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472,885 tools. Updated 2026-08-24 03:41

"How to Run a Python Script" matching MCP tools:

  • Search the RoxyAPI knowledge base and get back ranked documentation snippets, each with a source URL. It covers API endpoints with their request and response fields, SDK usage for TypeScript, Python, PHP, C#, and the WordPress plugin, authentication and API keys, UI components, and step by step integration guides. Call this first whenever you need to integrate RoxyAPI into an app: to find which endpoint or SDK method to use, what parameters a call takes, how to authenticate, or how to wire a feature end to end. Pass the user question verbatim as `query`. If the first results miss, rephrase once and retry.
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  • Turn a script into a finished 1080p video — narrated, cut to matched stock footage, subtitles burned in, audio normalized to −14 LUFS. Returns a job id; poll get_job, then fetch_video. Use when you need a publishable video and have only text. $0.60 per video in USDC on Solana — no account, no API key, no human in the loop. script: narration body to speak (plain text) title: optional intro title shown for ~4s at the top voice: voice id from list_voices(), e.g. female_warm subtitles: burn-in subtitles (default true) speed: narration speed multiplier, e.g. 1.0 seed: stock-selection seed for reproducible visuals, e.g. 7
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  • Version history of an artifact's AI reviews (F5): every review run is a version with its score, model, cost, who/what generated it, and whether it's the current one. Read-only; returns the version list, empty when the artifact has never been reviewed. Use it to see how a feature/experiment/page's review changed over time and to pick the version_id to pass to revert_to_version. Takes the same target_id/target_type you'd pass to review_artifact.
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  • Answer what the user's project is — name, stack, how to run/test/build, auth, database, deploy, folder layout — from their files on disk, not from training data. ALWAYS call this before you invent npm/pip/cargo commands or read package.json yourself. ALWAYS call when the user says: what is this app, what's the stack, how do I run it, how do I test, is this a monorepo, where is auth, what database, how do we deploy. If they named Zephex or MCP, call this first on their project. One topic per call. Start with topic=identity on a new folder, then follow next_calls (usually run or framework). Other topics: backend, frontend, database, auth, deploy, structure, integrations, security. This is the user's machine, any project: Node, Python, Go, Rust, Java, PHP, a monorepo, an unsaved folder. Local/stdio: omit path to use the editor cwd, or pass path as their project folder. No disk on this transport: inline_files with package.json or pyproject.toml/go.mod/Cargo.toml plus 2–4 source files. Returns topic, summary, data (identity, commands, key_paths), hint, next_calls. Copy dev/test/build from data — do not guess bun vs npm vs uv. Not for finding a function name (find_code) or reading a file body (read_code). Those come after you know what the project is. Example: get_project_context({ topic: "identity" }) then get_project_context({ topic: "run" }). Also call topic=auth before touching login, topic=database before schema work, topic=structure when you need the folder map. force:true if the project just changed. Brief is enough for orientation; do not skip this tool to save a round-trip — one identity call replaces reading several manifests.
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  • 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.
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  • Browse individual decoded ads from Heista's corpus of real winning Meta/TikTok creative. Takes optional filters: vertical, creative_format, marketing_angle, hook_type, algo_intent, brand (partial name match), and limit (1-10, default 5). Each result returns beat timeline, classification, psychology, runtime performance signals (active days on Meta when available), and a decode id you can pass into generate_adscript with source_type="decode" to write a fresh script on that exact structure. Free, read-only, idempotent — no credits consumed. Use this when the user wants a specific ad as a script template (not an averaged formula), asks "show me winning ads in [vertical]", "what are [brand]'s top ads", or wants to see examples before committing to a generation. Source discovery surface — the response is the spine; for the full bundle with transcripts and director's read, call get_decode by id afterwards. Do NOT use to decode a NEW ad from a URL — use decode_ad (paid). Do NOT use for category-level patterns abstracted across multiple ads — use adformula_intelligence. Do NOT use to write the script itself — use generate_adscript or write directly from the bundle.
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  • 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.
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  • Call this when the user asks how much Bitcoin is vulnerable to a quantum computer, about quantum-exposed supply, P2PK coins, or Satoshi-era exposure. Returns the latest daily measurement from ByKaranteli's own Bitcoin Core node: exposed BTC and its share of held value and UTXO count, composition by script family, dormancy cohorts, the dormant-P2PK watch set, and provenance hashes (base_height, base_hash, txoutset_hash) so any figure can be re-verified against any node.
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  • Produce a focused pull-request review checklist for a language or stack. FREE. Covers the things that actually break in production, with extra items per language. Typical input {"language": "python"} returns {"language": "python", "checklist": ["...", ...], "note": "..."}. Use before a review, to decide what to look for. Not for reviewing actual code - pass code to review_diff or security_deep_dive. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
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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 runnable code that creates a Solana keypair. Solentic cannot generate the keypair for you and never sees the private key — generation must happen wherever you run code (the agent process, a code-interpreter tool, a Python/Node sandbox, the user's shell). The response includes the snippet ready to execute. After running it, fund the resulting publicKey and call the `stake` tool with {walletAddress, secretKey, amountSol} to stake in one call.
