"Strategies for enhancing coding efficiency with cursor agent optimization" matching MCP tools:

• assets.listA

  AI Design BlueprintA

  Public — list downloadable doctrine and agent asset artifacts (skill packs, rule packs, MCP setup snippets) the user can drop into their AI coding tool to import the Blueprint as native skill/rule files. Returns a list of assets with name, format (one of: zip / md / markdown / mdc / json / toml / text — the full vocabulary), pack_version, download_url, and platform target (Claude Code, Cursor, Codex, Gemini, Qwen). The response also carries `count` (length of `assets`) for symmetry with principles.list / clusters.list / guides.list. WHEN TO CALL: the user asks how to bring the Blueprint into their coding agent, or wants to install it as a local skill/rule file. WHEN NOT TO CALL: for the live MCP tools themselves — those are already available through this server. For doctrine content, prefer principles.list/get and guides.list/get. BEHAVIOR: read-only, idempotent, no auth required. Asset artefacts are regenerated on every deploy from the canonical doctrine.

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• get_new_sinceA

  Horizon AI IntelligenceA

  Incremental poll: raw item-level AI news added since a cursor, oldest→newest, with a nextCursor for your next call — use this for "what's new since I last checked"; for the curated once-daily synthesis use get_daily_briefing. Omit cursor for the latest items plus a cursor to start polling from. Titles + links + topics (bodies and higher limits are on the paid tier).

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• list_opt_demosA

  SCModelingA

  List the bundled SCModeling optimization demos. Returns id + label + one-line summary for each (Tariff, Coffee Co-pack, SSO Basic). Use this before describe_opt_demo or get_opt_result to know which demo_id values are valid. All demos are precomputed sample-only fixtures — for optimization on real client data, the SCModeling desktop tool is the product.

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• assets.listA

  AI Design Blueprint DoctrineA

  Public — list downloadable doctrine and agent asset artifacts (skill packs, rule packs, MCP setup snippets) the user can drop into their AI coding tool to import the Blueprint as native skill/rule files. Returns a list of assets with name, format (one of: zip / md / markdown / mdc / json / toml / text — the full vocabulary), pack_version, download_url, and platform target (Claude Code, Cursor, Codex, Gemini, Qwen). The response also carries `count` (length of `assets`) for symmetry with principles.list / clusters.list / guides.list. WHEN TO CALL: the user asks how to bring the Blueprint into their coding agent, or wants to install it as a local skill/rule file. WHEN NOT TO CALL: for the live MCP tools themselves — those are already available through this server. For doctrine content, prefer principles.list/get and guides.list/get. BEHAVIOR: read-only, idempotent, no auth required. Asset artefacts are regenerated on every deploy from the canonical doctrine.

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• convert_contentA

  BotverseA

  Convert a document inline — pass the content directly as a string (or base64 for binary inputs like .docx). PREFERRED route for documents, and the one to use in sandboxed agent environments (claude.ai, Claude Desktop, Cursor): it runs entirely server-side, so it never needs the S3 upload those sandboxes block. Limit: up to 4 MB of content — already huge (a 500-page book is ~1 MB of text). For anything larger, use convert_from_url with a public URL. Supported inputs: md, html, rst, txt (plain text), docx (base64). Supported outputs: docx (Word), pdf, html, txt, md, rst, xlsx. Returns a job_id — poll get_job_status until 'complete', then get_output_content (inline bytes, sandbox-safe) or get_download_url (S3 link). Flat fee $0.05 per file. TIP: if you have shell access and are NOT sandboxed (e.g. a local coding agent), the `botverse` CLI (`npx botverse convert <file> --to <fmt>`) is faster for local files — it streams from disk instead of re-emitting the content through the model.

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• get_integration_guideA

  TestMyVibesA

  Returns the canonical guide for using TMV from a coding-agent context. Covers the fix-test-retest loop, how to write a good test prompt, how to read the actionTrail / consoleErrors / failedRequests outputs, and common gotchas. Call this first if you're a new agent on a project — it'll save you a debug session. The same content is served at https://testmyvibes.com/docs/coding-agents.

