qualified_work — SpendQuery MCP Prompt
qualified_work
is one of 4 prompts on the
SpendQuery
MCP server. Connect the server and it appears in your client, ready to invoke.
by client
How to invoke qualified_work from your client
- Claude Code SpendQuery Prompt/qualified_work run in your project directory
- Claude Desktop SpendQuery Prompt/qualified_work ~/Library/Application Support/Claude/claude_desktop_config.json
- Cursor SpendQuery Prompt/qualified_work ~/.cursor/mcp.json
- VS Code SpendQuery Prompt/qualified_work .vscode/mcp.json
- Zed SpendQuery Prompt/qualified_work ~/.config/zed/settings.json
- Windsurf SpendQuery Prompt/qualified_work ~/.codeium/windsurf/mcp_config.json
- Cline SpendQuery Prompt/qualified_work ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
- Gemini CLI SpendQuery Prompt/qualified_work ~/.gemini/settings.json
- Grok SpendQuery Prompt/qualified_work .mcp.json (in your project root)
- ChatGPT SpendQuery Prompt/qualified_work Settings → Connectors → Advanced → Developer mode
- Claude.ai SpendQuery Prompt/qualified_work Settings → Connectors → Add custom connector
- LangChain SpendQuery Prompt/qualified_work pip install langchain-mcp-adapters
Other prompts on this server
SpendQuery also offers 1 resource — data you attach as context, where a prompt is something you run.
Read from the server itself, by connecting to it and calling prompts/list on 5 November 2026.
A listing does not declare its prompts, so asking is the only way to know them, and this
is what the server actually offers rather than what its listing claims. Names only are
stored; connect the server for each prompt's arguments.