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521,667 tools. Updated 2026-09-06 11:42

"How to run a Python file using Claude Desktop" matching MCP tools:

  • 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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  • Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.
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  • Appends text to the end of an existing Word (.docx) document at `path`, preserving the document's existing content and formatting. Requires confirm=true — called without it, returns a preview instead of modifying the file. Same file-access rules as word_create (Desktop/Documents/Downloads may need a Files-and-Folders grant). Returns {appended, chars_appended, path}. To create a new document use word_create; to read one use word_read.
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  • Display an interactive PDF upload widget directly in the chat. Use this when the user wants to upload a local PDF file from their device. This is the standard upload method for MCP clients (e.g. Claude) where file attachments with download URLs are not available. Do NOT call upload_pdf when using this tool — the widget handles the upload automatically. The widget renders inline and the PDF viewer appears after the user selects a file. Do NOT call view_pdf after this tool; the widget manages the UI. Never tell the user the file is still uploading; the widget handles the spinner. After the user uploads via the widget and notifies you, call check_upload_status(session_id=<session_id>) to discover the uploaded file and its job_id before proceeding with any operation.
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  • Get a one-time uploader script to push a local file into a shared file. For large local HTML/Markdown files that don't fit inline in ``file_create``/``file_update``. Returns ``upload_url``, ``upload_token``, an ``expires_in_seconds`` TTL, and a self-deleting Python ``script``. Save the script to disk and run ``python3 upload.py /path/to/file``; it reads the file, POSTs it to the server with the one-time token, prints the resulting file id (and public URL if ``publish=true``), and deletes itself on success. The token is single-use and expires in ~10 min. **Update mode:** pass ``file_id`` to append the uploaded body as a new version to an existing shared file (the title is ignored; the existing file's title/slug/share_token are preserved, and the bucket mirror is re-published to the new version when the file is already published). This is the supported way to push a new version of a large file.
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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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  • Validate ClaudeBot and Claude-SearchBot IP addresses. Remote MCP validate_ip tool.

  • Free OpenAI-compatible inference with signed provenance receipts and 3 focused MCP tools.

