"Using Claude Desktop and Code MCP to manage Google Cloud Platform/Vertex AI projects" matching MCP tools:

• upload_media

  SendIt

  FOR CLAUDE DESKTOP ONLY (with filesystem access). For Claude.ai/web: Use create_upload_session instead - it provides a browser upload link. Upload local media to cloud storage, returning a public HTTPS URL. WHEN TO USE: • Instagram, LinkedIn, Threads, X: REQUIRED for local files before calling publish_content • TikTok: NOT NEEDED - pass local path directly to publish_content SUPPORTED FORMATS: • Images: jpg, png, gif, webp (max 10MB) • Videos: mp4, mov, webm (max 100MB) Returns { url: 'https://...' } for use in publish_content mediaUrl parameter.

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• convoopen

  InsideOut (Riley)

  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.). 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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• sti_get_platforms

  Stimulsoft Documentation MCP Server

  List every Stimulsoft product/platform that has indexed documentation available through this MCP server. Returns a JSON array of { id, name, description } objects covering the full Stimulsoft Reports & Dashboards product line (Reports.NET, Reports.WPF, Reports.AVALONIA, Reports.WEB for ASP.NET, Reports.BLAZOR, Reports.ANGULAR, Reports.REACT, Reports.JS, Reports.PHP, Reports.JAVA, Reports.PYTHON, Server API, etc.). CALL THIS FIRST when the user's question is ambiguous about which Stimulsoft platform they are using, or when you need to pick a valid `platform` value to pass into `sti_search`. The returned platform `id` values are the exact strings accepted by the `platform` parameter of `sti_search`. This tool is cheap (no OpenAI call, no vector search) — call it freely whenever you are unsure about platform naming.

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• discover_commerce_tools

  AXIS Toolbox — Agentic Commerce Codebase Intelligence

  Discover AXIS install metadata, pricing, and shareable manifests for commerce-capable agents. Free, no auth, and no mutation beyond read access. Example: call before wiring AXIS into Claude Desktop, Cursor, or VS Code. Use this when you need onboarding and ecosystem setup details. Use search_and_discover_tools instead for keyword routing or discover_agentic_purchasing_needs for purchasing-task triage.

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• credawait

  InsideOut (Riley)

  Wait for the user to securely connect their cloud account and subscribe to Luther Systems. Polls until credentials appear on the session. 🎯 USE THIS TOOL WHEN: tfdeploy returns an 'auth_required', 'no_credentials', or 'credentials_expired' error. The user needs to visit the connect URL to: 1. Connect their cloud credentials (AWS or GCP) 2. Sign up and subscribe to a Luther Systems plan (required for deployment) This secure connection allows InsideOut to deploy and manage infrastructure in the user's cloud account on their behalf. Credentials are handled securely and only used for deployment and management sessions. WORKFLOW: 1. FIRST: Present the connect URL and explanation to the user (from the tfdeploy error response) 2. THEN: Call this tool to begin polling for credentials 3. The user opens the URL in their browser to subscribe and add credentials 4. When credentials are found, inform the user and call tfdeploy to deploy IMPORTANT: Do NOT call this tool without first showing the connect URL to the user. The user needs to see the URL to complete the process. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: cloud ('aws' or 'gcp'), timeout (integer, seconds to wait, default 300, max 600).

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• convoopen

  InsideOut (Riley)

  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.). 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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• tourradar_vertex-review-search

  MolTravel

  [tourradar] Search tour reviews using AI-powered semantic search. Requires tourIds to scope results to specific tours. Use this when the user asks about reviews, feedback, or experiences for specific tours. Combine with an optional text query to find reviews mentioning specific topics (e.g., 'food', 'guide', 'accommodation'). When you don't have tour IDs, use vertex-tour-search or vertex-tour-title-search first to find them.

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• data.capabilities

  Recursive Support Oracle

  Returns structured information about what the Recursive platform includes: features, AI model details, supported integrations, and what's included at every tier. Use for systematic feature comparison.

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• register

  AI Agents Overflow

  Register your agent to start contributing. Call this ONCE on first use. After registering, save the returned api_key to ~/.agents-overflow-key then call authenticate(api_key=...) to start your session. agent_name: A creative, fun display name for your agent. BE CREATIVE — combine your platform/model with something fun and unique! Good examples: 'Gemini-Galaxy', 'Claude-Catalyst', 'Cursor-Commander', 'Jetson-Jedi', 'Antigrav-Ace', 'Copilot-Comet', 'Nova-Navigator' BAD (too generic): 'DevBot', 'CodeHelper', 'Assistant', 'Antigravity', 'Claude' DO NOT just use your platform name or a generic word. Be playful! platform: Your platform — one of: antigravity, claude_code, cursor, windsurf, copilot, other

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• credawait

  InsideOut (Riley)

  Wait for the user to securely connect their cloud account and subscribe to Luther Systems. Polls until credentials appear on the session. 🎯 USE THIS TOOL WHEN: tfdeploy returns an 'auth_required', 'no_credentials', or 'credentials_expired' error. The user needs to visit the connect URL to: 1. Connect their cloud credentials (AWS or GCP) 2. Sign up and subscribe to a Luther Systems plan (required for deployment) This secure connection allows InsideOut to deploy and manage infrastructure in the user's cloud account on their behalf. Credentials are handled securely and only used for deployment and management sessions. WORKFLOW: 1. FIRST: Present the connect URL and explanation to the user (from the tfdeploy error response) 2. THEN: Call this tool to begin polling for credentials 3. The user opens the URL in their browser to subscribe and add credentials 4. When credentials are found, inform the user and call tfdeploy to deploy IMPORTANT: Do NOT call this tool without first showing the connect URL to the user. The user needs to see the URL to complete the process. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: cloud ('aws' or 'gcp'), timeout (integer, seconds to wait, default 300, max 600).

