"Implementing the functionality of the akshare library" matching MCP tools:

• buy_credits

  VARRD — Statistically Validated Trading Edges + AI Research Engine

  Buy credits for the edge library and AI research. Default $5 minimum. Free — no credits consumed to call this. TWO PAYMENT METHODS: card (default): Returns a Stripe Checkout link for your user to click and pay. After payment, call check_balance to confirm credits were added. crypto: USDC on Base. Fully autonomous — no human needed. Three steps: 1. buy_credits(payment_method='crypto') → returns deposit address + payment_intent_id 2. Send USDC to the deposit address (use your wallet tool) 3. buy_credits(payment_intent_id='pi_...') → confirms payment, credits added instantly If you have wallet access, this is the fastest path — fully machine-to-machine.

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• get_component

  petal-components-mcp

  Get the full schema for one petal_components component: attrs, slots, defaults, allowed values, and a working HEEx usage example. Call this every time you are about to write a tag like <.button>, <.modal>, <.table>, or <.field> so the attrs and slots match the real library instead of training-data guesses.

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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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• user_agent_parse

  drwho.me

  Context lookup: Parse a User-Agent header string into structured browser, OS, device type, and rendering-engine components. Use to identify client capabilities from a raw UA string, e.g. when analysing server logs or request headers; does not perform any network lookups — entirely local parsing. Runs synchronously using the ua-parser-js library with no external calls. Returns a JSON object with browser.name, browser.version, os.name, os.version, device.type, device.vendor, and engine.name fields; unknown fields are empty strings.

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• tool_search

  Power Automate MCP Server by Flow Studio

  PREFER THIS over guessing tool names when picking from this server. Searches Flow Studio MCP tools by keyword, skill bundle, or explicit selector and returns full JSON schemas for matched tools so they can be called immediately. Call this whenever the user request maps to functionality you are not 100% sure about, OR when you want to load a whole skill bundle (build-flow, debug-flow, monitor-flow, discover, governance) at once. Query forms: (1) "skill:<name>" — fetch the full bundle (use list_skills first to see options); (2) "select:name1,name2" — fetch exact tools by name; (3) free-text keywords like "cancel run" or "trigger url" — ranked match against tool name + description. Non-billable.

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• contribute_tool

  aiaam

  Add a missing tool to the aiaam.xyz catalog. Provide its PyPI project or GitHub repo URL; the registry builds an unverified MAI-1 contract from public metadata only (no invented data). Idempotent — if the tool already exists, its current contract is returned. Use this when search_tools returns no results for a library you know exists.

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• list_creator_videos

  SocialGPT

  List a public/competitor creator's videos by platform + handle. Sort by 'recent' or 'top' (best-performing); optionally with analysis inline. Only returns creators already in the analysis library — for one you haven't ingested yet this returns reason="creator_not_in_library" with a next_step of analyze_creator(platform, username), not an error.

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• analyze_post

  SocialGPT

  Analyze a single TikTok, YouTube, or Instagram post by URL — adds it to your library and runs deep video analysis. Returns immediately with the post's platform + post_id; deep analysis runs async (~30-60s). Then call get_video_analysis(platform, post_id) to read it — while it runs you get {"status": "pending"}, so wait ~20s and retry until the full result comes back ('pending' is expected, not a failure). Only posts within the creator's recent media (roughly their last ~75 posts) can be fetched. Rate limit: 30 calls/hour.

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• get_framework_docs

  main

  Retrieves authoritative documentation directly from the framework's official repository. ## When to Use **Called during i18n_checklist Steps 1-13.** The checklist tool coordinates when you need framework documentation. Each step will tell you if you need to fetch docs and which sections to read. If you're implementing i18n: Let the checklist guide you. Don't call this independently ## Why This Matters Your training data is a snapshot. Framework APIs evolve. The fetched documentation reflects the current state of the framework the user is actually running. Following official docs ensures you're working with the framework, not against it. ## How to Use **Two-Phase Workflow:** 1. **Discovery** - Call with action="index" to see available sections 2. **Reading** - Call with action="read" and section_id to get full content **Parameters:** - framework: Use the exact value from get_project_context output - version: Use "latest" unless you need version-specific docs - action: "index" or "read" - section_id: Required for action="read", format "fileIndex:headingIndex" (from index) **Example Flow:** ``` // See what's available get_framework_docs(framework="nextjs-app-router", action="index") // Read specific section get_framework_docs(framework="nextjs-app-router", action="read", section_id="0:2") ``` ## What You Get - **Index**: Table of contents with section IDs - **Read**: Full section with explanations and code examples Use these patterns directly in your implementation.

