Andrea9293 MCP vs Onto — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
Andrea9293 MCP vs Onto
In-depth architectural comparison of the Andrea9293 MCP and Onto MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Andrea9293 MCP
Developer Tools · Local stdio
Quality: 63/100 (Good) | Auth: No auth required
Onto
Developer Tools · Local stdio
Quality: 51/100 (Good) | Auth: No auth required
Verdict Summary: Choose Andrea9293 MCP if you need specialized Developer Tools tools running via a local process. Choose Onto if your workspace requires Developer Tools integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Andrea9293 MCP when:
You need dedicated capabilities in the Developer Tools domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Andrea9293 MCP is categorized under Developer Tools and uses a local stdio subprocess. In contrast, Onto belongs to Developer Tools using local stdio subprocess. Select Andrea9293 MCP when you need capabilities focused on developer tools and Onto when you require tools for developer tools.
Lists files in the uploads folder with size and format info
get_ui_url
Returns the Web UI URL (e.g. http://localhost:3080) — useful to open the dashboard or to locate the uploads folder from the browser
search_documents
Semantic vector search within a specific document
search_all_documents
Hybrid (full-text + vector) cross-document search
get_context_window
Returns a window of chunks around a given chunk index
search_documents_with_ai
🤖 AI-powered search using Gemini (requires `GEMINI_API_KEY`)
Onto Tools (6)
read_url
Returns clean Markdown for a URL with metadata about the extraction (sizes, reduction %, cache state).
score_url
Returns the AIO (AI-readability) score for a URL — 0-100 with a letter grade, hallucination risk, and a structured list of penalties / benefits / recommendations.
read_and_score
Returns clean Markdown plus the AIO score in one call. Recommended default for agentic workflows.
batch
Process many URLs in **one call**. **N credits** — one per URL in the list after the list is known. Failed URLs (`ok: false`) are refunded. Default mode `read-and-score` is still N, not 2N. Give an explicit list or a base URL whose pages are auto-discovered.
map_site
Discover a site's URLs (sitemap → on-page links) without reading them. **1 credit per call** (not per discovered URL) — use it to plan which pages to read or batch next.
extract_data
Return the structured data a page already declares — JSON-LD, OpenGraph, and meta tags — plus the AIO score. Deterministic; no fields are inferred by a model.