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644,364 tools. Updated 2026-10-06 12:59

"How to execute a Python script" matching MCP tools:

  • Run arbitrary Blender Python (bpy) against a base revision and commit the result as a new immutable revision. Policy-gated: an owner must enable scripting on the deployment first. Runs in a credential-free, network-restricted throwaway container; the runner loads the scene before your script and commits it after. Assign a JSON-serializable value to a variable named `result` to get it back in job_get result.scriptResult with full finite JSON retained in an immutable report up to 8 MiB. Use job_report section=report for complete stored bytes; non-JSON or oversized results are explicitly unavailable. The result variable is not predeclared: no assignment records hasResult=false, while result=None records hasResult=true with JSON null. print() output returns as result.scriptStdoutTail — both are returned in the job_get/job_wait text, so use them for introspection instead of encoding findings into object names. A script that raises commits NOTHING: the revision is only written after the whole script succeeds, so failures leave the base revision untouched. The runner evaluates the depsgraph before your first statement, so world transforms of pre-existing objects are correct on entry; objects your script CREATES keep an identity matrix_world until you call bpy.context.view_layer.update(), so measure world positions only after that call — otherwise obj.matrix_world reads zeros and any floor, bounds or framing number you derive from it is silently wrong. The environment is FIXED (Blender 5.2 LTS) — verify identifiers with docs_bpy_lookup (free, instant) instead of a probe job. Call docs_operations FIRST: the validated DSL already covers 298 operations including booleans, lathe, loft, UV unwrap, modifiers, materials, lights, cameras and render settings, and scene_apply is cheaper and safer for all of them. Reach for scripting only for shader node graphs, novel parametric generators, and computation.
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  • Mint a one-time uploader script for a private Storage blob (any file type). Storage is the org's binary store: bytes go to the Railway bucket, metadata to ``drive_objects``. Objects stay private until a human seat calls ``storage_publish`` (agent seats must ``request_gated_approval(gate=publish)``). Document MIME types (Markdown, HTML, plain text, diagram JSON/YAML) are stored as blobs; versioned edit / ``/s/`` publish still use ``file_upload_request``. Pass ``work_id`` and/or ``project_id`` to attach after ingest (``kind=artifact``; transcripts stay on shared files). Pass ``parent_id`` to place the file in a folder. Batch uploads are multiple grants (one file each) — console multi-select uses sequential ingest, not a multi-file grant. Returns ``upload_url``, ``upload_token``, ``max_bytes``, and a self-deleting Python ``script``. Token is single-use and expires in ~10 min. ``drive_upload_request`` is a deprecated alias of this tool.
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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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  • Free. Quotes a comic before anything is made: script credits plus panel credits for a page count and panel engine. The quote assumes 4 panels a page, so it runs HIGH for short books (a 12-page book was quoted 462 and cost 301 because the script wrote 27 panels, not 48). The real panel price is known after the script: aetherwave_comic_status reports creditsToFinishDrawing, and aetherwave_comic_draw accepts maxCredits. Character references cost 6 credits each on top.
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  • 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 Go, 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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  • Get the Designesy WCAG 2.2 AA accessibility verification framework: 11 conformance checks (a01-a11) plus a ready-to-run Playwright + axe-core 4.13.0 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(), for example with branding overrides or rule disables. Omit for standard WCAG 2.2 AA.
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  • Run a read-only script against the connected Foundry world. `code` is the body of an async function: call foundry.<domain>.<method>({ ... }) (see docs), use await, console.log, and return the value you want back (JSON, size-limited). Only read methods are bound; writes are not available here — use execute. Every call counts against the per-script limits shown in the docs index (calls, wall time, CPU time, result size); an oversized call result arrives as { __truncated: true, bytes, limit, preview }.
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  • Use this when the user shares a link to a public Instagram Reel, YouTube Short or TikTok and asks why it went viral, how strong its hook is, or wants a script based on it. Analyzes why a short video went viral: the opening hook and its score, why it went viral, and a ready-to-shoot script adapted to the user, plus a caption and hashtags. Uses one analysis from the account's monthly plan (repeating the same URL does not). Returns the result if it is ready within a few seconds, otherwise a job_id - then call get_analysis with it after 30-60 seconds. Do not use without a video link, or to reopen a finished analysis (get_analysis).
