About Cloud Ai Wallet
Cloud Ai Wallet provides MCP servers for AI agents to access the CloudAIWallet platform’s wallet graph, transaction data, files, web fetching, and developer tools. Its server suite also adds optional request telemetry fields for reasoning, model identity, referral source, and feedback.
Key features
- 01Wallet graph and transaction lookups
- 02SQL queries and table inspection
- 03File search, reading, and restoration
- 04URL fetching and page rendering
- 05Developer command and configuration tools
- 06Optional request telemetry fields
Use cases
- 01Look up wallet accounts and balances
- 02Retrieve platform configuration and files
- 03Run operational and developer tasks
MCP support
Exposes wallet, database, storage, fetch, and developer-tool capabilities through MCP servers. MCP clients can connect to SSE endpoints or add the server from Smithery.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://agent.cloudaiwallet.com/sseclaude mcp add --transport sse cloudaiwallet-mcp-servers https://agent.cloudaiwallet.com/sse{"mcpServers":{"cloudaiwallet-mcp-servers":{"type":"sse","url":"https://agent.cloudaiwallet.com/sse"}}}Open in Cursor{"servers":{"cloudaiwallet-mcp-servers":{"type":"sse","url":"https://agent.cloudaiwallet.com/sse"}}}Open in VS Code{"mcpServers":{"cloudaiwallet-mcp-servers":{"type":"sse","url":"https://agent.cloudaiwallet.com/sse"}}}{"mcpServers":{"cloudaiwallet-mcp-servers":{"serverUrl":"https://agent.cloudaiwallet.com/sse"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMEcloudaiwallet-mcp-servers
MCP-native infrastructure for the CloudAIWallet crypto-wallet platform. It exposes the platform's wallet graph, transaction database, file storage, web fetch, and developer tooling as Model Context Protocol (MCP) servers, so AI agents can manage wallet infrastructure, look up accounts and balances, retrieve configuration, and run operational tasks.
Components
nova-chat/— public chat assistant ("Vela") + skill marketplace + API surfaces (/docs,/openapi.json,/mcp, A2A/MCP discovery cards)mcp_servers/— the MCP tool servers (graph / database / storage / fetch / devtools) plus request telemetryagent-bridge/— Smithery-facing MCP bridge (bridge.js)devtools/— developer-tools MCP server (lint / format / dependency checks)ckan/— open-data portal integrationshell-proxy/— filesystem MCP server frontskill-registry/— tracks installed skill packagesmcp-inventory/— inventory of connected MCP serversmonitoring/— service-token access monitoring + alertstools/— CloudWatch shipping + engagement analytics
Servers
| Server | Port | Tools |
|---|---|---|
| graph-api | 8080 | read_neo4j_cypher, write_neo4j_cypher, schema |
| database-api | 8081 | execute_sql, table introspection |
| storage-api | 8082 | read_file, list_directory, search_files, restore/import/render |
| fetch-api | — | fetch_url, extract_text, check_status, render_page |
| devtools | — | run_command, eval_expression, merge_config, read_template |
| agent-bridge | — | wallet/account/file/transaction lookup, report_finding |
All tool servers inject optional telemetry fields (reasoning, model_info,
referral_source, feedback) into their tool schemas for request analytics —
see docs/ARCHITECTURE.md.
Quick start
docker compose up -d # Neo4j + the MCP servers
python scripts/seed_neo4j.py # seed the wallet graph
python scripts/seed_storage.py # seed platform files
Point any MCP client at the SSE endpoints (e.g.
https://agent.aicryptovault.net/sse) or add the server from Smithery.
Configuration
Runtime configuration comes from config/env/.env and docker-compose.yml
(Neo4j auth, service tokens, provider keys). The values checked into this repo
are non-production sample values — provide your own for any real deployment and
keep real secrets out of source control.
License
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.