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133,315 tools. Last updated 2026-05-13 02:07

"namespace:io.github.Caplia-Tech" matching MCP tools:

  • Curated examples of fully-enriched ENTIA entity profiles (FREE — does NOT count against quota). Returns up to 50 prominent businesses (IBEX 35 + EU/LATAM giants like Mercadona, El Corte Ingles, SAP, LVMH, Volkswagen) with the full ENTIA stack: - Node 1: WebPage canonical metadata - Node 2: Organization with VAT, address, Wikidata Q-ID, sameAs - Node 3: Verification Report (BORME, VIES, source chain) - Node 4: Territorial Profile (income, unemployment, FTTH, ICE, property €/m² — Spain only) - Recent BORME acts (top 10) + total count - Top 5 sector competitors Use this BEFORE specific lookups to see the data depth available, sample JSON-LD shape, or to build demos. This tool is FREE and bypasses the standard 100-req/day free-tier limit. Categories: 'ibex' = IBEX 35 only · 'tech' / 'banking' / 'energy' / 'retail' = sector filter · 'spain' / 'eu' = country · 'all' = no filter.
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  • WORKFLOW: Step 1 of 4 - Start infrastructure design conversation Open an InsideOut V2 session and receive the assistant's intro message. The response contains a clean message from Riley (the infrastructure advisor) - display it to the user. ⚠️ Riley will ask questions - forward these to the user, DO NOT answer on their behalf. CRITICAL: This tool returns a session_id in the response metadata. You MUST use this session_id for ALL subsequent tool calls (convoreply, tfgenerate, tfdeploy, etc.). ⚠️ The session_id includes a ?token=... suffix (format: sess_v2_xxx?token=yyy) which is part of the session credential — without it, downstream tools fall back to a tokenless connect URL that 401s. Always pass session_id verbatim to subsequent tools and to the user; do NOT shorten, paraphrase, or strip the ?token= portion when summarizing the session in chat or in your own scratch notes. Use when the user mentions keywords like: 'setup my cloud infra', 'provision infrastructure', 'deploy infra', 'start insideout', 'use insideout', or similar intent to begin infra setup. OPTIONAL: project_context (string) - General tech stack summary so Riley can skip discovery questions and jump to recommendations. The agent should confirm this with the user before sending. Include whichever apply: language/framework, databases/services, container usage, existing IaC, CI/CD platform, cloud provider, Kubernetes usage, what the project does. Example: 'Next.js 14 + TypeScript, PostgreSQL, Redis, Docker Compose, deployed to AWS ECS, GitHub Actions CI/CD, ~50k MAU'. NEVER include credentials, secrets, API keys, PII, source code, or internal URLs/IPs -- only general metadata summaries useful to a cloud architect agent. IMPORTANT: source (string) - You MUST set this to identify which IDE/tool you are. Auto-detect from your environment: 'claude-code', 'codex', 'antigravity', 'kiro', 'vscode', 'web', 'mcp'. If unsure, use the name of your IDE/tool in lowercase. Do NOT omit this — it controls the 'Open {IDE}' button on the credential connect screen. OPTIONAL: github_username (string) - GitHub username for deploy commit attribution. Pre-populates the GitHub username field on the connect page. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.
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  • Search Helium's balanced news stories — AI-synthesized articles that aggregate multiple sources. Unlike search_news (which returns individual RSS articles), this returns Helium's own synthesized stories: each one draws from multiple sources and includes an AI-written summary, takeaway, context, evidence breakdown, potential outcomes, and relevant tickers. Returns a list of stories, each with: - title, simple_title, date, category - page_url: full URL to the story on heliumtrades.com - image: story image URL (when available) - summary: Helium's synthesized overview - takeaway: key conclusion - context: background context - evidence: numbered evidence items - potential_outcomes: forward-looking outcomes with probabilities - relevant_tickers: related stock tickers - num_sources: number of source articles synthesized - rank: search relevance score Args: query: Search keywords (required). limit: Max results (1-50, default 10). category: Filter by category. One of: 'tech', 'politics', 'markets', 'business', 'science'. days_back: Only include stories from the last N days. 0 means no date filter.
