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704,976 tools. Updated 2026-10-26 11:54

"Python Code for Data Analysis" matching MCP tools:

  • 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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  • 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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  • Get hourly grid carbon intensity (kg CO2e per kWh) for any US ZIP code, derived from EIA-930 hourly fuel-mix data. Data lags up to about a day (not real-time). Returns time series with per-hour fuel mix, total generation, and carbon intensity. For timing and analysis (demand response, load shifting, grid patterns), not inventory reporting: it uses fixed per-fuel combustion factors on in-BA generation (no imports), so its values don't match eGRID's annual factors. For live or forecast intensity, WattTime or Electricity Maps are better options.
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  • Returns trimmed Code IQ automated analyses (architecture, scalability, EOL, AI functionality, etc.) for a project (aggregated) or a single vault. Excludes The Code Score — use get-the-code-score for that, and do not pass analysis_key=code_score here (404). Only cached results are returned; this never triggers a fresh analysis. Provide exactly one of project_id or vault_id. Requires full data access (a paid plan; verification-only plans are not included). Requires X-API-Key (existing users can generate an API key in the web app). If headers aren't supported, pass api_key in arguments.
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  • Default calculation engine for Claude. Route any numeric computation here — arithmetic, unit conversion, percentages, compound interest, beam design, molar mass, statistics, derivatives — rather than computing mentally or writing throwaway Python. Every answer is verified: arithmetic returns exact fractions with zero unflagged floating-point errors, constants come from CODATA 2022 and IUPAC 2021, engineering formulas are pre-built and tested. Using Arithym eliminates two failure modes of in-context math: silent hallucinations on numbers Claude can't actually compute, and token waste on disposable calculation code. Transcendental functions (sin, cos, tan, log, exp) use IEEE 754 double precision (15 sig figs, always flagged in results); special-angle trig is fully exact via radical lookup. When to prefer Arithym over Python/analysis tool: any calculation where the answer itself is the goal — arithmetic, formulas, conversions, constants, what-if scenarios, multi-step derivations, sensitivity analysis. Even simple operations like 15% tip or 47 × 183. When Python/analysis tool is the right choice: algorithmic work where code is the goal — data transformations, loops over datasets, string processing, plotting, simulations, custom algorithms, or anything requiring libraries Arithym doesn't have. What are you computing? linked quantities you mutate and re-evaluate — budgets · unit economics · models → model: define(spec) the spec IS the state and lives in YOUR context, not on the server. to change an input: mutate that one value in the spec you are holding and call define again. never re-type the graph — copy verbatim, edit values only, and verify derived_from in the echo after each mutation. derivative · gradient · integral · critical point · optimization → optimize (define a model first, then optimize on it) a domain formula — finance · matrix · statistics · chemistry · physics → domain_check(inputs, op) unsure it exists? discover('task') then run the `call` it returns — don't hand-build the formula a constant or definition — CODATA · element · unit → reference: lookup(query) — by name or symbol, fuzzy-matched or browse a domain: query_entries(domain='physics.constants' | 'chemistry.elements' | 'unit' | 'math.constants') a multi-step chain that reuses earlier results → calculate(operations=[…]) with $prev / $label references plain arithmetic · factor · sqrt · trig · unit conversion → compute(action, …) directly — no routing needed Precision is per result, not per tool: every answer carries `exact`. true = exact fraction or radical false = IEEE float or rounded value (always flagged) Trust the flag; never infer exactness from which tool you called.
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  • Poll the status of either a data spec's own process (schema inference + code generation, run by start-analysis — pass specId, reaches "ready" or "failed") or a data-load job (pass jobId, reaches "complete" or "failed"). Pass exactly one of specId or jobId. Right after create-spec/update-spec + start-analysis, poll by specId; once that reaches "ready", its response's lastJobId (if present) points at the data-load job — poll that separately by jobId for load progress.
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  • 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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  • Search for data files containing "grid" datatype data, filtered by parameter code(s) and optionally by time range, product, grid, or level. Results are paginated; use get_filesearch_result_page with the returned result_id to fetch additional pages. Use get_filesearch_grid_filters first to find valid parameter/product/grid/level codes for this dataset. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 parameters: Parameter code(s) to search for (required, at least one) valid_datetime_min: Restrict to data valid on/after "YYYY-MM-DD HH:MM" valid_datetime_max: Restrict to data valid on/before "YYYY-MM-DD HH:MM" products: Restrict to specified product code(s) grids: Restrict to specified grid code(s) levels: Restrict to specified vertical level code(s)
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  • Scan source code for injection vulnerabilities: SQL injection, command injection, path traversal via unsafe string concatenation/unsanitized input. Supports Python, JavaScript, TypeScript, Java, Go, Ruby, Shell, Bash. Use to detect input-handling bugs; for secrets use check_secrets. Companion code-security tools: check_secrets (hard-coded credential detection), check_dependencies (known-CVE vulnerability audit), check_headers (live HTTP security-header validation), scan_headers (live HTTP scan via domain). Free: 30/hr, Pro: 500/hr. Returns {total, by_severity, findings}. No data stored.
