"Pine Script v6 Python integration or comparison" matching MCP tools:

• get_doc

  Pine Script

  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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• list_docs

  Pine Script

  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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• search_docs

  Pine Script

  USE WHEN finding documentation sections that match specific terms across all Pine Script v6 docs. Returns up to max_results sections ranked by match count, each with a preview and a get_section() call hint. AFTER calling this tool, call get_section(file, header) for each result you want to read in full. Data sourced from bundled Pine Script v6 documentation.

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• get_section

  Pine Script

  USE WHEN reading a specific named section from a Pine Script v6 documentation file. Returns the section content from the matched header to the next same-level header, with file path and line range. AFTER calling this tool, call list_sections(path) if the header was not found, or get_section() again with a child header for a narrower subsection. Data sourced from bundled Pine Script v6 documentation.

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• get_readiness_card_tool

  TWZRD Agent Intelligence

  Free pre-spend gate. Pass seller_wallet (or resource_name for marketplace listings). Check decision in the response (top-level, no nesting via MCP): "block" -> DO NOT PAY. Stop. "warn" -> proceed with caution. "allow" -> proceed. trust_score is a free heuristic (heuristic_registry_v0), NOT the full corpus. For the corpus-backed score + signed v6 receipt: GET /v1/intel/trust/{wallet} (0.05 USDC).

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• verify_receipt

  TWZRD Agent Intelligence

  Free utility: offline-verify a portable v5/v6 trust receipt — the "after you pay" half of the loop. Recomputes the Keccak256 leaf from the receipt's preimage (tamper-evidence) AND verifies the Ed25519 signature against the published TWZRD receipt-signing key (authenticity). Returns valid/leaf_valid/signature_valid plus the recomputed leaf and any errors. Pure and offline — no DB, no network, no payment. Pass the entire PaidReceipt object you were issued. Trust is anchored on the published key (or expected_pubkey), NOT on whatever pubkey the receipt carries. max_age_seconds: optional freshness gate (replay protection). Same semantics as the Python library verify_paid_receipt(..., max_age_seconds=...) and the standalone CLI --max-age.

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• decoder_intelligence

  Heista

  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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• viral_remix

  hooklayer

  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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• search_docs

  Devdocs Io

  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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• get_strategy_code

  quantifyme

  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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• competitive_deep_dive

  gapup-mcp

  Gold-standard competitive deep dive — STRUCTURED multi-source data (no LLM narrative). Pair tool: `competitor_intel` for LLM-narrated board briefing + slide script. Aggregates Wikipedia, Yahoo Finance, SEC EDGAR, Wayback Machine, DuckDuckGo, HackerNews, domain scraping — all keyless. Returns agent-shaped JSON: KPIs (funding, employees, revenue, market cap), P0/P1/P2 competitive signals, pricing radar, competitor comparison matrix, Wayback timeline, positioning (sector/industry/icp_hypothesis/moat_signals), quality score. Every field is sourced or marked unavailable — no hallucinated figures. SLA: p50 ~25s, p95 ~30s · score 80+ on listed targets (US/EU/foreign) · score ~40 on private companies (no EDGAR/Yahoo data). Use sync for batch agents (≤30s tolerance). Use `competitive_deep_dive_async` + `competitive_deep_dive_result(job_id)` for conversational agents. Inputs: company name or domain (required), optional competitor list (≤5), optional depth (easy/medium/hard).

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• analyze_record

  Senzing

  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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• get_template

  Gitignore

  Fetch the raw .gitignore content for the named template (case-sensitive, e.g. "Node", "Python", "macOS").

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• predict_virality

  hooklayer

  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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• get_embedder_network_manifest

  storyflo

  Wrap the public ``/embed/network-manifest.json`` — the platform-level DSP/SSP onboarding bundle: supported creative formats, payout rails, attribution URLs, integration patterns. Use when an agent is evaluating whether to wire storyflo into its surface, or when a DSP partner needs the canonical integration shape. Public — no auth required.

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• compare_protocols

  Intelligence MCP

  Get a side-by-side comparison matrix of all five agent payment protocols (AP2, ACP, x402, MPP, UCP) across creator, layer, agent delegation, budget limits, cross-merchant coordination, and MCP integration. Use when the user asks to compare protocols ('AP2 vs ACP', 'which protocol handles budgets?', 'what's the difference between x402 and MPP?', 'show me the landscape'). Use get_protocol_info instead for deep details on a single protocol.

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• audit_github_repo

  Commit — Supply Chain Risk Scoring

  Audit the supply chain risk of a GitHub repository's dependencies. Fetches the repo's package.json and/or requirements.txt from GitHub and runs behavioral commitment scoring on every dependency. This is the fastest way to audit a project — just provide the GitHub URL or owner/repo slug, and get a full risk table in seconds. Risk flags: - CRITICAL: single publisher/maintainer/owner + >10M weekly downloads (publish-access concentration risk) - HIGH: sole publisher/maintainer + >1M/wk downloads, OR new package (<1yr) with high adoption - WARN: no release in 12+ months (potential abandonware) Examples: - "vercel/next.js" — audit Next.js dependencies - "https://github.com/langchain-ai/langchainjs" — audit LangChain JS - "facebook/react" — audit React's dependency tree - "anthropics/anthropic-sdk-python" — audit Anthropic Python SDK Use this when someone asks "is my project at risk?" or "audit this repo's dependencies".

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• compare_protocols

  intelligence-mcp

  Get a side-by-side comparison matrix of all five agent payment protocols (AP2, ACP, x402, MPP, UCP) across creator, layer, agent delegation, budget limits, cross-merchant coordination, and MCP integration. Use when the user asks to compare protocols ('AP2 vs ACP', 'which protocol handles budgets?', 'what's the difference between x402 and MPP?', 'show me the landscape'). Use get_protocol_info instead for deep details on a single protocol.

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• validate_function

  Pine Script

  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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• x711_code_sandbox

  x711 — Universal Agent Gas Station

  Execute JavaScript or Python code in an isolated sandbox. Use for: data processing, math, CSV parsing, JSON transformation, crypto calculations, algorithm testing. Secure — no filesystem access, no network. Returns: { output: string, runtime_ms: number, language: string }. Requires API key.

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