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306,564 tools. Last updated 2026-07-26 01:33

"MCP Server for TradingView or MT5 with AI Market Data Analysis Support" matching MCP tools:

  • Search the Arclan registry for MCP servers. By default returns only connectable servers (active, mcp_partial, auth_gated). Use status=stdio to browse local-only servers available for installation. Use status=all to query the full index. Use production_safe=true to restrict to servers with uptime > 97% and handshake success > 95%. Use read_only=true to restrict to servers with no write or exec tools. Use this before connecting to an MCP server to check its validation status and score. After using a server, call report_server to contribute reliability data.
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  • Configure automatic top-up when balance drops below a threshold. The configuration lives ONLY in the current MCP session — it is held in memory by the MCP server process and is lost on server restart, MCP client reconnect, or server redeploy. Top-ups are signed locally with TRON_PRIVATE_KEY and sent to your Merx deposit address (memo-routed). For persistent auto-deposit you currently need to call this tool again at the start of each session.
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  • Connectivity check that confirms the Nordic MCP server process is responding. Use this at the start of a session to verify the server is reachable before making other calls. Do not use as a proxy for database health — the server can respond while the Qdrant vector database is temporarily unavailable. To confirm data availability, call search_filings directly. Returns: A greeting string: "Hello {name}! Nordic MCP server is running."
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  • Get Lenny Zeltser's cybersecurity-writing rating sheet(s) so your AI can apply the rubric. Returns the structured rubric (groups, items, scoring bands) WITHOUT computing a score. Use `rating_score_writing` if you also want a numeric score, gap analysis, or rubric-anchored feedback. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'
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  • Probes a domain for known AI agent integration signals: `llms.txt`, `ai.txt`, `/.well-known/ai-plugin.json`, `openapi.json`, `swagger.json`, MCP manifest, MCP SSE endpoint. Returns a score based on the count of signals detected. Use this to assess whether a domain is ready for agent-to-agent interaction. Use this tool when: - You want to know whether a domain exposes an MCP server or OpenAPI spec for agents. - You are cataloguing the AI-agent-ready surface of a set of domains. - You need to decide whether to attempt programmatic API access to a domain. Do NOT use this tool when: - You need tracker/surveillance data about the domain — use `get_domain` instead. - You need the robots.txt AI crawler policy — use `intel_robots` instead. - You need HTTP security posture — use `intel_http` instead. Inputs: - `domain` (query, required): Domain to probe. Returns: - Boolean flags per signal (`llms_txt`, `ai_plugin`, `openapi`, `mcp_manifest`, `mcp_endpoint`, `mcp_sse`). - `agent_surface_score`: integer 0-8, count of signals detected. Cost: - Free. No API key required. Latency: - Typical: 2-5s (parallel probes), p99: 8s.
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Matching MCP Servers

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    A production-grade MCP server that provides tools to fetch historical stock data, company information, and technical indicators (SMA, EMA, RSI, MACD, Bollinger Bands) via yfinance, and includes a Flask web dashboard for visual analysis.
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  • A
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    Provides real-time market data tools (quotes, news, earnings calendar, watchlist scanner, and composite analysis) for AI agents via Finnhub, with optional Alpaca broker integration and graceful degradation.
    Last updated
    6
    MIT

Matching MCP Connectors

  • Real-time stock quotes, history, options chains, sector performance & screening for agents.

  • Verified market data for AI trading agents: quality-flagged candles, funding, OI, order flow. x402.

