Cursor 에이전트가 ai-agent-logs-api와 통신할 수 있게 해주는 MCP 서버입니다(ai-agent-logs-webui에 표시되는 것과 동일한 데이터).
API를 로컬에서 실행합니다(기본값 http://127.0.0.1:8120).
Node.js 18 이상.
Related MCP server: Log MCP Server
cd C:\Users\armin\GitHub\ai-agent-logs-mcp
copy .env.example .env
npm install
npm run build~/.cursor/mcp.json)"ai-agent-logs-mcp": {
"command": "node",
"args": ["C:/Users/armin/GitHub/ai-agent-logs-mcp/dist/index.js"],
"env": {
"AI_AGENT_LOGS_API_URL": "http://127.0.0.1:8120",
"AI_AGENT_LOGS_USERNAME": "armin",
"AI_AGENT_LOGS_PASSWORD": "dopadopa123"
}
}mcp.json을 변경한 후 MCP를 재시작하거나 Cursor를 다시 로드하세요.
도구 | API |
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인증이 필요한 도구는 JWT가 캐시되지 않은 경우 환경 변수의 자격 증명으로 자동 로그인합니다.
변수 | 기본값 |
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Create a prompt log row (POST /api/v1/prompts). Auto-logs in if needed.
Inputs: mode (required), rate (required), title (required), device (required), +4 more.
Get one session and its prompt turns (GET /api/v1/sessions/:id). Auto-logs in if needed.
Inputs: id (required).
Check ai-agent-logs-api health (GET /health).
Inputs: no named parameters.
List stored prompts newest first (GET /api/v1/prompts). Auto-logs in if needed.
Inputs: no named parameters.
List agent conversation sessions newest first (GET /api/v1/sessions). Auto-logs in if needed.
Inputs: no named parameters.
Login to ai-agent-logs-api and cache the JWT. Optional username/password override env defaults.
Inputs: password, username.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Scored across 6 tools
Each tool targets a distinct resource and action: health_check for API status, login for authentication, list/create for prompts, and list/get for sessions. No overlap or ambiguity among them.
All tool names follow a predictable lowercase_with_underscores pattern, mostly verb_noun (list_prompts, create_prompt, get_session). The singular 'login' and compound 'health_check' fit naturally without breaking consistency.
With only 6 tools, the set is tightly scoped for the purpose of a logs API. Each tool serves an essential function, and the count is well within the ideal range.
The surface covers core prompt and session operations (list/create for prompts, list/get for sessions) plus health and login. Missing a get_prompt or delete operations, but for a logging server these are often unnecessary, making the gaps minor.
Self-hosted AI prompt library: prompts, collections, tags, teams, chains. 29 MCP tools for agents.
Access Pollinations models and API capabilities through agent tools.
Context and control for any AI. Agent identity, scoped access, and proof across tools.
Generate contextual prompts and reusable agent skills, evaluate prompts with the 16-dimension Prompt Score, and manage saved work in PromptDrive. Twelve MCP tools also provide authorized access to private Memory for source-grounded answers. Connect over Streamable HTTP using OAuth 2.1 and PKCE. Generation consumes account quota and automatically saves successful results; Memory access follows account permissions and plan limits.
