magna-nz/tallybook

Two deterministic, rule-based analysis engines for agents: cloud billing cost anomaly detection (rolling-median + MAD, resistant to the masking effect) and auditable architecture risk scoring (single point of failure, coupling, scalability gap) with a rule-by-rule breakdown for every score. `uvx cloudsealed-mcp`

Run efficiency for Claude Code sessions: the share of paid tokens that became model output versus re-reading context already sent, by tokens and cost-weighted. Reads local transcripts, makes no network calls.

Agent tool spend tracker. Local JSONL storage, consent-gated, nothing leaves the file. Free MCP server. `npx scvd-tab`

Cross-agent failure intelligence. Before an agent retries a failed tool call, `check_tool_failure` reports whether other agents are hitting the same failure right now and which recovery actually worked, with a Wilson-score confidence. Privacy-by-schema: no prompts, keys, or payloads stored. Hosted remote server at `https://failecho.com/mcp`, or self-host.

Governed managed-endpoint fleet operations (thin-client/VDI) — login-storm and patch/config-drift analysis, inventory, and guardrailed remediation (13 tools) with unbypassable audit logging (MCP + CLI), budget/runaway guards, and undo/rollback.

Iris scores every agent run for quality, safety, and cost — on your machine, with no SDK and no account. Most agent projects check quality by running a few remembered prompts and eyeballing the output. Iris replaces that with numbers you can audit: your agent's runs land in a SQLite database on your disk, 13 built-in rules score them deterministically — PII, prompt injection, hallucination markers, cost thresholds — free, with no LLM calls, and an optional LLM judge with a hard per-eval cost cap handles the semantic questions. Every rule is inspectable and editable, because a judge you can't audit is just vibes with a number on it. MIT licensed, no telemetry; your traces never leave your machine.

6 tools · deploy_rule, evaluate_output

Your AI agent doesn't know what's broken in production. This fixes that. Last9 MCP Server connects Claude, Cursor, Windsurf, and any other MCP-capable AI assistant directly to your production observability data — logs, metrics, traces, exceptions, database queries, alerts, and deployments. The agent stops guessing and starts reading the actual signal. No binary to install. No tokens to manage. One URL, OAuth in your browser, done. Find your org slug in your Last9 URL: app.last9.io/<org_slug>/... Type /mcp, select last9, authenticate. That's it.

6 tools · get_alerts, get_database_slow_queries

A remote Model Context Protocol (MCP) server that connects AI assistants to your crash reporting and real user monitoring data in Raygun through natural language conversations. 📚 Tool Reference • 🚀 Quick Start • 🔑 Get API Token - 🐛 Error Management - Investigate, resolve, and track application errors and crashes with full stack traces and context - 🚀 Deployment Tracking - Monitor releases and correlate errors with deployments to identify problematic changes - ⚡ Performance Insights - Analyze page load times, user metrics, and performance trends over time - 👥 User Monitoring - Track customer sessions, behavior patterns, and identify affected users - 🤝 Team Collaboration - Manage invitations and coordinate error resolution across your team - 📊 Metrics & Analytics - Time-series analysis and distribution histograms for errors and performance

6 tools · applications_list, customers_search