"Villager 0.2.1rc1 Framework MCP Auto Penetration Testing" matching MCP tools:

• ateam_build_and_run

  ateam-mcp

  Build and deploy a governed AI Team solution in one step. ⚠️ HEAVIEST OPERATION (60-180s): validates solution+skills → deploys all connectors+skills to A-Team Core (regenerates MCP servers) → health-checks → optionally runs a warm test → auto-pushes to GitHub. AUTO-DETECTS GitHub repo: if you omit mcp_store and a repo exists, connector code is pulled from GitHub automatically. First deploy requires mcp_store. After that, write files via ateam_github_write, then just call build_and_run without mcp_store. For small changes to an already-deployed solution, prefer ateam_patch (faster, incremental). Requires authentication.

  Connector

• get_adoption_stage

  Stratalize Finance Intelligence

  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.

  Connector

• get_framework_docs

  main

  Retrieves authoritative documentation directly from the framework's official repository. ## When to Use **Called during i18n_checklist Steps 1-13.** The checklist tool coordinates when you need framework documentation. Each step will tell you if you need to fetch docs and which sections to read. If you're implementing i18n: Let the checklist guide you. Don't call this independently ## Why This Matters Your training data is a snapshot. Framework APIs evolve. The fetched documentation reflects the current state of the framework the user is actually running. Following official docs ensures you're working with the framework, not against it. ## How to Use **Two-Phase Workflow:** 1. **Discovery** - Call with action="index" to see available sections 2. **Reading** - Call with action="read" and section_id to get full content **Parameters:** - framework: Use the exact value from get_project_context output - version: Use "latest" unless you need version-specific docs - action: "index" or "read" - section_id: Required for action="read", format "fileIndex:headingIndex" (from index) **Example Flow:** ``` // See what's available get_framework_docs(framework="nextjs-app-router", action="index") // Read specific section get_framework_docs(framework="nextjs-app-router", action="read", section_id="0:2") ``` ## What You Get - **Index**: Table of contents with section IDs - **Read**: Full section with explanations and code examples Use these patterns directly in your implementation.

  Connector

• baselight_ping

  Baselight

  Simple ping test to verify MCP server is responding

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• get_adoption_stage

  Stratalize Crypto and DeFi Intelligence

  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.

  Connector

• get_adoption_stage

  Stratalize AI Governance Intelligence

  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.

  Connector

• vf_health

  VoiceFlip

  Returns VoiceFlip MCP server health and version metadata. No authentication required. Use this first to verify the server is reachable from your MCP client.

  Connector

• get_project_context

  main

  Captures the user's project architecture to inform i18n implementation strategy. ## When to Use **Called during i18n_checklist Step 1.** The checklist tool will tell you when to call this. If you're implementing i18n: 1. Call i18n_checklist(step_number=1, done=false) FIRST 2. The checklist will instruct you to call THIS tool 3. Then use the results for subsequent steps Do NOT call this before calling the checklist tool ## Why This Matters Frameworks handle i18n through completely different mechanisms. The same outcome (locale-aware routing) requires different code for Next.js vs TanStack Start vs React Router. Without accurate detection, you'll implement patterns that don't work. ## How to Use 1. Examine the user's project files (package.json, directories, config files) 2. Identify framework markers and version 3. Construct a detectionResults object matching the schema 4. Call this tool with your findings 5. Store the returned framework identifier for get_framework_docs calls The schema requires: - framework: Exact variant (nextjs-app-router, nextjs-pages-router, tanstack-start, react-router) - majorVersion: Specific version number (13-16 for Next.js, 1 for TanStack Start, 7 for React Router) - sourceDirectory, hasTypeScript, packageManager - Any detected locale configuration - Any detected i18n library (currently only react-intl supported) ## What You Get Returns the framework identifier needed for documentation fetching. The 'framework' field in the response is the exact string you'll use with get_framework_docs.

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• tronsave_get_user_permissions

  Tronsave MCP Testnet

  Read-only endpoint returning enabled permission operations (`autoSettings.permitOperations`) for the authenticated user. Requires a valid signature session and `mcp-session-id`. Use this before auto-sell mutations to confirm allowed operations; unauthorized/expired sessions can fail.