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  • Use this when a local script needs a stored login's values. Let a local script read a credential: returns a single-use URL, valid for about 60 seconds, whose fragment is a token; a POST of `{ token }` to the URL without its fragment answers the values as JSON. `name` resolves to your own personal credential, else the company-wide one shared with you; when neither exists it fails `not_found`, and you send the person a `credential_request` link to add their own. Hand the URL to the script; never fetch it yourself. Every use is recorded. Error codes: invalid_input, forbidden, not_found, database_failure, conflict, storage_failure, maintenance, INVALID_ARGUMENTS, INSUFFICIENT_SCOPE, ORGANIZATION_MEMBERSHIP_REQUIRED, SESSION_EXPIRED, activity_unavailable.
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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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  • 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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  • 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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  • Attach a third-party script to a page, either by URL or as inline code. Use this for analytics, pixels, chat widgets and similar integrations rather than editing the page HTML, so the script survives page edits. Scripts are injected when the page is next saved in the editor or published; the change is not live on an already-published page until then.
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  • Heuristic estimate (NOT a real per-model BPE tokenizer) of how many tokens a piece of text will become, ported from GO AI's browser-side token counter. Blends a chars/4 and words/0.75 baseline with surcharges for punctuation, digits and non-Latin script; typically within ±10-15% for ordinary English prose and ±15-25% when non-Latin script is detected -- code and heavily punctuated text tend to tokenize denser than this suggests. Also reports whether the text fits a set of context windows (default: 8K/128K/200K/1M/2M tokens, or pass your own) and, if you supply per-million-token prices, an estimated USD cost. Use a provider's own tokenizer for exact billing figures.
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  • Import an open-source load test (JMeter .jmx, k6 .js, or Locust .py) into an existing script, converting it to Loadster commands. An import may create script assets, datasets, and a scenario; replace=true also replaces the script's existing commands and variables. Returns action items to review. For OpenAPI/Swagger or Postman, read the spec yourself and build commands with create_script instead.
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  • Starts writing the script: chapters, pages, panels, dialogue and captions. Returns immediately; it takes about 2 to 3 minutes, then aetherwave_comic_status shows the panels. Charged on actual use when it finishes (a 12-page script cost 40 credits). Approve character references first. Re-writing a script DELETES every page and panel already made, including drawn panels, so a project that already has panels needs replaceExisting: true.
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  • Find which documentation SETS exist whose NAME matches a substring (e.g. "python" → Python 3.x, "react" → React). Returns doc SETS, NOT their content — this does NOT look up a function/method/API name. To search inside a doc for an entry like "Array.map" or "fetch", use search_index (slug + query).
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  • Attach a DESIGNED DASHBOARD to a rig you own — a stored, self-contained HTML template rendered after EVERY run with that run's data injected. Without this an outside agent could author a rig but never give it a face, so its runs left a trace and nothing to look at. The template MUST contain the literal __ROKHA_DATA__ slot; at run completion every occurrence is replaced with the chosen step's JSON (script-context-escaped), so write `<script>const DATA = __ROKHA_DATA__;</script>` and build from DATA. RULES: fully self-contained (inline ALL CSS/JS, images as data: URIs — the stage frame blocks every network request, and external src/href are REFUSED at save), <=400k chars, LIGHT-FIRST: dark ink on a light ground the template paints on its OUTERMOST wrapper (background #fafbfd, ink #10161f, cyan accent #0b7fa8) — the stage embeds on a light canvas, so a dark page is unreadable there; at most one bounded near-black band as an accent, never the page ground. `data_source`: 'last_step' (default) or 'step:<tag>' to feed from a NAMED step — how a rig whose last step is a data-write still stages the step you care about. `clear: true` removes the stage. Requires Authorization: Bearer <JWT>; the rig must be yours.
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  • Run the first-run setup interview with the user. Returns a short interview script (how to address them, who they are, communication preferences, current focus) for you to ask conversationally and save with penny_write (entityType:"profile"), plus a custom-instructions snippet for the user to paste into their AI app so future conversations use these notes. Calling this marks onboarding complete (see penny_read target:"profile" `meta.onboarded`), so only call it when the user agrees to set up. Pass `client` when you know which app the user is in, to tailor the paste-here instructions.
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