, code immediately 2. Depersonalize — no names, project names, or absolute paths 3. Use `__VAR__` markers for anything the user must replace 4. One Verification command, result visible in <30 sec 5. Fallback — 1-2 options max, always include When/Risks 6. Context at End — WHY is optional reading for curious agents
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  • Search the last 3 months of global news coverage (65+ languages) using the GDELT DOC API. Returns up to 250 articles with URL, title, source domain, language, country, publication date, and social image URL. Query supports full GDELT syntax: phrases ("bird flu"), boolean OR ((flu OR pandemic)), source country (sourcecountry:china), source language (sourcelang:spanish), domain (domain:who.int), GKG theme (theme:DISEASE_OUTBREAK), tone filter (tone<-5 for negative), proximity (near20:"flu virus"), and repeat (repeat3:"outbreak"). 250 is a hard per-call ceiling and GDELT offers no cursor: when a query fills it, split the run into narrower startDatetime/endDatetime windows — the response hands back the exact windows to use. Note: this API covers only the most recent 3 months — use gdelt_search_tv for historical TV transcripts back to 2009.
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  • Lists on-screen windows of any app with window_id, owning app bundle id + name, title, bounds (global space, top-left, points), display_id (the CGDirectDisplayID — matches list_displays, so you can look up which display a window is on), and is_focused. Window TITLES require Screen Recording permission — without it this returns an explicit permission_required error rather than a title-less result. Optional app_bundle_id filter — note that Electron-style apps often own their windows from a HELPER process with a different bundle id, so a filter can come back empty while the app is plainly on screen. on_screen_only DEFAULTS TO TRUE and excludes minimized, hidden and other-Space windows; pass false to see them. When the result is empty this tool returns a `note` explaining which filter emptied it and what to pass instead — read it instead of concluding the app has no windows. window_id is stable within the session for later targeting.
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  • Find earlier windows IN THE SAME SERIES whose shape resembles the current price action and return the FULL distribution of what followed (win-rate, median, min, max, n) over a fixed forward horizon. Parameters: window = how many recent bars form the shape being matched (default 32); horizon = how many bars forward each match is measured over (default 20). WHEN: an agent wants the historical spread of outcomes after a similar-looking setup, including how wide and how uncertain that spread is. WHEN NOT: you want the current technical picture (use brief), you want to find candidates across the market (use scan), or you need one expected value — this deliberately returns a distribution, not a point estimate. NOT a prediction, NOT a backtest of a strategy; past distribution does not guarantee future results. Example: {"ticker":"ETH/USDT","timeframe":"1d"}. Impersonal data, not advice.
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  • Get the exact commands for running the local crypto plane (the `@seekrit/mcp` npm server, the `@seekrit/cli` CLI, seekrit-run, seekrit-proxy), including a copy-paste .mcp.json that reuses this session's credential. Takes no arguments and returns prose plus config. Call this the moment you need to SET or READ a secret value — no tool on this server will ever return one. Use local_tool_for instead when you want the single command for one named operation.
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  • Commit, at the START of a run, to the criteria by which THAT RUN will be judged when it closes — before you can see how it turns out. This is how a run stops grading itself: once declared, a success ping whose body does not satisfy every declared criterion is recorded as a FAILED run with cause 'assertion', regardless of the exit code or what the ping claims. Call this right after your run's /start ping, before doing any work — see the assertions argument for the full, immutable contract, and get_ping_instructions' expectations_how_to for a worked example.
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    Destructive
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Matching MCP Servers

Matching MCP Connectors

  • Per-chain node health verdict: healthy / lagging / unreachable / listener-down. Computes how old each RPC node’s last block is — any non-BTC chain older than 10 minutes (BTC: 90 minutes, since BTC blocks every ~10m) is flagged as lagging or not syncing. Also checks the chain’s listener worker. When something is wrong it names the exact remediation (usually restart_payram_worker). Read-only — run this first; restart second; re-run this ~60s after a restart to confirm recovery.
