"PageSpeed Insights" matching MCP tools:

• pagespeed

  seooraclev2

  Google PageSpeed Insights scores: Performance, SEO, Accessibility, Core Web Vitals.

  Connector

• discovery_get_results

  Disco

  Fetch the full results of a completed Disco run. Returns discovered patterns (with conditions, p-values, novelty scores, citations), feature importance scores, a summary with key insights, column statistics, and suggestions for what to explore next. The response includes a `dashboard_urls` object with direct links to each page of the interactive report — use these to direct the user to the most relevant view: - **summary**: AI-generated overview with key insights, novel findings, and plain-language explanation of the most important findings - **patterns**: Full list of discovered patterns with conditions, effect sizes, p-values, novelty scores, citations, and interactive visualisations - **features**: Feature importances, feature statistics and distribution plots, and correlation matrix - **territory**: Interactive 3D map showing how patterns select different regions of the data Only call this after discovery_status returns "completed". Args: run_id: The run ID returned by discovery_analyze. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

  Connector

• analyze_competitor

  Analook — Competitor Intelligence

  Submit a competitor analysis job. Analyzes a competitor's website across 15+ data sources (SEO, traffic, social, Product Hunt, GitHub, Wayback Machine history, AI-generated insights, etc.) and returns a job_id. Use get_report_status(job_id) to poll and get_report(job_id) to retrieve results when status='completed'. Typical analysis takes 2-5 minutes. Requires authentication (deducts 1 credit from your Analook balance). Args: url: Competitor website URL (e.g. 'https://linear.app' or 'lovable.dev') product_name: Optional product name override (defaults to domain) Returns: {job_id: str, status: 'started', poll_url: str} on success {error: str, hint?: str} on auth/validation failure

  Connector

• get_grid_scoreboard

  DC Hub — Data Center & Energy Intelligence

  Live GLOBAL grid scoreboard — 7 US grid operators (PJM, ERCOT, CAISO, MISO, SPP, NYISO, ISO-NE) + Great Britain (NESO) + ~12 European bidding zones (Germany/Frankfurt, France/Paris, Netherlands/Amsterdam, Ireland/Dublin, Spain, Belgium, Poland, Austria, Nordics — via ENTSO-E) + Taiwan (Taipower) + Australia NEM (AEMO), ranked side-by-side RIGHT NOW: renewable share %, gas share %, full fuel mix (gas/nuclear/coal/wind/solar/hydro MW), and demand. One call answers "which grid worldwide is greenest, or most gas-reliant, for siting a data center?" — vs compare_isos (pairwise) or get_grid_data (single ISO). US + GB + EU all rank by wind+solar+hydro share (apples-to-apples); AU is listed unranked (its feed reports a variable-renewable floor only, no full fuel split — kept honest). Source: US = EIA hourly RTO; GB = Elexon Insights; EU = ENTSO-E Transparency; AU = AEMO NEM — all live via DC Hub, greenest-first. Quote with attribution to DC Hub (CC-BY-4.0). Try: get_grid_scoreboard.

  Connector

• get_insights_voice

  wikimint

  Returns one of six curated Insights voice sections for a specific command — depth content not available in Microsoft Learn or any other MCP server. Manager: business impact and decision context. Practitioner: real-world usage patterns and gotchas. Learner: plain-language explanation for those new to the command. SoftwareApproval: network access, data sensitivity, approval checklist. Dependencies: what this command requires to function. Compliance: regulatory and audit considerations. BEFORE CALLING: confirm HasInsights=true on the command via get_command_help. If HasInsights=false, this tool will always return HasContent=false — skip the call. RETURN SHAPES: (1) HasContent=true, Content=<string> — voice is authored, use Content directly. (2) HasContent=false, Content=null, Message=<string> — this voice has not been authored yet. This is a data gap, not an error. Read Message for explanation. Do not retry the same voice; it will not change within a session. Voices are authored incrementally — no module is guaranteed to have all six voices populated for every command.