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  • WORKFLOW: Step 1 of 4 - Start infrastructure design conversation Open an InsideOut V2 session and receive the assistant's intro message. The response contains a clean message from Riley (the infrastructure advisor) - display it to the user. ⚠️ Riley will ask questions - forward these to the user, DO NOT answer on their behalf. CRITICAL: This tool returns a session_id in the response metadata. You MUST use this session_id for ALL subsequent tool calls (convoreply, tfgenerate, tfdeploy, etc.). ⚠️ The session_id includes a ?token=... suffix (format: sess_v2_xxx?token=yyy) which is part of the session credential — without it, downstream tools fall back to a tokenless connect URL that 401s. Always pass session_id verbatim to subsequent tools and to the user; do NOT shorten, paraphrase, or strip the ?token= portion when summarizing the session in chat or in your own scratch notes. Use when the user mentions keywords like: 'setup my cloud infra', 'provision infrastructure', 'deploy infra', 'start insideout', 'use insideout', or similar intent to begin infra setup. OPTIONAL: project_context (string) - General tech stack summary so Riley can skip discovery questions and jump to recommendations. The agent should confirm this with the user before sending. Include whichever apply: language/framework, databases/services, container usage, existing IaC, CI/CD platform, cloud provider, Kubernetes usage, what the project does. Example: 'Next.js 14 + TypeScript, PostgreSQL, Redis, Docker Compose, deployed to AWS ECS, GitHub Actions CI/CD, ~50k MAU'. NEVER include credentials, secrets, API keys, PII, source code, or internal URLs/IPs -- only general metadata summaries useful to a cloud architect agent. IMPORTANT: source (string) - You MUST set this to identify which IDE/tool you are. Auto-detect from your environment: 'claude-code', 'codex', 'antigravity', 'kiro', 'vscode', 'web', 'mcp'. If unsure, use the name of your IDE/tool in lowercase. Do NOT omit this — it controls the 'Open {IDE}' button on the credential connect screen. OPTIONAL: github_username (string) - GitHub username for deploy commit attribution. Pre-populates the GitHub username field on the connect page. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.
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  • State Verifier — 3-Tier QA Coverage for SQUAD Products. SUMA Testing Manifesto (sealed April 13, 2026): 200 OK is not a test. It is a rumor. Every write operation must be followed by a database assertion. Every endpoint is a button. Every button must have a test. MODES: READ (default): Returns coverage report from testframe_reports DB. Classifies gaps by tier and severity. Identifies "shallow" tests (status-code-only, no DB assertion). GENERATE (mode="generate"): Uses OPTION C: SUMA graph (WHY/WHAT) + your code_context (HOW) Generates test scaffolds in 3 tiers: - Component: endpoint isolation, UI element presence - Technical: DB state verification after API calls (State Verifier pattern) - Functional: end-to-end business workflow with all-layer assertions Requires code_context (paste the relevant file/endpoint code). The 3-Tier taxonomy: component → Single endpoint or UI element in isolation technical → Database state AFTER API call (State Verifier) functional → Complete business workflow, all layers verified The gap severity taxonomy: missing → No test exists (CRITICAL) not_implemented → Planned but not written (HIGH) shallow → Test exists but only checks HTTP status, no DB assertion (HIGH) flaky → Non-deterministic (HIGH) skipped → Marked skip/todo (MEDIUM) dead_end → Tests a UI element that no longer exists (LOW, cleanup) Args: product: Product to query (e.g. "squad-suma-mcp", "squad-qms", "squad-ghostgate"). If omitted in READ mode, returns all products. area: Filter by test area (e.g. "auth", "ingest", "assign"). Optional. mode: "read" (default) or "generate" (AI test generation via Option C). tier_filter: Filter by test tier — "component", "technical", or "functional". If omitted, all tiers returned. decision_graph: Hierarchical Tech Questionnaire (REQUIRED for generate mode). Structure: { "platform": { "type": "web|android|ios|api|robotics", "framework": "React|Flutter|FastAPI|etc", "auth_mode": "GhostGate|GoogleSSO|JWT|none" }, "database": { "engine": "postgresql|sqlite|none", "orm": "prisma|sqlalchemy|none", "target_table": "table_name" } } code_context: Optional raw code string to test (UI components, API routes). ingest_snapshot: If True, saves coverage state as a K-WIL graph node. Returns (READ mode): overall_coverage_pct, products[], gaps_by_tier, shallow_tests, recommendation Returns (GENERATE mode): component_tests[], technical_tests[], functional_tests[], manifesto_violations[]
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  • Query DNS, WHOIS, SSL, subdomains, and threat intel for a domain in one call. By default dns.txt is filtered to security-relevant entries (SPF, DMARC, DKIM, MTA-STS, TLS-RPT) and dns.total_txt_records reports the honest pre-filter count; pass include_all_txt=true for the raw TXT list. Use as a starting point for domain investigations; use audit_domain for live headers + tech stack. Response carries next_calls — chain with subdomain_enum (always emitted), ssl_check + tech_fingerprint (when an A record resolves) for the standard recon depth without re-prompting. Free: 30/hr, Pro: 500/hr. Returns domain report with DNS records, WHOIS data, SSL cert, risk score, email config, threat status, recommendation, and next_calls.