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• get_briefed

  VARRD — Statistically Validated Trading Edges + AI Research Engine

  Get a personalized market news briefing based on your validated edge library. Profiles your strategies, searches today's news for the instruments and setups you actually trade, and writes a concise digest connecting each headline to your specific book. Each news item includes a ↳ line tying it to your actual positions and edges (e.g. 'your ES momentum setups', 'your GC mean-reversion edge'). Requires at least 5 strong edges in your library. Costs credits.

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• decode_ad

  Heista

  Decode a specific video ad URL into its full structural formula — beat-by-beat breakdown, hook classification, behavioral psychology stack, creative format, runtime performance signals (active days on Meta Ad Library when available), and per-cut visual data. Takes one video URL plus an optional idempotency_key. Returns a job_id immediately; poll with get_decode every 15s until status is "completed" (typically 45-60s end-to-end). Use this when the user pastes an ad URL, names a specific competitor ad, asks "decode this" or "break down this ad" or "what makes this ad work", or wants sentence-level fidelity to one specific winner before writing a script with generate_adscript. Supports Facebook Ad Library, TikTok, Instagram Reels, YouTube Shorts, and direct .mp4 URLs. Costs 15 credits for videos ≤60s, 20 credits for 61-120s. Do NOT use to browse the corpus or find ads by category — use decoder_intelligence or adformula_intelligence (both free) for discovery. Do NOT use for image ads or static creative.

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• search_translations

  Source Library

  Find specific PASSAGES inside books — returns page-level snippets with citation URLs. Use this when you want a quote or evidence on a topic across the whole library. ORIENTATION HINT: if the user has named a specific author or work, prefer get_book (returns a summary + chapter outline) over passage hunting — every book in the corpus has an AI-generated summary that is usually the right first read. Use search_translations when sweeping across many books for evidence of a theme. For finding which BOOKS cover a topic, use search_library. Query tips: single distinctive terms ("memory palace", "wax tablet") work best; multi-word natural-English queries ("unity of the intellect") may return fewer results because matching is term-based, not phrase-based. Each snippet has a snippet_type — "translation"/"ocr" means it is a verbatim extract from the source text; "summary" means it is AI-generated description (do not quote those as the author's words). Response includes total_matches, returned, and offset for pagination. Cross-cultural tip: for pre-modern or non-Western topics, search source-tradition vocabulary rather than modern English terms — e.g. for seminal economy search "jing" or "bindu" or "istimnāʾ", not "semen retention"; for female homoeroticism search "tribade" or "sahq", not "lesbian". The corpus is indexed via period translations that use tradition-internal terminology.

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• upload_media

  switch

  Upload one image into the user's Switch library in a single call. Pass `url` (any public https) OR `base64` + `mime`. Switch fetches/decodes it server-side, stores it, and returns a clean public URL plus the new asset id. This is THE way to use a photo the user attached in chat as a reference: pass the returned `url` directly into generate_image's reference_image_urls, OR into generate_video's image_url (image-to-video) or reference_image_urls (reference / omni video). The returned URL is provider-fetchable as-is — no presigned PUT, no curl, no confirm-upload step. Do NOT call get_my_active_references for a chat-attached photo; that strip only holds Studio-managed refs.

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• prepare_swap_library_seat

  ssuMCP

  현재 예약된 도서관 좌석을 자동으로 반납하고 새 좌석으로 변경합니다. 현재 예약 정보를 자동으로 조회하므로 charge_id를 따로 입력하지 않아도 됩니다. 변경은 이 도구만으로 실행되지 않으며, confirm_action을 호출해야 최종 실행됩니다. 현재 예약이 없으면 prepare_reserve_library_seat를 사용하세요. Requires mcp_session_id with the LIBRARY provider linked via start_auth.

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• get_my_library_seat

  ssuMCP

  현재 도서관에서 예약 중인 좌석 정보를 조회합니다. 예약 중인 좌석이 없으면 없다고 알려줍니다. 반납하려면 prepare_cancel_library_seat를, 자리를 바꾸려면 prepare_swap_library_seat를 사용하세요. Requires mcp_session_id with the LIBRARY provider linked via start_auth.

