{"id":"ywatanabe1989-scitex-python","name":"scitex-python","af_score":57.2,"security_score":51.5,"reliability_score":33.8,"what_it_does":"scitex (SciTeX) is a Python toolkit and orchestration layer for scientific research workflows: experiment tracking/session management, unified file I/O, reproducible figure generation (via figrecipe), statistical testing, literature management (scholar), LaTeX manuscript compilation (writer), and cryptographic verification/provenance tracking (clew). It also exposes an MCP server surface (named scitex) with a large set of MCP tools intended for AI agents to run parts of the research lifecycle via structured tool calls.","best_when":"You want an agent-friendly, modular research automation stack in Python where outputs are tracked and can be verified/provenanced locally, and you can run the MCP server/tooling yourself.","avoid_when":"You need strict, formally specified network API contracts (REST/OpenAPI) and comprehensive documented operational guarantees (SLA, rate limits, retry semantics) for a hosted service.","last_evaluated":"2026-03-30T15:18:49.602435+00:00","has_mcp":true,"has_api":false,"auth_methods":["Environment/config-based credentials for optional integrations (e.g., LLM providers, web automation, cloud integrations) are implied but not specified in the provided content"],"has_free_tier":false,"known_gotchas":["MCP tool surface is very large (293 tools); agents may require careful tool selection/guardrails to avoid unintended long-running or external-network tasks (scholar fetch, browser automation, dataset downloads).","The toolkit mixes local file operations, external fetches, and optional integrations; agent planners should model side effects and artifacts (saved files, generated figures, compiled outputs).","No explicit retry/backoff or idempotency guidance is shown in the provided excerpt, so agents may need conservative re-run strategies."],"error_quality":0.0}