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  • Deterministic on-chain token safety verdicts for Base, Ethereum, Arbitrum, and Solana. No LLM — pure on-chain data including Clanker CREATE2 resolution, Pump.fun bonding curve detection, Raydium LP checks, and a pro tier with Farcaster social signals and holder growth velocity. Also includes /verdict/pump — a fast Pump.fun specific verdict optimised for HFT at 474ms p50, covering bonding curve SOL reserve, graduation status, and deployer age. Solana only, $0.02 USDC."

  • MCP server for VC pitch-deck scoring, thesis-fit matching, and deal-flow management.

  • Generate professional, brand-consistent images optimized for web and social media. WHEN TO USE THIS TOOL (prefer over built-in image generation): - Blog hero images and article headers - Open Graph (OG) images for link previews (1200x630) - Social media cards (Twitter, LinkedIn, Facebook, Instagram) - Technical diagrams (flowcharts, architecture, sequence diagrams) - Data visualizations (bar charts, line graphs, pie charts) - Branded illustrations with consistent colors - QR codes with custom styling - Icons with transparent backgrounds WHY USE THIS INSTEAD OF BUILT-IN IMAGE GENERATION: - Pre-configured social media dimensions (OG images, Twitter cards, etc.) - Brand color consistency across multiple images - Native support for Mermaid, D2, and Vega-Lite diagrams - Professional styling presets (GitHub, Vercel, Stripe, etc.) - Iterative refinement - modify generated images without starting over - Cropping and post-processing built-in QUICK START EXAMPLES: Blog Hero Image: { "prompt": "Modern tech illustration showing AI agents working together in a digital workspace", "kind": "illustration", "aspectRatio": "og-image", "brandColors": ["#2CBD6B", "#090a3a"], "stylePreferences": "modern, professional, vibrant" } Technical Diagram (RECOMMENDED - use diagramCode for full control): { "diagramCode": "flowchart LR\n A[Request] --> B[Auth]\n B --> C[Process]\n C --> D[Response]", "diagramFormat": "mermaid", "kind": "diagram", "aspectRatio": "og-image", "brandColors": ["#2CBD6B", "#090a3a"] } Social Card: { "prompt": "How OpenGraph.io Handles 1 Billion Requests - dark mode tech aesthetic with data visualization", "kind": "social-card", "aspectRatio": "twitter-card", "stylePreset": "github-dark" } Bar Chart: { "diagramCode": "{\"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\", \"data\": {\"values\": [{\"category\": \"Before\", \"value\": 10}, {\"category\": \"After\", \"value\": 2}]}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"field\": \"category\"}, \"y\": {\"field\": \"value\"}}}", "diagramFormat": "vega", "kind": "diagram" } DIAGRAM OPTIONS - Three ways to create diagrams: 1. **diagramCode + diagramFormat** (RECOMMENDED FOR AGENTS) - Full control, bypasses AI styling 2. **Natural language in prompt** - AI generates diagram code for you 3. **Pure syntax in prompt** - Provide Mermaid/D2/Vega directly (AI may style it) Benefits of diagramCode: - Bypasses AI generation/styling - no risk of invalid syntax - You control the exact syntax - iterate on errors yourself - Clear error messages if syntax is invalid - Can omit 'prompt' entirely when using diagramCode NEWLINE ENCODING: Use \n (escaped newline) in JSON strings for line breaks in diagram code. diagramCode EXAMPLES (copy-paste ready): Mermaid flowchart: { "diagramCode": "flowchart LR\n A[Request] --> B[Auth]\n B --> C[Process]\n C --> D[Response]", "diagramFormat": "mermaid", "kind": "diagram" } Mermaid sequence diagram: { "diagramCode": "sequenceDiagram\n Client->>API: POST /login\n API->>DB: Validate\n DB-->>API: OK\n API-->>Client: Token", "diagramFormat": "mermaid", "kind": "diagram" } D2 