    ConnectorNo auth
  • Get FX trading windows for FX execution timing and spread / rate optimization. Returns market sessions and liquidity windows for a currency. Use this to understand: - **Rate optimization** (primary, reliable use): higher liquidity means tighter spreads and better rates. Execute during peak windows to minimize conversion costs. - **Delay diagnosis** (use with care): the FX market session is when a currency TRADES. It is NOT a guaranteed processing schedule for an inbound foreign-currency payment that the beneficiary bank converts on arrival. Conversion timing is beneficiary-bank-specific (some convert in real time during the session, others batch once or twice daily), so do NOT tell the user a payment is "held until the next session" and do not quote specific hold durations ("adds X hours", "overnight delay"); those are bank policy and are not in our data. For the binding delivery-side cutoff that gates the converted local-currency leg, call country_banking_rules(destination) and read local_clearing.systems. When a currency is restricted, this tool's own output carries an inbound_processing_note with the accurate framing to quote. Pass a currency code to get its optimal window, or omit to get all market sessions and overlap windows. Args: currency: ISO 4217 currency code (e.g., "EUR", "JPY"). Omit to get all sessions and overlaps. Examples: fx_timing_advisor("EUR") fx_timing_advisor("JPY") fx_timing_advisor("INR") # Check INR conversion windows fx_timing_advisor()
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  • Get the current progress and results of one autopilot run. This is how you follow a run started by start_autopilot: call it with the runId, leaving time between polls. Reading status does not pause, stop, or alter the run in any way — it keeps going regardless, and there is no tool here to stop it. Reads only and costs no credits, however often you call it. Requires an API key. A run still in progress is a normal answer, not an error.
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  • First step of setting up a new data integration: creates a data spec. By default (sourceType "file") this returns presigned upload URL(s) for the sample file (and optional format/target-schema file) — upload the file(s) per the returned instructions, then call finish_data_source_onboarding with the returned specId to kick off AI analysis and wait for it to complete. Use sourceType "tables" instead when the request is to derive/aggregate data that is ALREADY loaded into workspace tables — e.g. "build me a daily summary of the customers table", or "set up a job that reads from the orders table and maintains a running total" — rather than loading a new file. It generates a SQL query (INSERT or MERGE, per `merge`) via AI instead of a Python parser, run through the query engine instead of a Glue job. There are never sample/format files, but targetOption still works the same three ways as sourceType "file" (see targetOption below) — so this call returns files: [] and you can call finish_data_source_onboarding immediately UNLESS targetOption is "target-schema-file", in which case it returns one upload URL for that file, same as the file-source path. The generated SQL automatically windows itself to rows added since the spec's last successful run. sourceType "tables" ALSO requires autoRefresh — how this spec stays up to date is not optional to decide, and must not be inferred from other jobs/triggers that happen to already exist in the workspace: ask the user whether it should re-run automatically whenever a specific upstream spec finishes loading ("spec_success" — the natural choice when the request is "run this after X finishes/loads"), on a plain cron-like cadence ("schedule" — the natural choice when the request is "run this every day/hour" with no mention of depending on another job), or stay manual-only ("none" — re-run later with run_data_job). If the request already states the timing unambiguously, that answers it; otherwise ask before calling this tool. Getting this wrong either way has a real cost: "none" means the summary silently goes stale until someone remembers to re-run it by hand, while an unwanted trigger keeps re-running (and charging credits for) a spec the user only wanted once. See autoRefresh below.
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  • Returns instructions for creating a DPF account, verifying its email, resending the verification code, or resetting a forgotten password — it never performs these itself and never asks for a password. A password typed into this chat would sit in the conversation transcript, so every action instead returns the DPF website's own form, or a curl command that reads the password from a shell variable the user sets themselves in their own terminal. Hand the command to the user to run — do not run it yourself even if you have shell access, since composing the export line would require seeing the password. action "register": requires email, firstName, lastName, and termsAccepted: true (only after the user has explicitly agreed to the DPF Terms of Service and Privacy Policy in this conversation). action "verify": confirm the 6-digit code DPF emailed after registration (requires otp). action "resend": re-send that code if it never arrived. action "forgot-password": request a password-reset code (requires email). action "reset-password": submit that code and set a new password (requires otp).