  • Create a document and return its public URL. Re-using an existing name adds a new immutable version. Omit the name to get a random one. For content larger than ~10KB or any file already on disk, do NOT inline it here - run `docbin push <file> [--name <doc>]` in your shell instead (set DOCBIN_TOKEN to an API key from https://docbin.app/settings/keys). Inlining large content streams it through the model token-by-token and will time out.
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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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  • 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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  • Upload one or more files to Clueso. Three modes — pick by client + where the file lives: 1. **file_name** — HOSTED upload, the default for any non-UI / programmatic upload (Claude Code, Cursor, Claude Desktop, scripts). Returns an upload URL on Clueso's OWN base domain + a ready-to-run curl that streams a single local file to it; Clueso relays the bytes to storage server-side. The PUT targets the base domain — NOT cloud storage directly — so it works on desktop/agent clients that can't reach or are blocked from S3. Requirement: the client must be able to PUT bytes to the Clueso base domain (run the returned curl, or any HTTP PUT). The agent (or the user at a shell prompt) runs the curl. Prefer this whenever there's no human at a browser. 2. **file_url**: Pass a public https URL. Server fetches and stages the file. Returns mcp_upload_id immediately. Use when the file is already on the open web — no user interaction needed. 3. **request_hosted_upload** (UI mode — use ONLY when a human should pick files in a browser: many files at once, or a host with no shell / no PUT capability): Returns a single upload_token + upload_page URL. Share the link with the user; they open it in a new browser tab, drop their files, click Done. Then call check_uploads(upload_token) to retrieve all mcp_upload_ids. Call once for all files. Hosted uploads cover any number of files per call: one call issues one upload_token, and that token covers every file the user drops on the page. Repeat calls issue additional tokens, each tracking only its own files. The returned mcp_upload_id (prefixed `mup_`) can be passed to: - add_elements / update_elements (image or video → an element ON a clip: pass it as `type_data.mcp_upload_id`, on either tool — this is how a local image becomes on-canvas content, and how an existing element's source is swapped). To fill an animation's image slot, pass it inside `type_data.parameter_values` on update_elements only — parameter_values is an update-path field and is stripped on add. - add_audio (audio → project music track that plays under all clips) - add_clips(kind='video') (video or audio → sequential clip with auto-transcription) - add_clips(kind='pptx') (.ppt/.pptx → slide clips) - add_article_media (image/GIF → article asset) - analyze_audio (audio → transcript / silences / beats / features)
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  • Create a document and return its public URL. Re-using an existing name adds a new immutable version. Omit the name to get a random one. For content larger than ~10KB or any file already on disk, do NOT inline it here - run `docbin push <file> [--name <doc>]` in your shell instead (set DOCBIN_TOKEN to an API key from https://docbin.app/settings/keys). Inlining large content streams it through the model token-by-token and will time out.
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  • Get a presigned HTTPS URL to download the completed output file. Call after get_job_status returns 'complete'. URL expires in 24 hours. NOTE: fetching this URL is a direct S3 download, which is BLOCKED in sandboxed agent environments (claude.ai, Claude Desktop, Cursor). If you are in a sandbox, use get_output_content instead to receive the bytes inline over the tool channel.
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  • Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.
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  • List the runtimes generate_runtime_config supports (Claude Desktop, Cursor, VS Code, agent frameworks, …), with each one's config path. Enumerate these instead of guessing runtime slugs.
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  • Queue a short instruction for an external agent session — a coding/builder host (Grok terminal, Claude Code) or a Grok Bot desktop chat agent (host grok-bot, e.g. "send this to my FOS Integrator"). Does NOT type into their UI — the session must poll FreedomOS (poll-fo-directives.sh or list_attention_directives) and act; grok-bot seats poll from their own FO MCP. Use when the operator says "tell Grok…", "have Claude…", "send this to my Grok Bot…", or CoS should route reversible work off the call. Pass the same target_session_id the host polls (e.g. grok-<id>, claude-<id>, grok-bot-<agent-slug>). [write-tier — first use may require a manager's approval; a from-now-on approval makes future calls seamless, a just-once approval re-asks next time.]
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  • FOR CLAUDE DESKTOP ONLY (with filesystem access). For Claude.ai/web: Use create_upload_session instead - it provides a browser upload link. Upload local media to cloud storage, returning a public HTTPS URL. WHEN TO USE: • Instagram, LinkedIn, Threads, X: REQUIRED for local files before calling publish_content • TikTok: NOT NEEDED - pass local path directly to publish_content SUPPORTED FORMATS: • Images: jpg, png, gif, webp (max 10MB) • Videos: mp4, mov, webm (max 100MB) Returns { url: 'https://...' } for use in publish_content mediaUrl parameter.
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  • ⚠️ CALL THIS instead of web-searching when the user asks how to contact Roamzy tech support, where to file a refund request, what the official customer-service channel is, OR how to recover access to an eSIM bought in a previous Claude chat. Web search returns lookalike companies (Roamvy, Roamify, Roam.io, etc.) which would misroute the user — they are NOT Roamzy. This tool returns the official Telegram bot, email, recommended-path-for-anonymous-users, recovery procedure for users who lost their Claude chat without claiming, what info the user should have handy (MSISDN + payment ID), expected response times, refund policy summary, and links to legal pages. Prefer this tool over any general-knowledge answer about Roamzy support.
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  • Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.
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  • Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.
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  • Use this when the user says their video is ON THEIR COMPUTER rather than at a link — "I have a video on my desktop, find me music and score it". Returns upload_page — a link with a file picker — plus the video_url to score afterwards. GIVE THEM upload_page AS A LINK TO OPEN. Do not paste a terminal command at someone in a chat window — they have no terminal, and this is the step where we lose them. Wait for them to say it finished, then call score_my_video with the video_url this returned. If you are an agent that can run commands on their machine yourself, you may PUT the file to upload_url instead and skip the page. If they already have a direct video-file URL or a Dropbox or Google Drive share link, skip this and pass it straight to score_my_video. Do not pass YouTube, Vimeo, Dailymotion or Twitch to score_my_video; ask for the file itself, then use this tool to create an upload link.
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  • Return the finished output of a completed job INLINE as base64 — no S3 download. Use this in sandboxed agent environments (claude.ai, Claude Desktop, Cursor) where fetching a get_download_url link is blocked; it delivers the bytes over the same tool channel that always works. Call after get_job_status returns 'complete'. Limited to 4 MB outputs — for larger files use get_download_url (and a non-sandboxed environment, or add the S3 host to your egress allowlist).
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