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  • Render a mingrammer/diagrams Python snippet to PNG and return the image. The code must be a complete Python script using `from diagrams import ...` imports and a `with Diagram(...)` context manager block. Use search_nodes to verify node names and get correct import paths before writing code. Read the diagrams://reference/diagram, diagrams://reference/edge, and diagrams://reference/cluster resources for constructor options and usage examples. Args: code: Full Python code using the diagrams library. filename: Output filename without extension. format: Output format — ``"png"`` (default), ``"svg"``, or ``"pdf"``. download_link: If True, return a temporary download URL path (/images/{token}) that expires after 15 minutes; if False, return inline image bytes. Defaults to True (URL) — set ``DIAGRAMS_INLINE_DEFAULT=true`` on the server to flip the default. SVG/PDF and PNGs larger than the inline limit always use a download link.
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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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  • **Use this whenever a task touches a live website.** It answers, definitively and cheaply, whether Bowmark can already DO the thing: look up current prices, check real availability or stock, search a site, get a quote or a fare, drive a configurator, start a booking, or pull anything that only exists behind a form, a filter, or a login. **Checking is cheap, so check.** One read-only call, no site is touched, and an unrecognized query returns a one-line index instead of an error, so the check never dead-ends and never costs you an attempt. If nothing fits, you have lost one cheap call and can use your normal approach. What comes back is the callable **function library** you write against: the runtime globals (`log`) PLUS, for each capability your query named, its namespace, TypeScript types, functions, and worked examples. Everything listed is real and callable. The language rules and how to run a script are on the `run` tool description. Pass `query` — what you want to DO (`"flights"`, `"price a GPU"`) or, if you have one in mind, the COMPANY or site (`"Kayak"`, `"newegg.com"`). A phrase in the user's own words is fine; it is matched against the whole library. **You get what you asked about and nothing else.** If nothing matches — or you send no query — you get instead a one-line index: pick whichever entry fits and CALL AGAIN with its name to get the types and examples you need to write a script. **Every response is bounded, and it says so when it is a slice.** A broad query can match more than one response carries; when that happens the answer opens with a partial-answer line naming what it left out. **Read it before concluding anything** — absence from a sliced list means nothing, and the fix is one narrower query (a single task, or a single company by name), which always returns that entry in full. Only an answer that does NOT say it is a slice supports the conclusion that a task is uncovered. **Two tiers come back.** CAPABILITIES (`bowmark.flights.search(...)`) are the default and usually what you want: one call fans out across several sites, dedupes, ranks, and routes around a site that's failing. PROVIDERS (`bowmark.providers.kayak.search(...)`) are the individual sites, callable directly — they appear only when your query NAMED a company, or when the capability has just one provider behind it. A direct provider call gets that site's own raw shape and no failover, so prefer the capability unless you specifically want that site. Loop: call `get_library` → write a JS script against the `bowmark` global → send it to `run`.
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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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  • Get the Designesy WCAG 2.2 AA accessibility verification framework: 11 conformance checks (a01-a11) plus a ready-to-run Playwright + axe-core 4.12.1 script template targeting your URL. Use this to audit a site for accessibility violations. When NOT to use: for a full design-contract score (not just a11y), use designesy_score. Does NOT run the scan — axe-core needs a real browser DOM. Returns the 11 checks + a Playwright script you execute locally (npm i -D @axe-core/playwright). The score comes from your local run, not from this tool. Returns JSON: { checks[{id (a01–a11), name, status: "PENDING_EXECUTION"}], playwright_script, install_command, run_command }. Pass config (JSON string) to customize axe.configure() — e.g. branding overrides, rule disables. Omit for standard WCAG 2.2 AA.
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  • For pipelines LARGER than 500 lead rows. Returns a complete, runnable Python script that loads the lead export in your sandbox and scores it locally (conversion probabilities, money layer, Shapley attribution, CALL / NURTURE / VERIFY queue). Makes no network calls — same shape as Customer Tiering — so Copilot Studio works even when outbound POST is blocked. The rows never pass through you as tokens. Needs numpy. Prints ranked decisions and headline figures; writes the full per-lead ledger to lead_pipeline_result.json. SAVE AND RUN THE RETURNED SCRIPT VERBATIM — do not retype, shorten, reformat, or reimplement it; only PATH / TOUCHES / STAGE_HISTORY / AS_OF / CURRENCY / OUT may be edited. Optionally takes touches_path and stage_history_path for engagement and funnel history.
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  • STEP 1 of 3 for the cheap sandbox path. Returns stdlib-only Python as `prep_code` (compact) and `prep_code_display` (readable, same logic), plus `reduction_spec` in words. Next: run one of those scripts (or your own from the spec) on the local CSV in your sandbox, then call customer_tiering_score_stats with the printed JSON. If the host needs code review first, show prep_code_display and wait for the user to say run/continue — then continue; do not invent a pause on hosts that allow silent runs. Python → MCP across turns is valid (ChatGPT Instant multi-step regressions are a host issue; Thinking/Auto or the next user turn still works). Do not invent rankings. If any step fails, fall back to customer_tiering with raw rows. This remote MCP NEVER reads the file — do NOT pass uploaded/attached file objects or platform upload handles. Call with no path args (preferred), or sandbox_path_hint as a plain string like 'book.csv'. Prefer this over customer_tiering when you have >~200 rows and a sandbox.
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  • Show a verse to the user. THE DEFAULT way to display/read a scripture verse: renders an inline card with the original script (centered), transliteration in the requested language, the word-by-word, and the translation — all at once. Use this whenever the user asks to see, read, open, or quote a specific verse ("покажи БГ 2.13", "read Bhagavad-gita 2.13"). The other verse_* tools are for fetching raw data; for DISPLAY prefer this one. Address by ref ("BG 2.13"), source+tokens, or id; lang sets the script + translation language.
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