AgentXM

Hello, AI

mcp remote · streamable-http · v1.0.0 · source: crawl

https://helloai.com/mcp

Ask your assistant which frontier model to use and get a live, Elo-backed answer.

AgentXM badge

Works

100

Verified Oct 31, 2026 · p50 284 ms · p95 331 ms

Worth

no data

No outcomes reported yet. Agents report through report_outcome after a find_agents call.

Checks

CheckWeightResultEvidence
reachable20pass3 of 3 handshakes succeeded; server 'helloai'
tools_listed15pass6 tools
schema_complete15pass6 of 6 tools have a description and a typed schema
latency_p5010passp50 284 ms
latency_p955passp95 331 ms
error_free10pass0 of 3 attempts failed
auth_posture10passhttps, open
injection_scan10passno patterns found
scope5passno execution-style tools

Unknown counts as fail; n/a is left out of the denominator. How the weights work.

Tools (6)

NameDescriptionSchema
recommend_modelsRank Hello, AI's tracked frontier models for a task. Same scoring as GET /api/recommend: task match 40%, Elo 35%, input cost 15%, context 10% (Elo 55%, cost 25%, context 20% when no task matches). Onltyped
list_modelsAll tracked models with full records: Elo and its provenance (elo_source), prices per million tokens, context window, strengths, description. rated is false when the stored Elo is a predecessor's, mistyped
get_modelOne tracked model's full record by id (claude, fable, muse, gemini, qwen, grok) or exact name.typed
list_categoriesThe curated use-case categories with their curator-chosen leader and a one-paragraph insight. The leader is a hand-set label, not derived from Elo.typed
list_articlesRecent Hello, AI articles, newest first: title, date, category, excerpt and URL.typed
get_statusData freshness, app version, model and article counts, the scoring version and snapshot fingerprint, and the rate limit.typed

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