{"id":"vyogotech-frappe-mcp-server","name":"frappe-mcp-server","af_score":55.0,"security_score":40.8,"reliability_score":18.8,"what_it_does":"Provides an MCP server (and local HTTP endpoint) that lets AI assistants query and manipulate ERPNext/Frappe data using generic “doctype” tools, plus wrapper project analytics tools. It can connect to ERPNext via Frappe/ERPNext APIs and to an OpenAI-compatible LLM provider (including local Ollama).","best_when":"You have an ERPNext/Frappe deployment and want to integrate AI agents via MCP with tools that map to Frappe doctypes, optionally using a local model (Ollama) for privacy.","avoid_when":"You need a fully standardized, audited auth model (e.g., OAuth scopes per action) for third-party access, or you cannot validate the server’s security posture (because docs reviewed here don’t show detailed security guarantees).","last_evaluated":"2026-04-04T21:22:09.550120+00:00","has_mcp":true,"has_api":true,"auth_methods":["Configuration-based API key for LLM provider (base_url/api_key/model)","Frappe/ERPNext credentials via config.yaml (not specified in provided README)"],"has_free_tier":false,"known_gotchas":["CRUD tools for “ANY doctype” increase the risk of accidental writes/overwrites if the agent is not constrained; ensure the agent is granted least-privilege permissions in ERPNext.","If LLM outputs unstructured or ambiguous intents, the server may attempt broader queries (e.g., search/analyze) that can be slow or return large result sets; constrain queries via doctype and filters.","Because pagination/limits aren’t evidenced in the README, agents may request large datasets without safeguards—implement client-side limits if needed."],"error_quality":0.0}