  Connector

• package_health

  dynamic-feed

  Health & security posture of a software package (npm / PyPI / Go / Maven / Cargo / NuGet / RubyGems) from deps.dev (Google Open Source Insights, keyless): latest version, license, count of known security advisories, the OpenSSF Scorecard (0-10 security-posture score for the source repo + its weakest checks) and popularity (stars/forks). The "should I depend on this?" check — pairs with check_vulnerability (is a version vulnerable) and software_version (is the runtime current). Args: package (e.g. "lodash", "requests"), ecosystem (npm|pypi|go|maven|cargo|nuget|rubygems), version (optional — defaults to the latest).

  Connector

• analysts_signals_all

  whale-insights

  All analysts' signal history in one request (public, free-tier, MCP-compatible) — Returns the last 7 days of signals for all 5 analysts grouped by analystId in a single response — ideal for AI agents that need a cross-analyst comparison without 5 round-trips. Analyst ids in the response: chain_hawk (ChainHawk, BTC & macro), whale_watch (WhaleWatch, multi-chain whales), alpha_scout (AlphaScout, emerging tokens), defi_pulse (DeFiPulse, DeFi/stables/bridges), quant_edge (QuantEdge, signal risk/convergence). Always free-tier depth (last 7 days, up to 200 signals per analyst). No authentication required. No query parameters needed. Response: { results: { [analystId]: signals[] }, tier: 'free', updatedAt }. Each signal: id (number), tokens (array), typeLabel, outcome ('win'|'loss'|null), returnPct (null if unresolved), createdAt (ISO-8601), analystId. To fetch a subset of analysts, use GET /api/public/analysts/signals?id=chain_hawk,whale_watch.

  Connector

• burn_tracker

  whale-insights

  Token burn tracker — Recent on-chain token burn events (with explorer-verifiable tx) plus a 14-day daily burn-volume history, sourced from Etherscan, Blockchair, and Solana RPC. Free preview: top 3 events; the full list requires a Weekly Alpha subscription. Served from cache (no per-request AI cost). Cached ~5min.

  Connector

• x711_hive_read

  x711 — Universal Agent Gas Station

  Query The Hive — x711's collective agent memory. The Hive contains knowledge contributed by all agents that have ever used x711: gas patterns, contract wisdom, DeFi discoveries, cross-chain insights, tool integration guides. Semantic search returns the most relevant entries ranked by similarity. Use before tx_simulate to get contract-specific hive wisdom. Use as a knowledge base for any on-chain or AI-agent topic. Returns: { query, entries: Array<{ content, namespace, domain_tags, agent_id }>, count: number }. Free tier: 10 calls/day.

  Connector

• framefetch_platform_capabilities

  framefetch

  Return a JSON matrix of which data types (metadata, insights, transcript, frames) each supported platform provides — YouTube, YouTube Shorts, TikTok, Instagram Reels, Pinterest, Reddit. Purpose: check what is available for a platform BEFORE calling framefetch_extract, so you only request supported fields. No input required.

  Connector

• get_locality_insights

  AVnester — Indian Real Estate Intelligence

  Get aggregated insights for a Coimbatore locality: avg price, supply count, demand pulse, livability/investment grade, highlights, watchouts, 12-month priceTrends, and strengthTags. Use when the user asks "what is X locality like" about a Coimbatore neighborhood. Out-of-scope cities return supported=false; surface the scopeMessage to the user. Always surface the disclaimer field when returning livability or investment grade.

  Connector

• resolve_reasoning

  AI Reasoning Commons

  🔥 TOKEN SAVER: Before you spend tokens solving from scratch, check if 128+ reasoning objects already have the answer. Avg savings ~2,400 tokens per HIT. On HIT: get solution, key insights, consensus score, and ready-to-use provenance block. On MISS: you solve it, store it, earn points. Always call this first — it costs almost nothing and can save thousands of tokens. Use auto_route=true to auto-create a claimable task on MISS.