  Connector

• macro_coherence_v1

  macroooracle

  🔒 PREMIUM (requires x402 payment, $0.05): [v1.1 AGGREGATOR] Neutral aggregation of fed_rates_v3 + inflation_v3 + yield_curve_v3 in one call. Returns reason-code index, distributions of signal_strength/risk_band/staleness_tier, cross-tool concentration. No directional labels, no alignment score, no venue hints. One call replaces three for macro-aware agents. → Call via https://tooloracle.io/x402/macro/mcp/ with X-PAYMENT header. New wallets get 5 free units auto-applied.

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• setup_payment

  BoostedTravel

  Attach a payment card. Required before booking. For testing: {"token": "tok_visa"} For production: {"payment_method_id": "pm_xxx"} from Stripe.js One-time setup — all future charges are automatic. Requires GitHub star verification.

  Connector

• list_starter_prompts

  Vocab Voyage

  Lists Vocab Voyage's MCP starter prompts (also exposed via the standard MCP prompts/list endpoint). Useful for hosts that don't yet support prompts/list.

  Connector

• get_adoption_stage

  Stratalize Real Estate Intelligence

  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.

  Connector

• thinkneo_bridge_mcp_to_a2a

  ThinkNEO Control Plane

  Bridge an MCP tool call to an A2A (Agent-to-Agent Protocol) agent. Maps MCP tool name and parameters to the A2A task format, enabling interoperability between MCP servers and A2A agents. Returns a ready-to-send A2A task object with full protocol compliance. Translates the MCP tool_name and arguments into an A2A task, sends it to the target A2A agent, waits for completion, and translates the response back to MCP format. Use this to make any MCP tool accessible to A2A agents (Google's agent ecosystem). Requires authentication.

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• list_stack_tags

  Civis

  Returns the complete list of valid, canonical technology tags that Civis recognizes. Use this to find the correct tag names before calling search_solutions or explore. Tags are organized by category (ai, framework, database, language, etc.). Common aliases are auto-resolved (e.g. "nextjs" resolves to "Next.js"), but using canonical names is recommended.

  Connector

• ateam_test_status

  ateam-mcp

  Poll the progress of an async skill test. Returns iteration count, tool call steps, status (running/completed/failed), and result when done. (Advanced — use ateam_test_skill with wait=true for synchronous testing.)

  Connector

• search_vaadin_docs

  docs-mcp

  Search Vaadin documentation for relevant information about Vaadin development, components, and best practices. Uses hybrid semantic + keyword search. USE THIS TOOL for questions about: Vaadin components (Button, Grid, Dialog, etc.), TestBench, UI testing, unit testing, integration testing, @BrowserCallable, Binder, DataProvider, validation, styling, theming, security, Push, Collaboration Engine, PWA, production builds, Docker, deployment, performance, and any Vaadin-specific topics. When using this tool, try to deduce the correct development model from context: use "java" for Java-based views, "react" for React-based views, or "common" for both. Use get_full_document with file_paths containing the result's file_path when you need complete context.

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• thinkneo_bridge_a2a_to_mcp

  ThinkNEO Control Plane

  Bridge an A2A (Agent-to-Agent Protocol) task to an MCP server. Receives an A2A task, identifies the best matching MCP tool on the target server, executes it, and returns the result wrapped in A2A response format. Enables A2A agents to use any MCP server transparently. Extracts the intent from the A2A task, maps it to an MCP tool, calls the tool, and wraps the result in A2A response format. Use this to let A2A agents interact with any MCP server. Requires authentication.

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• get_device_ads

  Trillboards DOOH Advertising

  Get current ads scheduled for a device (for testing). WHEN TO USE: - Testing device ad delivery - Debugging which ads are being shown - Verifying ad targeting is working RETURNS: - ads: Array of advertisement objects - default_stream: Default content when no ads - schedule: Current ad schedule EXAMPLE: User: "What ads are showing on device P_abc123?" get_device_ads({ fingerprint: "P_abc123" })

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• baselight_ping

  baselight

  Simple ping test to verify MCP server is responding

  Connector