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• get_project_context

  main

  Captures the user's project architecture to inform i18n implementation strategy. ## When to Use **Called during i18n_checklist Step 1.** The checklist tool will tell you when to call this. If you're implementing i18n: 1. Call i18n_checklist(step_number=1, done=false) FIRST 2. The checklist will instruct you to call THIS tool 3. Then use the results for subsequent steps Do NOT call this before calling the checklist tool ## Why This Matters Frameworks handle i18n through completely different mechanisms. The same outcome (locale-aware routing) requires different code for Next.js vs TanStack Start vs React Router. Without accurate detection, you'll implement patterns that don't work. ## How to Use 1. Examine the user's project files (package.json, directories, config files) 2. Identify framework markers and version 3. Construct a detectionResults object matching the schema 4. Call this tool with your findings 5. Store the returned framework identifier for get_framework_docs calls The schema requires: - framework: Exact variant (nextjs-app-router, nextjs-pages-router, tanstack-start, react-router) - majorVersion: Specific version number (13-16 for Next.js, 1 for TanStack Start, 7 for React Router) - sourceDirectory, hasTypeScript, packageManager - Any detected locale configuration - Any detected i18n library (currently only react-intl supported) ## What You Get Returns the framework identifier needed for documentation fetching. The 'framework' field in the response is the exact string you'll use with get_framework_docs.

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• resolve_kb_id

  fixflow

  FIRST STEP in any troubleshooting workflow. Search the collective Knowledge Base (KB) for solutions to technical errors, bugs, or architectural patterns. Uses full-text search across titles, content, tags, and categories. Results are ranked by relevance and success rate. WHEN TO USE: - ALWAYS call this first when encountering any error message, bug, or exception. - Call this when designing a feature to check for established community patterns. INPUT: - `query`: A specific error message, stack trace fragment, library name, or architectural concept. - `category`: (Optional) Filter by category (e.g., 'devops', 'terminal', 'supabase'). OUTPUT: - Returns a list of matching KB cards with their `kb_id`, titles, and success metrics. - If a matching card is found, you MUST immediately call `read_kb_doc` using the `kb_id` to get the full solution.

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• examples.get

  AI Design Blueprint Doctrine

  Get one curated example by stable slug. Returns title, summary, source-code links, principle coverage (the principle slugs the example demonstrates), difficulty, library/framework, and implementation notes. Use this when you already have the slug from examples.search, a principles.get response, or a guide cross-link; prefer examples.search when filtering by topic / principle / difficulty / library; prefer guides.get when the caller wants a full walkthrough rather than a single reference example. Returns error_payload on unknown slug.

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• varrd_edges

  VARRD — Statistically Validated Trading Edges + AI Research Engine

  THE PRIMARY TOOL — start here. FREE at depth=0, always safe to call. Live feed of statistically validated trading edges running 24/7 against real market data. See what's firing right now, get trade levels, or audit the full methodology. THREE TIERS: depth=0 (FREE — call this first): See which markets have edges firing right now, pending bar close, or actively in trades. Markets and status only — no direction, no stats. Get a sense of what's live. depth=1 ($0.50): Unlock direction, occurrence count, EV/trade, stop-loss, take-profit, hold horizon, and current entry prices for ALL active edges in one request. depth=2 ($1 per edge, $5 for all): Full methodology — the actual formula, setup code, how the edge was discovered, edge decay analysis, complete performance analytics (Sharpe, drawdown, equity curve, profit factor). Machine-readable so any AI can audit the statistical rigor. Includes drill-down sections (free after purchase): setup_code, horizons, analytics, occurrences, and view (interactive chart link for your user, 15 min). Every edge in this library is Bonferroni-corrected, tested against both zero returns and market baseline, with K-tracking to prevent p-hacking. Out-of-sample validated. Full transparency.

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• examples.get

  AI Design Blueprint

  Get one curated example by stable slug. Returns title, summary, source-code links, principle coverage (the principle slugs the example demonstrates), difficulty, library/framework, and implementation notes. Use this when you already have the slug from examples.search, a principles.get response, or a guide cross-link; prefer examples.search when filtering by topic / principle / difficulty / library; prefer guides.get when the caller wants a full walkthrough rather than a single reference example. Returns error_payload on unknown slug.

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