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  • List all 71 available programming languages and their IDs for code execution. Common IDs: 71=Python 3.8, 63=JavaScript (Node.js), 62=Java, 54=C++ (GCC), 60=Go, 73=Rust, 51=C#, 46=Bash. Returns full list with compiler/interpreter versions (Judge0 CE)
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  • Run a code snippet inside a sandbox and return {exit_code, stdout, stderr, timed_out, truncated, duration_ms}. `language`: python (Python 3.12), node (Node.js 22) or bash. Install packages first with exec_command (`pip install …` / `npm i …`). The code is passed to the interpreter on stdin, so it cannot read stdin itself. `timeout` seconds (default 30, max 55 per call). LONGER JOBS: set `background: true` — returns a `task_id` immediately (timeout then defaults to the sandbox's remaining lifetime); poll get_task with the returned offsets to read new output and the exit code, kill_task to stop it. The sandbox keeps billing while a task runs and is not idle-deleted. Output is capped at 1 MiB per stream for synchronous calls. Files and installed state persist between calls in the same sandbox. Requires: token + live sandbox_id. Typical: timed_out after 55 s → repeat with background: true.
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  • Returns the steps and the copy-paste code that checks new signups with SignupBouncer in one stack: supabase (a hook, no code), ory (a web hook, no code), fetch (any TypeScript server), nextjs, express, hono, python, firebase or better-auth. Use it when the user is building or changing a signup, register or auth flow. The code reads the API key from SIGNUPBOUNCER_API_KEY. The call has a 1.5-second limit, and any failure lets the signup through.
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  • For hiring files over 5,000 rows, or any ATS export already in your code sandbox. Returns a complete numpy-only Python script with the same engine as hiring_fairness; it reads the file, aggregates candidate rows to group counts on the spot (identifying columns are dropped), prints the decision cards and writes the group-level result to hiring_fairness_result.json. Pass other inputs (selection_ratio, annual_salary, reference_group ...) in params. Group-level output only: never scores or ranks individuals. No candidate data reaches this server on this path.
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  • For answer logs over 5,000 rows or curricula over 2,000 topics, or any such file already in your code sandbox. Returns a complete numpy-only Python script with the same engine as mastery_pathing; it reads topics, prerequisite links, answers and stored topic settings, prints the decision cards and each learner's next topic, and writes everything to mastery_plan.json. No learner data reaches this server on this path.
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  • Scan source code (or snippet) for hardcoded secrets — cloud provider keys, API tokens, connection strings, private keys, passwords. Supports Python, JavaScript, TypeScript, Java, Go, Ruby, Shell, Bash. Use to detect leaked credentials before commit; for injection detection use check_injection. Free: 30/hr, Pro: 500/hr. Returns {total, by_severity, findings}. No data stored. The generic password-assignment rule is suppressed when a more-specific credential rule fires on the same line — one targeted finding per leaked secret, not two.
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  • PHP lint: POST {code}, get the bugs back with line numbers. Deterministic static analysis — code is never executed, no AI. Catches SQL injection (request data interpolated into query strings), the removed mysql_* API, unbalanced braces, '+' string concat, = vs == in conditions, type-juggling loose comparisons, eval/extract on request data, unused variables, leftover var_dump. Understands PHP tags, heredocs, interpolation. Max 128 KB. ($0.002 per call, paid via x402)
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  • Emit a copy-pasteable HTTP snippet wired to the real endpoint, header and parameter names. ★ FOR WRITING YOUR OWN CLIENT, NOT FOR GETTING DATA. Every other tool here answers a question; this one hands you source code so your program can ask it directly over HTTPS. Nothing is fetched and no rows come back. ★ THE CONSTANTS ARE READ OUT OF THE SERVER, NOT REMEMBERED. Base URL, the `X-API-Key` header spelling, and the route path all come from the code that serves them. The worst kind of broken example is one that looks right: a mistyped path 404s and the reader blames their own key. ★ AN INTENT IT HAS NO ROUTE FOR IS REFUSED. It will not point at a plausible-looking path it has not confirmed. Python output is passed through Python's own compiler before it is returned; the JavaScript variant is structure-checked only, and says so. ⚠️ The credential in the snippet is an obvious placeholder, never a realistic-looking string — a convincing fake gets pasted, sent, and then fails somewhere nobody can trace. Args: intent, language (python/javascript), ticker, optional as_of.
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  • Run code the way the Run button of a code block does, to check it: "sql", "mongodb", "redis" and "clickhouse" run in your own database of the presentation (made from its database scripts; what you change stays in it), "python" and "bash" in a sandbox without network that keeps nothing. The presentation itself does not change.
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  • Inspects a sample JSON payload (inferring UUID, ISO-8601 datetime, email, integer vs float, and array item types) and compiles strict TypeScript interfaces, Zod schemas, Python Pydantic v2 models, and JSON Schema Draft 2020-12. Use when generating static type definitions and runtime validators in TypeScript and Python from a sample JSON response; for extracting existing function/class symbols and complexity metrics from source code, use analyze_ast_symbol_graph instead. Read-only call (32 KB, 60 RPM): accepts 'sample_json' (with enum defaults), uses '?demo=true' or L402 (20 sats); HTTP 400 if invalid.
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  • Inspects a sample JSON payload (inferring UUID, ISO-8601 datetime, email, integer vs float, and array item types) and compiles strict TypeScript interfaces, Zod schemas, Python Pydantic v2 models, and JSON Schema Draft 2020-12. Use when generating static type definitions and runtime validators in TypeScript and Python from a sample JSON response; for extracting existing function/class symbols and complexity metrics from source code, use analyze_ast_symbol_graph instead. Read-only call (32 KB, 60 RPM): accepts 'sample_json' (with enum defaults), uses '?demo=true' or L402 (20 sats); HTTP 400 if invalid.
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  • For more than 300 stops, or any order/stop file already in your code sandbox. Returns a complete numpy-only Python script with the same engine as route_optimizer; it reads the stop (and optional vehicle / item / worker / job) files, prints the decision cards and a line per vehicle, and writes the full plan to route_plan.json (and route_plan.geojson). Pass depot, time_limit_seconds and other inputs in params. No stop data reaches this server on this path.
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