Snapback

Name: Snapback
Author: ra1labsworkx-wq

MCP serverMCP

Diagnose why an AI agent failed and get the verified fix instantly. Free, no token.

by ra1labsworkx-wqrepository ↗v1.8.4

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Evidence

Everything here was measured by our prober or read from a registry. Nothing is self-reported.

30-day availability65/65 checks ok

when	check	result	http	latency	detail
4 h ago	mcp initialize	ok	200	176 ms	server snapback 1.0 · protocol 2025-06-18 · 23 tools
11 h ago	mcp initialize	ok	200	169 ms	server snapback 1.0 · protocol 2025-06-18 · 23 tools
18 h ago	mcp initialize	ok	200	173 ms	server snapback 1.0 · protocol 2025-06-18 · 23 tools
25 h ago	mcp initialize	ok	200	182 ms	server snapback 1.0 · protocol 2025-06-18 · 23 tools
31 h ago	mcp initialize	ok	200	186 ms	server snapback 1.0 · protocol 2025-06-18 · 23 tools
2 d ago	mcp initialize	ok	200	170 ms	server snapback 1.0 · protocol 2025-06-18 · 23 tools
2 d ago	mcp initialize	ok	200	179 ms	server snapback 1.0 · protocol 2025-06-18 · 23 tools
2 d ago	mcp initialize	ok	200	174 ms	server snapback 1.0 · protocol 2025-06-18 · 23 tools
2 d ago	mcp initialize	ok	200	183 ms	server snapback 1.0 · protocol 2025-06-18 · 23 tools
3 d ago	mcp initialize	ok	200	180 ms	server snapback 1.0 · protocol 2025-06-18 · 23 tools

Tools observed via MCP handshake

diagnose_trace — Diagnose why an AI agent run failed. Returns a structured verdict (failure_class, failed_at_step, root_cause, fix_suggestion, confidence). LATENCY: known patter
diagnose_batch — Diagnose SEVERAL traces in one call (up to 20). Each trace is metered like a separate diagnose_trace. Returns a verdicts array (per-trace, order preserved); a b
get_verdict — Fetch a previously produced verdict by its id or by trace_id (your org only).
preflight — BEFORE running: get known failure patterns for a given agent setup so you can avoid them. Returns a ranked list of {failure_class, root_cause, fix_suggestion} f
submit_feedback — Tell Snapback whether a verdict was correct (correct=true/false), with an optional free-text note (did the fix work? what was wrong?). ONE rating per verdict —
get_request_status — Check what happened to a pattern you requested (from request_pattern's request_id). Returns pending / approved / rejected / in_library so you can see if your su
my_usage — See YOUR current usage + remaining allowance so you can self-govern spend: snapbacks (diagnoses) used/cap/remaining, guard checks used/cap/remaining, estimated
my_impact — See how your feedback + pattern requests have shaped the shared library — how many verdicts you've rated, patterns you've requested, and how many were approved
what_others_did — THE CROWD: for a failure_class (or pass the family/error and we'll map it), see what OTHER agents did about the same failure and whether it worked — anonymized,
report_outcome — Report the outcome of an auto-applied fix (the self-heal interceptor calls this after it gate-applied a fix and retried). Pass failure_class, family, fix, confi
recommend_failover — Should you RETRY the same target, SWITCH provider, FALL BACK to another chain, or STOP? Pass the error + your configured topology (current_chain, available_chai
cascade_root — Given an ORDERED list of errors from a run (oldest first), find the TRUE root — the error that cascaded or shouldn't have been retried — not just the final symp
suggest_budget_recovery — Approaching a token/context/cost budget mid-run? Pass your counters and get the LEAST-DISRUPTIVE recovery ranked: truncate context | switch to a cheaper model |
request_pattern — Leave us a message: ask us to add a failure pattern to the library, or report a problem we couldn't diagnose well. Use this when diagnose_trace didn't have a go
detect_loop — MID-RUN loop check (fast, no LLM, free). Send your recent steps DURING a run; get back whether you're stuck repeating a tool call and a concrete next move. Call
session_start — Open a LIVE mid-run session so Snapback can watch your run step-by-step and warn you in real time (loop / token / cost / context) - the always-on guardian mode.
session_step — Report ONE step of a live run and get back any warnings immediately (loop detected / budget breach). Pass the step (action + inputs) and any counters you have (
session_end — Close a live session and get a short run summary (total steps, duration). Frees the session. Free, no token.
agent_memory — See what YOU (this agent) tend to fail on — your recurring failure patterns across past diagnoses. Returns your top failure classes with counts and what share o
budget_guard — Live MID-RUN budget check (fast, no LLM). Send whatever counters you have and get an advisory on context %, token burn, cost burn, step budget, and off-task dri
convert_trace — Turn your raw logs into a Snapback trace so you don't hand-craft JSON. Pass 'source' = a list of log/step entries, or an object with a spans/steps/messages/even
search_docs — Search Snapback's documentation for how to use it — how to format a trace, what each tool does, the failure taxonomy, auth, pricing, and errors. Free and needs
diagnose_infra_error — Diagnose a cryptic AGENT-INFRASTRUCTURE error — payments (x402/EIP-3009), Solana on-chain (ATA, blockhash, compute), MCP protocol, RPC/providers, EVM & Solana w

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Official MCP Registryio.github.ra1labsworkx-wq/snapback

first seen 18 d ago · fetched 2 d agoopen ↗

Field history (8 recent events)

8 d ago · declared · version → "1.8.4"
16 d ago · declared · version → "1.8.2"
18 d ago · declared · publisherName → "ra1labsworkx-wq"
18 d ago · declared · repoUrl → "https://github.com/ra1labsworkx-wq/snapback-selfheal"
18 d ago · declared · summary → "Diagnose why an AI agent failed and get the verified fix instantly. Free, no token."
18 d ago · declared · protocols → ["mcp"]
18 d ago · declared · version → "1.8.0"
18 d ago · declared · description → "Diagnose why an AI agent failed and get the verified fix instantly. Free, no token."

Snapback

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