  Connector

• x711_vault_write

  x711 — Universal Agent Gas Station

  Write a private memory pack to your agent's personal vault. Persists facts, insights, context, and skills with auto-decay timers (context 7d, insight 90d, skill 180d, fact 365d). First 500 lifetime writes free, then $0.01/pack. Mark immortal=true (+$0.05) to disable decay forever. Vault is private — only your agent can read it. Pair with vault_query for recall. Requires API key.

  Connector

• restore_context

  Penfield Memory

  Resume work from a saved cognitive context. This provides a narrative briefing to quickly orient you to: - The investigation that was in progress - Key discoveries and insights made - Current hypotheses being tested - Open questions and blockers - Suggested next steps - All relevant memories with their connections The briefing reconstructs the cognitive state, not just the data. You'll understand not just WHAT was discovered, but WHY it matters and HOW the understanding evolved. Example of what you'll receive: "[API Timeout Investigation - Resuming after 2 hours] SITUATION: You were investigating production API timeouts that occur at exactly batch_size=100. This investigation started when user reported timeouts only in production, not staging. PROGRESS MADE: - Identified sharp cutoff at 100 items (not gradual degradation) - Disproved connection pool theory (monitoring showed only 43/200 connections used) - Found root cause: MAX_BATCH_SIZE=100 hardcoded in batch_handler.py:147 - Confirmed staging uses different config override (MAX_BATCH_SIZE=500) EVIDENCE CHAIN: User report → Reproduced locally → Noticed batch_size correlation → Searched codebase for limits → Found MAX_BATCH_SIZE → Checked staging config → Discovered config difference CORRECTED MISUNDERSTANDINGS: - Initially thought it was Redis connection exhaustion (disproven by monitoring) - Assumed gradual performance degradation (actually sharp cutoff) - Thought staging/production were identical (config differs) CURRENT HYPOTHESIS: Production deployment uses default MAX_BATCH_SIZE=100 from code, while staging has environment variable override. Fix requires either code change or prod config update. BLOCKED ON: Need production deployment access to apply fix. User considering whether to change code default or add production environment variable. RECOMMENDED NEXT STEPS: 1. Verify production environment variables (check if MAX_BATCH_SIZE is set) 2. If not set, add MAX_BATCH_SIZE=500 to production config 3. If code change preferred, update default in batch_handler.py 4. Run load test with batch_size=100-500 range to verify fix KEY MEMORIES FOR REFERENCE: - 'Initial timeout report from user' - Starting point of investigation - 'MAX_BATCH_SIZE discovery' - Root cause identification - 'Redis monitoring data' - Evidence disproving connection theory - 'Staging config analysis' - Explanation for environment difference" This cognitive handoff ensures you can continue the work with full understanding of the problem space, previous attempts, and current direction. The narrative preserves not just facts but the reasoning process, mistakes made, and lessons learned. SPECIAL CASE: restore_context("awakening") The name "awakening" is reserved for loading the user's personality configuration. This loads the Awakening Briefing which includes: - Selected persona identity and voice style - Custom personality traits (Premium+ users) - Any quirks and boundaries from the persona preset Args: name: Name or ID of context to restore. Can be: - Context name (exact match, case-sensitive) - Context UUID (from list_contexts output) - "awakening" for personality briefing limit: Maximum number of memories to restore (default 20) ctx: MCP context (automatically provided) Returns: Dict with: - success: Whether restoration succeeded - description: The cognitive handoff briefing - memories: List of relevant memories - context_id: The restored context identifier

  Connector

• x711_vault_query

  x711

  Semantic vector search across your private vault. Returns ranked memories by cosine similarity × confidence × importance. Recalls the most relevant facts, insights, and skills your agent has accumulated. FREE always. Requires API key (reads your vault only — other agents cannot access it).

  Connector

• x711_hive_read

  x711

  Query The Hive — x711's collective agent memory. The Hive contains knowledge contributed by all agents that have ever used x711: gas patterns, contract wisdom, DeFi discoveries, cross-chain insights, tool integration guides. Semantic search returns the most relevant entries ranked by similarity. Use before tx_simulate to get contract-specific hive wisdom. Use as a knowledge base for any on-chain or AI-agent topic. Returns: { query, entries: Array<{ content, namespace, domain_tags, agent_id }>, count: number }. Free tier: 10 calls/day.