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  • Get the instructions for running a model eval with Ori, then follow them. Ori runs the user's own agent on their own prompts, on a pinned harness and model, and grades what it did — so a score change means the model changed, not the environment. Call this tool FIRST, before writing any eval code: it returns a step-by-step recipe (install and auth checks, how to spawn `ori code -p`, how to relay Ori's scoping questions to the user, how to report results) that you carry out yourself. Do not hand-roll an eval instead. Use it when the user asks which model they should use, wants to compare or bake off models, wants to measure whether their agent or prompt does the right thing, wants to catch regressions in agent behavior, or asks how good their current model is. Works for any codebase in any language. Do not use it for plain unit tests that involve no model, and do not use it to re-run an eval that already exists (run `ori eval <file>` directly instead). Takes no arguments; the same document is published at https://openrouter.ai/skills/spawn-ori-eval.
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  • Validates a Python automation script that runs OUTSIDE the game, on three axes: Python syntax (using the real interpreter), Minecraft commands embedded in the script (against the official command index), and the shape of the /connect WebSocket message envelope. For behavior pack scripts use validate_script instead — Python does not run inside a pack. The embedded command check is the most valuable one: a command written from memory can look syntactically fine and still do nothing in the game. Only strings starting with / are treated as commands. If syntax could not be checked, syntaxChecked is false in the result; ok:true alone does not mean the syntax is valid.
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  • Runs calculate_payslip for many employees in one call and returns the run totals: gross, employee deductions, net, and employer cost. Reach for this the moment more than two or three people are in play. A monthly payroll is not a sequence of unrelated questions — the employer share, the totals and the run id only mean anything across the whole run. Asking one employee at a time gives no total and no way to tell a retry from a second run. Put anything shared in defaults (prefecture, business_type, column) and let each row carry only what differs, which is usually pay and age. A row that cannot be computed comes back in errors with its index and id while the rest of the run completes — do not discard a whole payroll over one bad row. The reply carries a run_id derived from the route and the exact input, so sending the same payroll twice gives the same id. Nothing is stored, so a retry cannot double-count.
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  • Runs the annual 定時決定 for a whole payroll in one call, and reports which employees moved grade. 健康保険法第41条 puts every insured employee on the same schedule — the average of April, May and June pay, over the months with at least seventeen payment-basis days, applied from September to the following August. So June is the one month of the year when an office decides its entire payroll at once, and asking about one employee at a time is the wrong shape for the task. Reach for this the moment more than a couple of employees are in play. Each row returns the same judgement as decide_regular_remuneration, plus whether that person changed grade, which is what decides how much filing there is. Pass previous_remuneration to get that comparison; without it the answer is null rather than false, because "no grade to compare" and "did not move" are different facts. Pass acquired_on, left_on or revision_month and each row also says whether that employee is filed at all. 健康保険法第41条 leaves out anyone insured between 1 June and 1 July, anyone gone before the 1 July reference date, and anyone revised from July to September. The run totals to_file and not_required, which is the number of forms rather than the number of employees. A row that cannot be decided is returned in errors with its index and id, and the rest of the run still completes — do not discard a whole run over one bad row.
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  • Run the project's real test suite and return structured health — the same engine as the terminal command zephex check test. Detects bun, vitest, jest, pytest, go test, and cargo. Parses JUnit plus lcov (not a regex over stdout). Returns summary, a plain card (what broke, why clusters, coverage, warnings), fix_first, broken_areas, failure_clusters, coverage_by_area, and session_id. Not a file picker for what to edit. ALWAYS call after you edited source, when they ask if tests pass, what is failing, why tests failed, are we green, before commit, before push, or to re-run only the failed tests. PREFER this over running bun test, npm test, or pytest yourself and dumping logs. This already ran the suite, clustered the failures, and named the first file to fix. Workflow: task=detect sees the runner without executing (framework, command, test file count). task=run executes once and stores a session. Then task=failures, status, list, coverage, missing, why, or fix_prompt using session_id (or omit session_id to read the last run on this machine). Do not re-run the whole suite just to read failures. Read summary and fix_first first. On FAIL, call task=failures, then fix those files. task=why with a question explains clusters. task=missing finds changed source without tests. task=fix_prompt is a paste-ready brief. Pass diff_base: main after edits for failures_in_diff. area or file_filter scopes a later run. Local stdio: omit path (the editor cwd — their machine) or pass that project folder. Hosted: public GitHub URL or inline_files — not a local disk path. Does not modify source. Does not invent a runner if none exists. Does not choose product files to edit. Does not check npm packages. Does not scan a live URL. Example: check_test({ task: "run" }) then check_test({ task: "failures", session_id: "" }).
