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

• fetchA

  OneQAZ Trading IntelligenceA

  Purpose: ChatGPT-connector-standard document fetch by id from `search` results. Namespaces: `tool:{name}` returns the tool's full documentation and how to call it; `resource:{uri}` returns the resource's live data (core resources resolved server-side — also the bridge for clients without MCP resource support, e.g. Gemini); `signal:{market}:{symbol}` returns the symbol's latest combined research signal. Triggers: ChatGPT connectors / Deep Research call this after `search`. Clients without MCP resource support can call it directly with a known resource id, e.g. fetch("resource:market://global/summary"). When to call: whenever the full content behind a search result id is needed. Prerequisites: a valid id — from `search` results or a known namespace id. Next steps: for tool docs, call the named tool via tools/call; for signals, get_signal_detail / explain_decision for deeper evidence. Caveats: uncovered resource uris return description-only text (no fabricated data). `text` is a JSON document for resource/signal ids. Output: {id, title, text, url, metadata, disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize GovernanceB

  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. $0.02 USDC per call.

  ConnectorNo auth

• ir_get_brief_templateA

  website-searchA

  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.

  ConnectorNo auth

• cti_get_cross_server_routesA

  website-searchA

  Get Lenny Zeltser's CTI 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 `cti_load_context`. 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.

  ConnectorNo auth

• vuln_get_brief_templateA

  website-searchA

  Get Lenny Zeltser's Vuln one-page executive brief template. Standalone variant of `vuln_get_template` for callers that only want the brief without the long-form report. This server never requests your vulnerability notes and instructs your AI to keep them local—the brief template and guidelines flow to your AI for local analysis.

  ConnectorNo auth

• assessment_get_brief_templateA

  website-searchA

  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.

  ConnectorNo auth

• calculate_loan_payoffA

  Senaro Personal FinanceA

  Calculation, not advice. Verify with a professional before acting. Standard amortizing loan calculator. Pick this for a single fixed-rate installment loan (mortgage, auto, student, personal) with one amortization schedule; pick `calculate_cc_payoff` for a credit card, which carries revolving balances, multiple APR segments, and a snowball or avalanche strategy this tool does not model. Returns: - monthly payment, total interest, and a payoff date (null when the loan does not clear within its term) - a bucketed Analysis suitable for chart rendering. The Analysis includes KPIs, annotations (e.g. crossover month), and summary strings When extra_monthly_payment is supplied, every computed figure in the response outside with_extra describes this loan without the extra payment, including monthly_payment, payoff_months, total_paid, total_interest, remaining_balance_at_term, payoff_date, months_to_halfway_principal, months_to_interest_flip, and every value under analysis; a warnings[] entry names this. The response includes `chart_hints` with rendering directives any client can use. The `senaro-charts` MCP server renders them locally over stdio only. This tool does not support output: 'inline' on any transport.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize HealthcareB

  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. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize Real EstateB

  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. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize Crypto & DeFiB

  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. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize IntelligenceB

  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. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize FinanceB

  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. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize OracleB

  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. $0.02 USDC per call.

  ConnectorNo auth

• feedbackA

  Marketing Intelligence & OptimizationA

  Save user feedback to the MultipleWords feedback API (POST /api/feedback). Call this only when a marketing_intelligence or marketing_intelligence_get_engine result has feedback_prompt.ready=true (the server asks after every 3 MCP runs — not per tool — and keeps asking until saved). Ask the user to pick a reaction and show the emojis: 😍 excellent, 😊 good, 😐 average, 😞 bad. Pass `reaction` (excellent|good|average|bad, or the emoji) and optional `feedback` comment text. If the user does not add a comment, omit `feedback` — the server stores the MCP name marketing_analysis. Do not send the analysis, test notes, or the user's question as the comment. Ask on the 3rd, 6th, 9th, … MCP run. Do not invent a reaction. Do not send user_name, email, is_login, or app_id — those are injected server-side from the authenticated session. When showing the saved reaction, include the matching emoji. When to call this tool: - User picks a reaction on the 3rd, 6th, 9th, … time this MCP is used - Collect a short comment plus 😍 😊 😐 😞 after an analysis - Save how the marketing analysis felt (excellent/good/average/bad) When NOT to call this tool: - Calling feedback before this MCP has been used 3 times - Asking after every tool call instead of every 3rd MCP run - Inventing a reaction the user did not choose - Passing user_name, email, is_login, or app_id (server injects them)

  ConnectorOAuth

• __unlock_blockchain_analysis__A

  blockscout-mcp-serverA

  Initializes a Blockscout MCP session: returns server reference data, the `blockscout-analysis` skill pointer, and the URI resolution rule. Call this tool exactly once per session, before any other tool, and reuse its payload for the rest of the session; do not call it again.

  ConnectorOAuth

• url_scanner_async_scanA

  PreClick — An MCP-native URL preflight scanning service for autonomous agents (formerly URLCheck).A

  Submit a URL for asynchronous security analysis. Returns immediately with a task_id. Poll with url_scanner_async_task_status to check progress, then url_scanner_async_task_result to get the scan result. Async counterpart of url_scanner_scan for clients without native MCP Tasks support.

  ConnectorNo auth

• get_track_recordA

  voxoddsA

  Get VoxOdds' audited AI-vs-market forecast track record. Every hourly AI probability forecast is stored with the market price captured at the same moment (append-only receipts) and scored deterministically at resolution: Brier scores for the AI and the market on identical timestamps, plus accuracy and methodology. Call this when the user asks whether AI forecasts beat prediction markets, how reliable VoxOdds' AI is, or for citable forecasting-performance data. Losses are published too — the record is auditable, not curated.

  ConnectorNo auth

• rating_get_sheetA

  website-searchA

  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.

  ConnectorNo auth

• ir_get_cross_server_routesA

  website-searchA

  Get Lenny Zeltser's IR 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 `ir_load_context`. This server never requests your incident notes and instructs your AI to keep them local—guidelines flow to your AI for local analysis.

  ConnectorNo auth

• cti_get_brief_templateA

  website-searchA

  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.

  ConnectorNo auth

• malware_get_templateA

  website-searchA

  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.

  ConnectorNo auth