  Connector

• reflect

  Penfield Memory

  Reflect on recent thoughts and patterns. Analyzes recent activity to identify patterns, topics, and insights. Useful for understanding "what have I been thinking about?" By default, only returns user-created memories (not document chunks). Set include_documents=True to also include chunks from uploaded documents. ⚠️ EXPERIMENTAL: - Importance weighting in results not yet implemented. Importance scores are stored but don't affect ranking. Args: time_window: Time period to analyze ('recent', 'today', 'week', 'month', '1d', '7d', '30d', '90d') include_documents: Whether to include document chunks (default: False, only user memories) start_date: Filter memories created on or after this date (ISO 8601: '2025-01-01' or '2025-01-01T00:00:00Z') end_date: Filter memories created on or before this date (ISO 8601: '2025-01-09' or '2025-01-09T23:59:59Z') ctx: MCP context (automatically provided) Returns: Dict with analysis including top memories, active topics, patterns, insights, and any saved contexts (checkpoints) created in the window. Examples: >>> await reflect("recent") {'success': True, 'memories_analyzed': 50, 'active_topics': [...], 'contexts': [...], ...} >>> await reflect("week", include_documents=True) {'success': True, 'memories_analyzed': 150, ...} # includes document chunks >>> await reflect(start_date="2025-01-01", end_date="2025-01-07") {'success': True, 'memories_analyzed': 25, ...} # memories from first week of January

  Connector

• buy_studio

  moltdj

  Buy a Studio subscription for $25 USDC (30 days). Requires authentication. This endpoint returns HTTP 402 with x402 payment instructions. Your x402-enabled HTTP client will handle the USDC payment automatically. After payment, you get Studio tier: 20 tracks/day, 5 episodes/week, video, audience insights, and more.

  Connector

• substrate_breakthroughs

  Zambo AI Tools

  Zambo Stack — Fetch the latest AI-generated scientific breakthroughs from SubstrateLayer — a live autonomous research engine running 24/7. 64,000+ total breakthroughs across 6 domains: AI, energy, biology, climate, economics, materials. Returns the 12 most recent discoveries with title, domain, impact score, key insights, and share URL. Free, no auth. Use when you need cutting-edge research signals, cross-domain synthesis, or want to ground a strategy in the latest scientific thinking.

  Connector

• export_dataset

  Trillboards DOOH Advertising

  Export observation data as a structured dataset. Supports filtering by time, geography, venue type, and observation family. Applies k-anonymity (k=5) to protect individual privacy. Queries the relevant table based on the selected dataset type, applies filters, enforces k-anonymity by suppressing groups with fewer than 5 observations, and returns structured data. WHEN TO USE: - Exporting audience data for external analysis - Building datasets for machine learning or reporting - Getting structured vehicle or commerce data for a specific time/place - Creating cross-signal datasets for correlation analysis RETURNS: - data: Array of dataset rows (schema varies by dataset type) - metadata: { row_count, k_anonymity_applied, export_id, dataset, filters_applied, time_range } - suggested_next_queries: Related exports or analyses Dataset types: - observations: Raw observation stream data (all families) - audience: Audience-specific data (face_count, demographics, attention, emotion) - vehicle: Vehicle counting and classification data - cross_signal: Pre-computed cross-signal correlation insights EXAMPLE: User: "Export audience data from retail venues last week" export_dataset({ dataset: "audience", filters: { time_range: { start: "2026-03-09", end: "2026-03-16" }, venue_type: ["retail"] }, format: "json" }) User: "Get vehicle data near geohash 9q8yy" export_dataset({ dataset: "vehicle", filters: { time_range: { start: "2026-03-15", end: "2026-03-16" }, geo: "9q8yy" } })

  Connector