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553,292 tools. Updated 2026-09-12 18:39

"A sandbox environment to run Python code and install dependencies" matching MCP tools:

  • Run a short Python program in an isolated sandbox and get back exit code, stdout, stderr, duration and up to 3 artifact files from ./out/. Python 3.12 + numpy/pandas/requests, no network, 512 MB, 30 s max, 32 KB code, 4 input files of 256 KB. Free compile-only syntax check first at POST /api/v1/run-python/validate. $0.05/call (one run) via x402. Free syntax check: POST /api/v1/run-python/validate. Free worked example: GET /api/v1/run-python/example.
    ConnectorNo auth
  • Run Python code from natural language prompts, with optional PyPI package install
    ConnectorNo auth
  • An outside check on code, executed in a sealed sandbox. Call it before code crosses a consequence boundary: before you merge it, deploy it, publish it, settle a payout on it, or report it done. A self-audit verifies consistency, never completeness: a check written inside the frame that produced the code passes on the code's own assumptions. This is the check that is not you. Also call it when a fix passes your own check but the target still fails; that means your check shares the code's assumption and cannot see the error. INPUT: code (JavaScript/Node or Python 3 source, deterministic only) plus ONE of: contract {fn, examples:[{call,expected}]} (copy call and expected from the test or spec the consequence depends on), or assumption (plain-language claim, weaker read). It checks the code against the contract exactly as given. VERDICTS (synchronous): BROKE: the code violates your contract, with the exact input and a rerunnable proof; do not proceed. HELD: the code meets the contract you gave; proceed on that contract, and nothing more. FINDINGS: a stated property strains under a generated input; check it before proceeding. DROP: not deterministically checkable. PAYMENT: 0.10 USDC per call, x402 v2 on Base, no account. Every delivered verdict is charged, HELD and DROP included. If no verdict is produced, the payment authorization is cancelled and you are not charged.
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  • Check Python source without running it: parse, lint (ruff), type-check (mypy), AST security policy, credential scan. Safe on code you do not trust. Use it on every Python file you generated or edited, before writing it to disk. Alternatives: repair_python to get the corrected source instead of the diagnosis; execute_python to prove the code runs. Auth: a key is required. A free key covers this call, 25 per day, then HTTP 429; get one with POST /v1/keys. Credits are bought without an account, 1 per call: GET /v1/pricing says where to send the xDAI. Or pay for this one call with no key at all: call it without one and the result carries x402 payment requirements ($0.01 in USD Coin on eip155:8453); sign them and repeat the call with the payment in _meta['x402/payment']. Arguments: code: the whole file, 1..200000 bytes of UTF-8 measured after encoding (empty is refused with 400, larger with 413); a fragment is fine, but line and column numbers in the answer count from 1 in what you sent. language: must be 'python'; anything else is 400, and the field may be omitted. Of options only transpile_to (e.g. 'javascript', which returns a translated copy in transpiled) acts here; timeout_s, max_iterations, optimize, examples and expected_output need a pass that rewrites or runs the code, so send code alone. Ignored options are not refused, so a call that sets them looks like it worked; and code that does not parse is answered rather than refused: valid=false with the syntax error located, which is the point. Returns valid, score 0..1, diagnostics (rule, message, line, column), security findings, fixes, fixed_code and runtime; see outputSchema. The code and its verdict are retained to improve the service.
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  • Write or replace one TEXT file in a bucket (HTML/CSS/JS/JSON/SVG/Markdown/etc.). Write ready-to-serve static files. JavaScript is supported and published as-is; Revdoku does not install dependencies or compile project source. Pass expected_bucket_revision_id from bucket_get when practical to avoid overwriting concurrent edits. Binary assets and whole local folders use the CLI or REST direct-upload API. The response's bucket carries dashboard_url (and public_url when published); show that link to the user instead of the raw bucket id.
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  • Vaaya's consultant. Describe ANY external capability you or the user might want — generate an image/video, search or scrape the web, run code in a sandbox, send/receive email, enrich a contact — and it helps figure out the best way, teaching the user what Vaaya can do. It is CONVERSATIONAL and remembers prior turns. It returns: mode='converse' (a reply to RELAY to the user verbatim — questions, options, ideas; get the user's response and call consult again with it, so the conversation continues), mode='call' (an ordered list of calls to run via `use`, with a message explaining the preferred choice + alternatives + why; multi-step results may contain placeholders like '<from step 1: sandbox_id>' — run earlier steps first and substitute), or mode='unsupported'. Every reply includes `suggestions` (2-3 things to do next) — surface these to the user. AFTER you run a `call` result's calls via `use`, call consult ONE more time with a short note on the outcome (what was produced / any failures) — it returns result-aware, Vaaya-grounded next steps to offer the user (the `call` result's `after_running` field reminds you). Call consult whenever you hit a capability gap or the user wants to know what's possible. It does NOT execute or bill — you run returned calls via `use`. ALWAYS show the user consult's `message` and `suggestions` and let them steer.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    F
    maintenance
    Enables secure execution of Python code in a sandboxed WebAssembly environment using Pyodide and Deno. Automatically handles package management and captures complete execution results including stdout, stderr, and return values.
    194
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    Enables execution of Python code in a safe environment, including running scripts, installing packages, and retrieving variable values. Supports file operations and package management through pip.
    8
    Apache 2.0

Matching MCP Connectors

  • Run Python code from natural language prompts, with optional PyPI package install.