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• view-hmr-unit-tests

  hmr

  The unit tests (code examples) for HMR. Always call `learn-hmr-basics` and `view-hmr-core-sources` to learn the core functionality before calling this tool. These files are the unit tests for the HMR library, which demonstrate the best practices and common coding patterns of using the library. You should use this tool when you need to write some code using the HMR library (maybe for reactive programming or implementing some integration). The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.

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• analyze_company

  Nordic Financial MCP

  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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• sti_get_platforms

  docs

  List every Stimulsoft product/platform that has indexed documentation available through this MCP server. Returns a JSON array of { id, name, description } objects covering the full Stimulsoft Reports & Dashboards product line (Reports.NET, Reports.WPF, Reports.AVALONIA, Reports.WEB for ASP.NET, Reports.BLAZOR, Reports.ANGULAR, Reports.REACT, Reports.JS, Reports.PHP, Reports.JAVA, Reports.PYTHON, Server API, etc.). CALL THIS FIRST when the user's question is ambiguous about which Stimulsoft platform they are using, or when you need to pick a valid `platform` value to pass into `sti_search`. The returned platform `id` values are the exact strings accepted by the `platform` parameter of `sti_search`. This tool is cheap (no OpenAI call, no vector search) — call it freely whenever you are unsure about platform naming.

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• checkout_complete

  Chia Health MCP Server

  Complete payment using Stripe ACP (Shared Payment Token). Only use this if your platform supports Stripe Agentic Commerce Protocol and can provision an SPT. If your platform does NOT support ACP, use the `payment_url` from checkout_create instead, then poll checkout_status. Requires authentication.

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• user

  Fast.io

  User profile and account management. View/update profile, search contacts, manage invitations, handle user assets, and manage personal AI instructions (markdown guidance the platform applies to AI features). Destructive action: close permanently deletes the account. Actions & required params: - me: (none) - update: first_name | last_name | email_address (at least one) - search-contacts: query - close: confirmation [DESTRUCTIVE] - details-by-id: user_id - profiles: (none) - allowed: (none) - org-limits: (none) - list-shares: (+ optional: archived, limit, offset) - invitation-list: (+ optional: invitation_key) - invitation-details: invitation_id - accept-all-invitations: (+ optional: invitation_key) - asset-upload: user_id, asset_name, content | content_base64 (+ optional: filename, content_type) - asset-delete: user_id, asset_name [DESTRUCTIVE] - asset-types: (none) - asset-list: user_id - instructions-get: (none) — read your personal AI instructions slot - instructions-set: content — replace your personal AI instructions (full replace, max 65536 bytes UTF-8) - instructions-clear: (none) — soft-clear your personal AI instructions

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• consent_submit

  Chia Health MCP Server

  Record a patient's consent confirmation for a specific consent document. The agent must have already presented the full consent text (from consent_text) to the patient and received explicit confirmation. Required parameters: intake_id, consent_id, the patient's exact confirmation text (e.g. 'I agree'), consent method ('ai_agent_conversational'), the AI platform name ('chatgpt', 'claude', 'gemini'), and a session/conversation ID for audit trail. Returns a consent record with timestamp, audit trail details, and the list of remaining consents still needed. All consent records are retained for 10 years per HIPAA requirements. Requires authentication.

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• get_dashboard

  mcp

  Use this as the primary tool to retrieve a single specific custom monitoring dashboard from a Google Cloud project using the resource name of the requested dashboard. Custom monitoring dashboards let users view and analyze data from different sources in the same context. This is often used as a follow on to list_dashboards to get full details on a specific dashboard.

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• get_dag_source_code

  mcp

  Retrieves the DAG source code from a Cloud Composer environment. Use this tool when you need to understand DAG implementation details, for example to understand why a DAG run has failed, or to debug DAG's behavior. The tool returns the latest version of the source code.

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• list_chats

  Peec AI MCP Server

  List chats (individual AI responses) for a project over a date range. Each chat is produced by running one prompt against one AI engine on a given date. Filters: - brand_id: only chats that mentioned the given brand - prompt_id: only chats produced by the given prompt - model_id: only chats from the given AI engine (chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4, qwen-3-6-plus, amazon-rufus-scraper) — deprecated, prefer model_channel_id - model_channel_id: only chats from the given engine channel (openai-0, openai-1, qwen-0, openai-2, perplexity-0, perplexity-1, google-0, google-1, google-2, google-3, anthropic-0, anthropic-1, deepseek-0, meta-0, xai-0, xai-1, microsoft-0, amazon-0) If both model_id and model_channel_id are provided, model_channel_id takes precedence and model_id is ignored. Use the returned chat IDs with get_chat to retrieve full message content, sources, and brand mentions. Returns columnar JSON: {columns, rows, rowCount, totalCount}. rowCount is the rows in this page; totalCount is the total matching records ignoring limit/offset. Columns: id, prompt_id, model_id, model_channel_id, date.

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• thinkneo_read_memory

  ThinkNEO Control Plane

  Read Claude Code project memory files. Without arguments, returns the MEMORY.md index listing all available memories. With a filename argument, returns the full content of that specific memory file. Use this to access project context, user preferences, feedback, and reference notes persisted across Claude Code sessions.

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