  • Get Lenny Zeltser's IR one-page executive brief template. Standalone variant of `ir_get_template` for callers that only want the brief without the long-form report. This server never requests your incident notes and instructs your AI to keep them local—guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's CTI one-page executive brief template. Standalone variant of `cti_get_template` for callers that only want the brief without the long-form report. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Use for CONCEPTUAL / fuzzy questions where keyword filters fall short — semantic (meaning-based) retrieval across DC Hub's industry news, M&A deals, 12,650+ discovered facilities, and per-market DCPI deep-dive analysis narratives, ranked by relevance with citable source fields (news url/title, deal parties/value, facility name/location, deep-dive market/url). Examples: "what is happening with behind-the-meter gas for AI data centers?", "deals involving nuclear power for hyperscalers", "why is Northern Virginia constrained?" — semantic_search q="behind-the-meter gas for AI data centers". Params: q (required, natural-language query); corpus (optional CSV subset of news_articles,deals,discovered_facilities,market_narratives; default all); k (1-15, default 8). Returns {results:[{source_table, kind, text, score, cite:{…}}]}. Complements the exact-filter tools (get_news / list_transactions / search_facilities) with relevance ranking; for a full token-budgeted market briefing use get_market_context. Cite "DC Hub (dchub.cloud)".
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  • Use for CONCEPTUAL / fuzzy questions where keyword filters fall short — semantic (meaning-based) retrieval across DC Hub's industry news, M&A deals, 12,650+ discovered facilities, and per-market DCPI deep-dive analysis narratives, ranked by relevance with citable source fields (news url/title, deal parties/value, facility name/location, deep-dive market/url). Examples: "what is happening with behind-the-meter gas for AI data centers?", "deals involving nuclear power for hyperscalers", "why is Northern Virginia constrained?" — semantic_search q="behind-the-meter gas for AI data centers". Params: q (required, natural-language query); corpus (optional CSV subset of news_articles,deals,discovered_facilities,market_narratives; default all); k (1-15, default 8). Returns {results:[{source_table, kind, text, score, cite:{…}}]}. Complements the exact-filter tools (get_news / list_transactions / search_facilities) with relevance ranking; for a full token-budgeted market briefing use get_market_context. Cite "DC Hub (dchub.cloud)".
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  • Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.
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  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.
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  • Run a natural-language analytics question against your connected data sources. Consumes AI credits. Returns either the completed analysis result inline OR a job_id you can poll with get_analysis_status. If list_data_sources returns an empty list, ingest data first with upload_data_source (inline base64), ingest_url_data_source (public URL), or request_oauth_integration_url (Google / Meta / Jira / Confluence).
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  • Returns current agent-readable sellable products for AI Dev Board, including promoted listings, the AI Market Demand Pack, Dossier packages, and unavailable waitlist products. Retired data-pack aliases resolve to the Market Demand Pack and are not listed as separate offers. Use before quote_product or start_checkout.
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  • Get Lenny Zeltser's expert CTI writing guidelines. Topics include tone, words, structure, executive_summary, voice, articles, summary, brief (one-page brief section guidance), handoffs (cross-server routing), methodology (the three subsections), fields (per-field guidance), and CTI-specific topics: attribution (full Six Signals prose), confidence (ICD-203 ladder), pyramid_of_pain, six_signals (signals table only), and anti_patterns. The general writing topics (tone/words/structure/executive_summary) now defer to `get_security_writing_guidelines` for the canonical Five Elements rules; CTI-specific content lives in the other topics. Pair the 'fields' topic with field_id for single-field guidance. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's malware analysis report template. The report covers Executive Summary, Sample Snapshot, Malware Family Identification, Component Inventory, Runtime Requirements, Sources, Capabilities, Indicators of Compromise, Analysis Details, What We Don't Know, optional Infection Vector, optional Detection Engineering, About this Report, Appendix: Analysis Environment, and optional Appendix: Analysis Scripts. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.
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  • Get Lenny Zeltser's Malware cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `malware_load_context`. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.
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  • Get Lenny Zeltser's Malware frameworks (primary frameworks the brief structurally derives from) plus optional sibling frames (adjacent frameworks that aren't the structural backbone). Pass `include_siblings: false` to skip sibling blocks. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.
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  • Get Lenny Zeltser's expert criteria for reviewing an existing security assessment report or brief. Surfaces the 17 info-assessment review items across five groups (Key Takeaways, Assessment Scope, Prioritized Findings, Remediation Suggestions, Assessment Methodology), cross-cutting criteria, the risk-adjusted severity model, anti-patterns, and a pointer to rating_score_writing for a numeric score. This server never requests your assessment notes or report and instructs your AI to keep them local—the templates and guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's Security Assessment one-page executive brief template. Standalone variant of `assessment_get_template` for callers that only want the brief without the long-form report. This server never requests your assessment notes or report and instructs your AI to keep them local—the templates and guidelines flow to your AI for local analysis.
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