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  • Returns copy-paste-ready fix recommendations (nginx, Apache, DNS, shell) for the issues found on a domain the caller has already paid for — either an active Monitor/Compliance subscription covering the domain, OR a purchased one-off Report for the domain. Each recommendation carries a stable issue_id, a priority (high/medium/low), a title, prose instructions, one or more config snippets with the target domain already interpolated, a verify command, and a category tag. Use this when the user asks how to fix an issue, wants the exact config to apply, or needs to verify a fix worked. Pass the optional issue_id to scope the response to one specific finding. The response is read-only — this tool NEVER triggers a fresh scan; fixes are computed from the most recent stored scan (including the Report-included re-scan if that was used). Do NOT use this for domains the caller hasn't purchased coverage for — you'll get an upgrade_required error that links to the pricing page. Do NOT use this to run or trigger a scan; call scan_domain for anonymous checks. Requires a valid API key.
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  • Run a flow on demand. Works with Request triggers — HTTP request, manual Button, or PowerApps — and with RECURRENCE (scheduled) flows, which it runs immediately, the same as the maker portal's "Run flow" button; a scheduled flow takes no inputs, so passing `body` to one is refused rather than silently dropped. Pass trigger inputs as `body`. Returns httpStatus, response body, triggerKind, how it was invoked, and runName when the platform reports one (use get_live_flow_runs otherwise). Automated connector triggers (webhooks, "when an item is created") cannot be run this way — they only fire from their source event.
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  • Built-in product help — ask a natural-language "how do I…" question about Fastio and get a grounded, product-aware answer (or a short clarifying question) back in one call. EXPLAIN-ONLY / ADVISORY: it returns GUIDANCE TEXT and performs NO platform action (it will not create shares, move files, or change anything) — read the guidance, then act with the other tools. Answers are grounded in Fastio's own how-to knowledge AND phrased in terms of these MCP tools — they name the concrete `<tool> action="…"` calls to make — so prefer this over guessing endpoints or burning exploratory calls. For Q&A over YOUR uploaded files (RAG) use the `ai` tool instead — `how-to` answers questions about Fastio ITSELF. FREE and requires only an authenticated user (no org, no plan gate, no billing). Call action='describe' for the full action/param reference.
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  • POST /events/:eventID/free-slots — Find shared free time slots across attendees Computes shared **free slots** across a set of event attendees — the time windows where they're NOT in a bookmarked session or meetup. Use to find a coffee window with one DCer, or a junto-style lunch slot for a group. **Body**: `userIDs[]` (1-20), `minDurationMinutes` (default 30, min 15, max 480), optional `eventDayDate: YYYY-MM-DD` to scope to a single event day. **Slot grid**: derived from the event's session schedule, partitioned into `minDurationMinutes` windows. For each window we subtract each user's bookmarked sessions + meetup RSVPs. **Sort**: slots ranked by `len(freeFor)` desc — fully-shared windows first, then partial overlaps. **Auth**: caller must hold a valid ticket. Non-attendee IDs are silently dropped. ⚠️ WRITE operation: this mutates your DC account data.
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  • Historical directional hit-rate of each intelligence source (free read). Returns per-source accuracy stats for the intelligence signal sources (Coin Bureau / YouTube, RSS, Fear & Greed API, on-chain), so an agent can weight a source's calls by how often its bullish/bearish reads have played out. Each source carries a ``windows`` map over rolling 7d/30d/90d periods, each with total resolved calls, correct/incorrect counts, and accuracy_pct; sources with no resolved calls yet are omitted, and results are ranked by longest-window accuracy. windows optionally narrows the periods (subset of [7,30,90]); source_type optionally filters by source kind (youtube, rss, api, on_chain). Accuracy = the directional call vs the realised Birdeye price over the prediction window. Past accuracy is not a guarantee. Not financial advice. Workflow: INTELLIGENCE step -- pair with get_market_briefing to discount or trust a signal by its source's track record before sizing a position.
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