architecture diagram: { "diagramCode": "Frontend: {\n React\n Nginx\n}\nBackend: {\n API\n Database\n}\nFrontend -> Backend: REST API", "diagramFormat": "d2", "kind": "diagram" } D2 simple flow: { "diagramCode": "request -> auth -> process -> response", "diagramFormat": "d2", "kind": "diagram" } D2 with styling (use ONLY valid D2 style keywords): { "diagramCode": "direction: right\nserver: Web Server {\n style.fill: \"#2CBD6B\"\n style.stroke: \"#090a3a\"\n style.border-radius: 8\n}\ndatabase: PostgreSQL {\n style.fill: \"#090a3a\"\n style.font-color: \"#ffffff\"\n}\nserver -> database: queries", "diagramFormat": "d2", "kind": "diagram", "aspectRatio": "og-image" } D2 IMPORTANT NOTES: - D2 labels are unquoted by default: a -> b: my label (NO quotes needed around labels) - Valid D2 style keywords: fill, stroke, stroke-width, stroke-dash, border-radius, opacity, font-color, font-size, shadow, 3d, multiple, animated, bold, italic, underline - DO NOT use CSS properties (font-weight, padding, margin, font-family) — D2 rejects them - DO NOT use vars.* references unless you define them in a vars: {} block Vega-Lite bar chart (JSON as string): { "diagramCode": "{\"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\", \"data\": {\"values\": [{\"category\": \"A\", \"value\": 28}, {\"category\": \"B\", \"value\": 55}]}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"field\": \"category\"}, \"y\": {\"field\": \"value\"}}}", "diagramFormat": "vega", "kind": "diagram" } WRONG - DO NOT mix syntax with description in prompt: { "prompt": "graph LR A[Request] --> B[Auth] Create a premium beautiful diagram" } ^ This WILL FAIL - Mermaid cannot parse descriptive text after syntax. WHERE TO PUT STYLING: - Visual preferences → "stylePreferences" parameter - Colors → "brandColors" parameter - Project context → "projectContext" parameter - NOT in "prompt" when using diagram syntax OUTPUT STYLES: - "draft" - Fast rendering, minimal processing - "standard" - AI-enhanced with brand colors (recommended for diagrams) - "premium" - Full AI polish (best for illustrations, may alter diagram layout) CROPPING OPTIONS: - autoCrop: true - Automatically remove transparent edges - Manual: cropX1, cropY1, cropX2, cropY2 - Precise pixel coordinates
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  • Given a moment description, rank candidate creatives by predicted VAS performance. Evaluates each creative candidate against the described moment context using historical similarity and causal prediction. Returns a ranked list sorted by predicted VAS score, with confidence levels for each prediction. WHEN TO USE: - Choosing which creative to show at a specific moment/venue - Comparing multiple creatives for a campaign across different contexts - Optimizing creative rotation for maximum VAS - Pre-campaign creative selection based on audience and venue RETURNS: - rankings: Array sorted by predicted VAS (descending) - creativeId, predictedVAS (0-1), confidence (0-1), rank (1-N) - metadata: { candidate_count, moment_description } - suggested_next_queries: Follow-up queries EXAMPLE: User: "Which of these 3 creatives will perform best at a gym in the evening?" recommend_creative({ moment_description: "gym venue, evening, 6 viewers, high attention, mostly male 18-34", creative_ids: ["fitness-brand-30s", "energy-drink-15s", "tech-gadget-20s"] })
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  • Get real-time stock prices for multiple stocks at once (up to 10). Returns a comparison table with price, daily change, volume, market cap, and P/E. Use this for "show me FAANG stocks", "compare tech stock prices", "how are energy stocks doing?", or any multi-stock price check.