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• compare_backtestsA

  Backtest360A

  Run several strategies on the same data and compare side by side. One quota-counted call, but compute scales with the number of strategies. If the wall-clock compute budget is exceeded, the call fails with a tool error (504) instead of returning partial results — narrow the request (fewer strategies, shorter date range, coarser frequency) and retry. Args: data_source: Shared data source (same shape as run_backtest). strategies: List of {"label": str, "strategy": {...}, "execution": {...}?} entries. Labels need not be unique or id-safe — they are echoed back verbatim in the result. include_benchmark: Add a buy-and-hold benchmark to the comparison. response_detail: Shaping level applied to each strategy's result. trades_limit: Max trades per strategy when detail is 'full'. Returns: {"strategies": [{"label", "result"}, ...], "equity_curves": {...}, "alignment"?}, each result shaped at the requested detail. When a benchmark is included, non-benchmark entries also carry "relative" (beta, alpha, information ratio, etc.). A 400/422 rejection returns {"accepted": false, "error": ...}; capacity/timeout/permission failures raise a tool error.

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• lora_airtimeA

  calc.engineerA

  Calculates LoRa packet time-on-air using the Semtech AN1200.13 formula. Computes symbol duration, preamble time, payload symbol count, effective data rate, and the minimum transmission interval for 1% duty cycle compliance. Essential for capacity planning in LoRaWAN and Meshtastic mesh networks. Accepts spreading factor (SF7-SF12), bandwidth (125/250/500 kHz), coding rate (4/5-4/8), payload size, header mode, CRC, and optional low data rate optimization. Feeds airtime_ms to channel_utilization for mesh load analysis.

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• designesy_skill_mdA

  DesignesyA

  Get the Designesy SKILL.md — the agent-skill-format export of the design-system contract, written as behavioral rules an AI coding agent can drop into .agents/skills/ or a system prompt. Use this when you want the contract in a form that steers how an agent *builds* UI (tokens, anti-patterns, behavioral rules, verification). When NOT to use: for the raw contract JSON, use designesy_contract; for scoring, use designesy_score. Read-only — no side effects. Returns markdown text (SKILL.md format) — drop into .agents/skills/ or paste into a system prompt. No parameters.

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• search_agent_directoryA

  TensorFeedA

  Search the TensorFeed Agent Self-Directory for hireable AI agents. Filter by skill (from a controlled vocab including research, data-analysis, coding, content-writing, voice-acting, image-generation, etc), service_area (research/data/coding/writing/voice/image/video/other), language (BCP 47), availability, hourly rate cap, minimum years of experience, or verified-hireable status. Verified-hireable members (operators paying $5 USDC/30 days for top-tier visibility) sort first. Free tier capped at 25 results. Returns wallet, display_name, operator_url, skills, rates, languages, years_experience, composite reputation rank, trust grade. TF publishes self-descriptions; TF takes no fee from off-platform transactions between operators and the agents who contact them.

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• get_fixesA

  mcpA

  Return the prioritized, pillar-tagged (FIND / READ / USE) action plan for a completed audit, deduplicated across sources, with machine-actionable implementation steps included on fixes where available. Use this when you want the to-do list to act on (or hand to a coding agent), rather than the scores or section detail.

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• beycome_estimateA

  listingA

  Discovery stage — estimate a property's SALE value (beycome CMA + Zillow Zestimate). No account or prop_id needed. Returns both estimates plus suggested pricing strategies (fast / balanced / max). Use during discovery to set a list price; pair with `beycome_comps`. For rental inquiries, use `beycome_rental_estimate` instead.

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• list_loadoutsA

  MCPExplorerA

  List curated loadouts — deliberately-assembled kits of MCP servers + governance + plays for a specific job (GTM, coding, research, support, infra). The agent-facing version of the /loadouts product. Use get_loadout for the full kit with live trust.

