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261,118 tools. Last updated 2026-07-05 10:01

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

  • Aggregate dossier check: Run all 10 Domain Dossier checks — dns, mx, spf, dmarc, dkim, tls, redirects, headers, cors, web-surface — in parallel and return all results in a single response. Use when you need a comprehensive domain health snapshot in one call; counts as ONE paywall call regardless of how many checks run. For a single focused check, prefer the individual dossier_* tools to minimise latency. Fires all 10 checks concurrently via Cloudflare DoH or direct HTTPS, 5 s per-check timeout. Returns a JSON object keyed by check id (dns, mx, etc.), each value a CheckResult discriminated union ({status:"ok",...} or {status:"error", reason}).
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  • USE WHEN reading the full content of a Pine Script v6 documentation file. Returns the file content; when limit is set, a header shows the char range and offset to continue reading. AFTER calling this tool, use offset=<end> to continue if the header indicates more content is available. For large files (ta.md, strategy.md, collections.md, drawing.md, general.md), prefer list_sections() + get_section() instead. Data sourced from bundled Pine Script v6 documentation.
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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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  • Take a viral source video and produce a fresh script that mirrors its viral DNA — same scene structure, same energy pattern, different topic. Returns the extracted formula, scene-by-scene script, camera shots, text overlays, and a `verify_hook` block prompting you to score the generated hook via score_hook. USE WHEN the user finds a video they want to copy the structure of, or chained from analyze_account.recommended_chain. Pass EITHER source_url (auto-extracts transcript) OR transcript directly — one is required. Costs 3 credits. NO SELF-RATING: viral_remix deliberately does NOT return a self-rated hook score. The script generator rating its own hook is structurally invalid (cardinal coupling). After every viral_remix call, you MUST call score_hook with the verify_hook.hook_text to get a structurally-independent quality signal before reporting to the user. UGC AUTHENTICITY: every response includes a `ugc_authenticity: { level: "native" | "ad_leaning", reasons: [] }` field. If level is "ad_leaning", you MUST flag this to the user verbatim ("Heads up — this remix is leaning ad-shaped because <reasons>") before reporting the script. The 2026 evidence is clear: polished/ad-shaped UGC underperforms organic-feeling UGC by ~40% on hook rate; an ad_leaning script is shippable but the user should know they're trading completion for whatever signal made the generator drift. Also surfaces `cta_archetype` so callers can verify CTA rotation across the fleet (comment_gate should be <25% of generations).
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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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Matching MCP Servers

Matching MCP Connectors

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  • A simple MCP server built with FastMCP and python