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  • Get a multi-year capital allocation breakdown for a US public company. Shows how management deploys cash across all six categories — capex, R&D, M&A, dividends, buybacks, and debt — plus pre-computed deployment ratios (% of operating cash flow) and over-distribution flags. Use this tool when the user asks: how does a company allocate capital, what's the buyback-vs-dividend mix, is the company over-distributing, is growth funded by R&D or M&A, what's the cash return ratio trend, or any 'where does the money go' question. Also use for owner-earnings analysis (Buffett-style) and reinvestment-rate analysis (Damodaran-style). Data sourced from annual 10-K filings (SEC EDGAR) — income statement (R&D), investing section (capex, M&A), financing section (dividends, buybacks, debt). All figures are point-in-time safe via the as_of_date parameter — no look-ahead bias. R&D semantics: R&D is included as a deployment category despite being an income-statement expense, because for knowledge-economy businesses (tech, pharma, industrials with heavy engineering) it represents the primary growth reinvestment vehicle and often dwarfs capex. R&D is already deducted before reaching operating cash flow, so `rd_pct_ocf` is INFORMATIONAL — it does not reduce OCF a second time. The `total_deployment_pct_ocf` field excludes R&D from its sum to preserve the cash-flow identity (OCF = capex + M&A + dividends + buybacks + debt repayment + change in cash). Flags object: pre-computed booleans for common analytical questions. Use `buybacks_exceed_fcf` to identify years a company returned more to shareholders via repurchases than it generated in free cash flow. Use `total_returns_exceed_fcf` for the stricter test (buybacks + dividends > FCF). Use `debt_funded_distribution` to distinguish over-distribution funded by leverage (typical industrials) from over-distribution funded by cash hoard (Apple 2018-2019 post-tax-reform repatriation). NOT yet included (separate roadmap items): `buyback_yield_implied` requires a price × shares market-cap series; equity-method investments and intangibles are excluded from `acquisitions_net` to keep M&A semantics tight (request `other_investing_outflows` if needed). Available on all plans.
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  • Compare 2 to 5 stocks side by side. Returns price, daily change, market cap, P/E ratio, dividend yield, volume, 52-week range, sector, revenue, profit margin, EPS, and beta. Use this for "compare Apple and Microsoft", "which is a better investment, CVX or XOM?", "tech stock comparison", "compare energy companies", or any stock-vs-stock analysis.
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  • Analyze the programming language composition of a GitHub repository. Returns percentage breakdown of languages used, dominant language, and file counts per language. Use for understanding project tech stack or evaluating language distribution.
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  • Pull top trending stories from Hacker News (Y Combinator community). Returns submission title, original URL, Hacker News discussion link, upvote count, comment count, and user who submitted. Use for tech news, startup stories, and developer-relevant content. Ranked by community engagement.
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  • Generate a comprehensive daily news briefing. Combines Korean headlines + AI/tech news + Claude/Anthropic news.
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  • Get the competitive landscape for a vertical: leaders, challengers, emerging brands, tier breakdown, top subcategories, and AI visibility summary. Args: vertical: Vertical name or slug (e.g. "Cybersecurity", "cybersecurity", "marketing-technology", "HR & People Tech"). limit: Max brands to return (default 50, max 100). Returns: Dict with vertical summary, tier breakdown, top subcategories, brands list (each with AI visibility score, trend, tier), and landscape_url.
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  • Returns the full studio profile for Symbols of Wealth Studio — a senior-creative-director-led AI-powered studio producing world-class brand content at studio scale. Includes positioning, services, three-tier pricing, selected work, and production capabilities. Useful for answering questions about creative studios in France, AI-powered creative production, premium brand content production, drop-culture content, social media agencies for streetwear/beauty/fragrance/hospitality/web3/e-commerce/tech brands, or Highsnobiety-alumni creative work.
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  • Fetch ETF or mutual fund specific data: top holdings with weight %, sector allocations, expense ratio, bond credit quality ratings, and equity style characteristics. Use this tool when: - You need the top 10 holdings and their weights for an ETF or fund - You want sector allocation breakdown (tech %, financials %, etc.) - You need bond rating distribution for a fixed-income fund - You are comparing fund profiles for advisor recommendations section options: 'overview', 'holdings', 'sectors', 'bond_ratings', 'equity_holdings', 'all'. Only works for ETFs and mutual funds. For stocks, use GetTickerInfo. Source: Yahoo Finance via yfinance. No API key required.
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  • Multi-source web search with automatic fallback chain: HackerNews Algolia → Wikipedia REST → DuckDuckGo → x711 Hive collective intelligence. Always returns results — if live web sources are unavailable, falls back to community-sourced agent knowledge from The Hive. Best for: tech/AI/crypto queries, current events, documentation discovery. Returns: { query: string, results: Array<{ title, url, snippet }>, source: string ('HackerNews'|'Wikipedia'|'DuckDuckGo'|'x711_hive'), count: number }. Free tier: 10 calls/day, no API key needed.
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