  • Proves AI-generated Python does what you asked: lint, types, security, sandbox run, exact fixes.

  • 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.
    ConnectorNo auth
  • [free] Describe this connector: flagship-first tools layer (search/answer as the front door), how to install (Claude Code / Cursor / npm), free vs paid tiers, and discovery URLs. Call this first.
    ConnectorNo auth
  • 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.
    ConnectorOAuth
  • Get ONE test case's result inside a run: status code, timing, the response body and headers, every assertion outcome, extracted variables and any script output. This is the read to make when a run failed and you need to know why. The id comes from get_test_run's results[].id, not from get_test_results (run ids). Requires project context.
    ConnectorAPI key
  • 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.
    ConnectorNo auth
  • Run the Apier dry-run validator against a proposed regulatory action without producing ANY upstream side effect — no Maskinporten call, no Altinn / Skatteetaten / NAV submission. Use this BEFORE the live execute path to catch missing delegations and payload-shape errors at zero upstream cost. The verdict carries five prerequisite check slots (each pass / fail / skipped), the overall `valid` boolean, the DRY_RUN_DISCLAIMER (a pass is NOT a guarantee of upstream success), and the preview echo `would_be_payload` + `preview_notice`. Inputs match the /v1/actions/execute body: { org_number (9 digits, MOD-11), action_type (`mva_melding` | `a_melding`), period, payload }. The nested `payload` object is intentional - it mirrors the upstream government payload schema for the action, so it is not flattened. Failure modes: SCOPE_INSUFFICIENT (needs read:actions), VALIDATION_FAILED; the validator never throws. To actually file a (sandbox) VAT return, use submit_vat_return instead. No sandbox mirror — under a sandbox bearer call submit_vat_return instead. Docs: https://www.apier.no/docs/guides/mva-filing
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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.
    ConnectorOAuth
  • Delete every email captured in the sandbox. The sandbox holds messages intercepted during testing so they are never delivered to real recipients. This DELETES ALL of them and cannot be undone — but it touches only intercepted test mail, never sent campaigns, real inbox messages, contacts, or templates. Takes no parameters and offers no filter: it is all or nothing. Requires an API key. Clearing an already-empty sandbox is harmless. Read anything you still need from the sandbox before calling this.
    Connector
    Destructive
    No auth
  • Run a sandbox backtest of strategy code without persisting anything. This is the fastest way to test a strategy. The code is run through static checks and a full backtest on historical data, but no Strategy or StrategyVersion rows are created. Use this for rapid iteration. Args: code: Python source code implementing the Strategy contract. Must define a METADATA dict and a class extending Strategy with an on_bar(ctx) -> Signal method. See CREATOR_API.md. domain: Trading domain (e.g. "eth_usdc", "btc_usdc", "sol_usdc"). symbol: Price symbol for historical data (e.g. "ETHUSDT"). user_id: Identifier for trial tracking (used for DSR correction). Returns JSON with: success, metrics (sharpe, sortino, win_rate, total_trades, return_bps, max_drawdown, regime_breakdown, exit_reason_breakdown), or error details if validation failed.
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  • Validate YOUR OWN draft BEFORE submitting it — the same checks the submit gate enforces, surfaced up front so you can fix issues first. Returns blocking issues (must fix before you can submit) and advisory warnings (recommended). For a CODE agent it also runs a build dry-run in the sandbox to catch a too-large bundle / missing dependency / build error before submit — that build is asynchronous (minutes), so the result shows `build_status: 'building'` while it runs; re-call this tool to see the final pass/fail. Optionally pass `smoke: true` to ALSO run your code agent once in the sandbox (after the build passes) to confirm it actually responds with the credentials you saved — the verdict comes back in `smoke` and is advisory (it never blocks submit). Pass `agent` (the slug or id of your draft from findagent_create_draft / findagent_create_code_draft). Submit (findagent_submit_for_review) is blocked server-side until this passes and, for a code agent, the build passes.
    ConnectorOAuth
  • Get Gonka Network signup link with referral welcome bonus (50M nGNK free tokens). Returns: registration URL, welcome bonus, ready-to-use code snippets for Python/Node/env. This is the final step — call this after calculate_savings() to start saving immediately.
    ConnectorNo auth
  • Search the Sigistry marketplace of Claude Code plugins by keyword and/or category. Returns matches with their install command and verification status (the registry runs an eight-check security audit; prefer "verified" plugins when recommending an install).
    ConnectorNo auth
  • Full details for one MCP server from the unified index, by slug (as returned by search_mcps): description, per-client install commands (Claude Code / Claude.ai / JSON config), transports, categories, the registries it's listed on with links, and its trust score. Use after search_mcps when the user wants to install or inspect a specific server.
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  • Run a workflow now in its deployed environment. This is the ONLY correct way to run or TEST a saved workflow — a run started here carries the workflow's rules, tracking and memory; browser_task/create_session runs do not. When the user says "run it", "run it once", "test it" or "try it" about a workflow, this is the tool they mean. Drives a real Chrome browser: the run may navigate, fill forms, and submit data on third-party websites as the workflow's prompt requires. Pass values for the workflow's {{variables}} in `variables` (stored variableValues defaults fill any gaps). Returns a sessionId — poll get_task_status for the result.
    Connector
    Destructive
    No auth