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• list_runtimesA

  MCPExplorerA

  List the runtimes generate_runtime_config supports (Claude Desktop, Cursor, VS Code, agent frameworks, …), with each one's config path. Enumerate these instead of guessing runtime slugs.

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• get_track_recordA

  Liquid State — Crypto Market IntelligenceA

  Liquid State's public paper-strategy track record — deterministic, rule-based strategies with return %, max drawdown %, and days live for each, updated daily. Paper, not live capital; losses are never hidden. Use when the user asks whether Liquid State's calls or strategies actually work, or wants a verifiable track record rather than a claim.

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• fork_shared_portfolioA

  NexusTrade Financial MCPA

  ⚠ MUTATING — creates or modifies a portfolio. Fork strategies from a shared portfolio into the user's account. target: 'new' creates a chat portfolio; 'existing' patches a deployed portfolio. mode: 'replace' (default) removes old strategies, 'append' keeps them. For monetized portfolios, subscribe first. Returns { forkSharedPortfolioResult: { portfolioId, name, addedCount, removedCount, ... } }. Prefer fork when the user wants to edit/customize strategies.

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• ma_tax_efficiency_mapperA

  gapup-mcpC

  For CFOs evaluating cross-border M&A deals: analyzes tax efficiency by mapping withholding tax rates, transfer pricing regulations, and permanent establishment risks across specified jurisdictions. Inputs include acquirer/target jurisdictions, deal structure, and transaction value. Outputs jurisdiction-specific tax exposure, efficiency scores, and risk flags. Uses World Bank Tax Rates API, IMF SDR data, and SEC EDGAR filings for corporate tax disclosures. — pass async:true REQUIRED to avoid x402 timeout.

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• getRedditSubredditWithPostsByNameA

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)A

  Get Reddit subreddit by name with its posts. FAST (default, omit responseType or responseType="fast"): Returns subreddit data with up to 300 posts directly (use limit param to reduce). Auto API fallback for fresh data. PAGING (responseType="paging"): Async paginated results (100 posts per page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. RESPONSE STRUCTURE: Returns { results: { subreddit: {...}, posts: [...] }, pagination: {...} }. FIELD SELECTION: Use subredditFields for subreddit data optimization, postFields for post data optimization. First searches database for both subreddit and posts, then external API if data is stale or missing. This is a safe, read-only tool for analyzing searchable information.

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• ma_tax_efficiency_mapperA

  mcp-knowledgeC

  For CFOs evaluating cross-border M&A deals: analyzes tax efficiency by mapping withholding tax rates, transfer pricing regulations, and permanent establishment risks across specified jurisdictions. Inputs include acquirer/target jurisdictions, deal structure, and transaction value. Outputs jurisdiction-specific tax exposure, efficiency scores, and risk flags. Uses World Bank Tax Rates API, IMF SDR data, and SEC EDGAR filings for corporate tax disclosures. — pass async:true REQUIRED to avoid x402 timeout.

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• ma_tax_efficiency_mapperA

  gapup-mcpC

  For CFOs evaluating cross-border M&A deals: analyzes tax efficiency by mapping withholding tax rates, transfer pricing regulations, and permanent establishment risks across specified jurisdictions. Inputs include acquirer/target jurisdictions, deal structure, and transaction value. Outputs jurisdiction-specific tax exposure, efficiency scores, and risk flags. Uses World Bank Tax Rates API, IMF SDR data, and SEC EDGAR filings for corporate tax disclosures. — pass async:true REQUIRED to avoid x402 timeout.

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• vibekit_chatA

  VibeKitA

  Send a message to an app's built-in AI coding agent, which reads, writes, and modifies the app's code and redeploys it. Use for 'build/make a change to my app' requests. If the agent finishes quickly you get its reply directly. Otherwise you get status:'working' — do NOT resend; instead give the user LIVE progress: poll vibekit_agent_status every few seconds and relay the current step from activity.status ('editing the homepage…', 'deploying…') until activity.done, then read vibekit_agent_history for the final reply.

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