"Deutsche Post" matching MCP servers:

Provides read-only visibility into Snowflake warehouse cost and query performance, including credit usage by warehouse, expensive queries, and heuristic right-sizing recommendations.

Provides cost analysis of AI agent usage by previewing scrubbed metadata before upload and analyzing spend, cache-hit rates, and waste recovery through a local or remote server.

Tracks AI agent token usage and spending in real time, with budget alerts, per-task cost breakdown, and a visual dashboard.

Enables Microsoft Copilot Desktop and Microsoft 365 Copilot Studio to post/read Slack messages, query GitHub repository status, and monitor live pod health and fleet runtime briefs.

Token-cost telemetry for OpenClaw, queryable from Claude or any MCP-aware agent. Provides per-agent and per-provider cost attribution, anomaly detection, model-routing recommendations, and 30-day forecasts.

Governed Docker + Portainer container-host operations (non-Kubernetes) — restart-loop, resource-pressure, and image/volume-bloat RCA, with guarded lifecycle writes, unbypassable audit logging (MCP + CLI), budget/runaway guards, dry-run, and undo/rollback.

Enables AI agents to directly inspect and debug embedded hardware: reading/writing serial ports and bus devices, auto-detecting baud rates, parsing Modbus/NMEA/custom binary frames, plus diagnosing failures through a 45-rule fault-pattern library, error-code tables, and compile/flash error translation. Also provides pin-conflict checking, register and Cortex-M fault decoding, pre-validated driver and component selection libraries, board knowledge, and guarded flashing with post-flash byte-level verification.

An MCP server for unified cost tracking and analysis across AWS, OpenAI, and Anthropic. It enables users to query expenditures, compare costs across providers, and analyze usage trends through natural language.

A portable, read-only Model Context Protocol server for turning observability data into bounded evidence that AI agents can inspect safely.

MCP server for Uptimepage uptime monitoring. An LLM client can read your monitors and incidents, run a check on demand, and post incident updates. Writes need an OAuth login and a scoped token, and each one is logged.

Enables agents to check a site's post-quantum readiness and retrieve headline figures, the probe map, service status, and pricing through MCP tools and resources.

Membrane is an MCP server that combines DORA metrics, deploy checks, and gamification rewards into a post-deploy verification layer for AI agents.

Provides a pre-flight/post-flight firewall for LLM calls with comprehensive detection, classification, policy enforcement, reversible redaction, output safety, and immutable audit logging.

Enables users to analyze AWS costs, track spending trends, and detect anomalies directly within Claude Desktop using the AWS Cost Explorer API. It provides tools to identify major cost drivers and compare usage across different time periods through natural language queries.

rungraph is an MCP server (stdio, npx rungraph mcp) that turns Claude Code and Codex transcripts into an interactive run graph. Seven tools: list_runs, get_graph, find_nodes, get_detail, focus_nodes, get_current_view, open_visualization. Reads transcripts post-hoc, no hooks, local-only, zero runtime dependencies. Published on npm as rungraph.

Enables post-hoc auditing of an agent's retrieval supply chain by recording digest-only provenance from source through chunk, transformation, context and decision, then reporting influence rankings with an honest unexplained residual, lineage traces, advisory source trust and retirement recommendations, and retrieval schema drift. It never performs retrieval, ranks documents at query time, or blocks decisions.

Local-first cost tracking and model routing for MCP agents with offline token counting, budget alerts, and spend reports.

Read-only AWS cost diagnostic server that enables AI agents to detect idle resources and orphaned assets, surfacing potential savings via boto3 describe calls.

Enables AI assistants to analyze Azure Data Factory costs, detect waste, and provide optimization recommendations by querying pipeline run metadata and Azure pricing.

Enables AI agents to independently audit a trading bot's exchange account read-only, cross-checking raw exchange positions and orders against bot beliefs to surface missing or undersized stop losses, ghost trades, duplicate or orphaned orders, and size mismatches, with a hash-sealed attestation record for disputes and post-mortems. It never places, edits, or cancels orders, and reports a critical failure rather than a false clean result when the exchange cannot be read.