  • 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 the connected user's profile, plan, onboarding state, team memberships, and note quota in a single call. Call this once at the start of a conversation so you can greet the user by first name, run the onboarding script only when needed, route notes to the right team space, and avoid suggesting Pro features to free users. Returns onboarding.completed (boolean) and onboarding.missing_steps (array of 'connect_mcp' | 'first_note'), which together tell you what, if any, setup is left. Exposes the user's email address and plan — same data the user sees in account settings, but never billing or token metadata. No parameters required.
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  • Fetch a read-only HeyClaude registry entry detail payload by category and slug. By default (bodyMode='excerpt') the body markdown is trimmed to a short lead and large copyable fields are omitted to conserve context, with bodyChars/bodyTruncated/omittedFields describing what was dropped; pass bodyMode='full' for the complete content or 'none' to drop the body entirely. Use entry.asset to retrieve omitted install/script content.
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  • Get the Senzing JSON analyzer script to validate mapped data files client-side. REQUIRED: `workspace_dir` (writable directory, e.g. ~/sz-workspace) — the call WILL FAIL without it. The analyzer validates records against the Entity Specification, examines feature distribution, attribute coverage, and data quality. Returns a Python script (no dependencies) with instructions. No source data is sent to the server. Typical workspace_dir values: Linux `/tmp` or `~/sz-workspace`; macOS `~/sz-workspace`; sandboxed envs: explicit path under home (do NOT assume /tmp exists).
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  • Fetch the raw .gitignore content for the named template (case-sensitive, e.g. "Node", "Python", "macOS").
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  • Score a draft script for viral potential with a mitigation-aware adversarial second-pass. Returns optimistic_score (upstream self-grade), virality_score (adversarial headline), calibration_gap (disagreement between them), vectors_summary (counts of present vs mitigated attack vectors), recommendation ({verdict: SHIP|REWORK|NO-GO, reason}), score_range, viral DNA breakdown, signals[] with evidence, would_fail_because, calibration_check, and attack_vectors[] each tagged status: present|mitigated. USE WHEN the user has a finished draft and wants pre-publish verification, or chained after viral_remix to validate the generated script. Pass either a script string or a video URL. Costs 2 credits. TOOL HEALTH: response includes a quality field (level: full | partial | degraded, plus reason). If level is partial or degraded, flag it in chat before reporting. Never silently route around degraded data. REPORTING (v1.1.1 reframe — calibration gap is the value proposition, not a deficiency): lead with the recommendation.verdict and reason in plain language. Then frame the calibration_gap explicitly: "Every other AI scoring tool ships the OPTIMISTIC value of X without checking. The adversarial pass disagrees by GAP points and lands at VIRALITY_SCORE." Then surface vectors_summary as "X total, Y mitigated by the script, Z still present" and enumerate each PRESENT vector with severity + mitigation hint. Surface would_fail_because and signals[] last. NEVER report optimistic_score alone (recreates the self-grading loop). NEVER apologize for the adversarial number being lower — the gap IS the value. A 24-point gap means the check is doing its job; a 0-point gap means the check is rubber-stamping and worthless.
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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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  • Retry a failed simulation run. Resets an errored run back to 'created' status and triggers a new package build. The same run ID is reused. Only valid when status is 'error'. Returns 409 for any other state.
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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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  • 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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  • POST /tools/tool_compute_sandbox/run — Executes Python 3.12 code in an isolated subprocess with a 5-second hard timeout. Input: {python_code: string, input_data: any (optional, bound as variable 'input_data')}. Output: {success, result, stdout (capped 50KB), execution_time_ms, error_type}. Return value: assign to 'result' variable. Pre-loaded: math, json, re, statistics, itertools, functools, collections, decimal, datetime, random, hashlib, base64. Blocked: import, open(), eval(), exec(), os, sys, network, class definitions, dunder attributes. error_type values: syntax_error | security_error | runtime_error | timeout_error. Cost: $0.1500 USDC per call.
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  • Aggregate dossier check: Run all 10 Domain Dossier checks — dns, mx, spf, dmarc, dkim, tls, redirects, headers, cors, web-surface — in parallel and return all results in a single response. Use when you need a comprehensive domain health snapshot in one call; counts as ONE paywall call regardless of how many checks run. For a single focused check, prefer the individual dossier_* tools to minimise latency. Fires all 10 checks concurrently via Cloudflare DoH or direct HTTPS, 5 s per-check timeout. Returns a JSON object keyed by check id (dns, mx, etc.), each value a CheckResult discriminated union ({status:"ok",...} or {status:"error", reason}).
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  • USE WHEN discovering what Pine Script v6 documentation is available. Returns a categorised list of doc file paths with one-line descriptions. AFTER calling this tool, call get_doc(path) for small files or list_sections(path) then get_section(path, header) for large files (ta.md, strategy.md, collections.md, drawing.md, general.md). Data sourced from bundled Pine Script v6 documentation.
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  • USE WHEN confirming a Pine Script v6 function name is valid before using it in code. Returns a valid/invalid verdict with namespace suggestions or known replacement hints (e.g. ta.adx → ta.dmi, security → request.security). AFTER calling this tool, call get_functions(namespace) to list all valid functions in the relevant namespace if the function is invalid. Data sourced from bundled pine_v6_functions.json.
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