"Java command reference and usage" matching MCP servers:

Enables access to Usage and Billing APIs for managing accounts, products, meters, plans, and usage reporting. Supports operations like creating products/plans, reporting usage, and retrieving billing information.

A local-first, multi-provider cost meter for LLM usage, exposed as MCP tools. Captures every call into a local SQLite ledger and lets any coding agent query spend, compare providers, and get recommendations — no cloud, no account. First-class support for Chinese providers (Qwen, DeepSeek) alongside Anthropic and OpenAI.

AI coding usage analytics — track spend, model usage, and costs across Cursor and Claude Code sessions.

MCP server that wraps Cursor Enterprise API to enable AI agents to answer questions about team spending, usage, and model adoption.

Provides access to the Reuters Business and Financial News API to retrieve articles, trending news, and market data. It enables searching and filtering financial content by date, author, category, and keywords.

Enables Claude to read your Patch API budget usage, including spend, burn rate, and account details, directly from chat.

An MCP Server that provides a conversational interface to the UK Open Banking account information API, allowing agents to interact with bank account data through natural language commands.

A local MCP server that integrates FastMCP with OpenAI to enable natural language queries for current weather, forecasts, and currency conversion using ExchangeRate-API and Open-Meteo.

Deterministic US equity-compensation tax optimizer. Six tools cover ISO/AMT exercise scheduling, NSO sell-vs-hold, RSU vest-and-sell, single-stock concentration, protective put / zero-cost collar pricing, and Section 1202 QSBS qualification. Federal plus 50-state plus DC tax math.

Fail-closed evidence manifests for financial AI research: one manifest records evidence artifacts, cutoff timestamps, checks, AI usage and human approval — one verdict before anything ships. MCP tools: gov_check, gov_report, gov_init.

Official reference MCP server for A202, the Verifiable Agreement Protocol for Agent-Led Commerce. Gives an agent the seven capabilities two organisations need to buy and sell directly: issue a mandate, check what an agent may do under it, approve an act needing a person, form an agreement, exchange obligations, verify a record by replay, and read the transaction record back.

Institutional-grade quantitative stock analysis and research signals for AI agents via the Model Context Protocol (MCP).

Enables currency conversion and exchange rate lookups using real ECB reference rates, free and without an API key. Supports natural language queries like 'convert 100 USD to EUR' or 'what's the exchange rate between GBP and JPY?'.

Enables AI agents to verify Robinhood Stock Token prices against real market prices before trading, providing a fair-price verdict based on onchain and reference data.

Enables querying OpenCode Go subscription quota (rolling, weekly, and monthly usage) and Zen prepaid balance as MCP tools.

Enables MCP-capable assistants to query and manage Lexware Office contacts, sales documents, vouchers, files, payments, webhooks, and reference data via the Lexware Office public API. Adds bank reconciliation tools for matching bank statement CSVs against Lexware vouchers or scanned receipt PDFs.

Enables issuing invoices, proformas, and delivery notices, collecting payments, submitting e-Factura to Romania's SPV, and querying reference data on Oblio.eu through natural language from MCP clients.

Enables MCP clients to query AWS Billing and Cost Management data via local AWS CLI, including cost usage, forecasts, anomalies, budgets, and commitment utilization for analysis.

Enables AI agents to query live sports betting data including odds, edges, arbitrage opportunities, player/team stats, and reference data using the SportWizzard API.

A portfolio analysis MCP server that enables AI agents to manage investment portfolios, fetch financial data from Yahoo Finance and CoinGecko, and perform advanced analysis like weight optimization and Monte Carlo simulations. It utilizes reference-based caching to efficiently handle large datasets without bloating the LLM's context window.