"PostgreSQL MCP (Multi-Version Concurrency Control or specific usage)" matching MCP servers:

Gives coding agents the exact installed version of each dependency in a project from node_modules and lockfile, preventing deprecated API usage by grounding them in reality.

Audits local Claude Code transcripts to show which connected MCP servers are actually used, idle, failing, or awaiting authorization, and recommends which to keep or disconnect.

Lets Claude look up your own Claude subscription usage limits — the same 5-hour and weekly percentages shown by /usage — mid-conversation, so it can pace long tasks and warn you before you hit a wall. It reads the local OAuth login, serves a throttled read-only snapshot (cached with graceful fallback), and exposes it as a single get_usage tool.

Provides Claude plan usage data (session and weekly percentages) as an MCP tool by reading through a logged-in browser, enabling Claude to check its own remaining usage.

Enables Claude Code sessions to check account rate-limit headroom, local token spend, and live session status, with read-only MCP tools for limits, summaries, token usage, and session listing.

Enables retrieving real Claude subscription usage statistics, including the 5-hour session window, weekly limit, and per-model limits, from the same endpoint Claude Code uses.

An MCP server that exposes Claude Code's real-time rate limit usage so that an orchestrator can check remaining capacity before launching subtasks.

Monitors your Cursor usage and provides a conserve flag to the agent, helping you manage request quota by batching questions, preferring defaults, and cutting unnecessary confirmations.

Reports your Claude subscription usage (5-hour and weekly limits) with a forecast and velocity recommendation, using Claude Code's existing OAuth session without requiring an API key.

A local MCP server that exposes Blacksmith.sh's usage and cost data, enabling queries for CI spend through agents, alerts, or dashboards.

Enables local-first document versioning with content-addressable storage, word-level AST diffs, and AI-compliance auditing for LLM agents.

Enables local-first tracking of AI token usage and costs from Claude Code and OpenCode, answering queries about tokens, models, sessions, and cost through MCP tools and a CLI, with no network calls.

MCP server that reads local OpenClaw session files to provide token usage and cost data without any network calls or authentication.

Gives the model visibility into the same 5-hour session, weekly, and per-model usage percentages shown in Claude Code, along with reset times. It also lets a project cap how much of the weekly limit may be spent via start/check/end budget tools that report OK, LOW, or OVER_BUDGET.

Read-only MCP server for querying AI usage metrics (exact tokens, cost, latency, errors, productivity) from a local SQLite database, enabling charts and analysis in Claude Code and Cursor.

Deterministic, LLM-first control of Word documents (.docx). An agent reads the outline, finds text by phrase, and applies edits as tracked changes and comments in one atomic batch, with a safety layer that refuses edits based on a stale read and rolls back failed batches. It works on .docx files directly, and can also drive an open document in Microsoft Word on Windows or in LibreOffice Writer.

Read-only MCP server for querying PostgreSQL, MySQL, and SQLite from AI agents — multi-database, safe by default.

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.

Fetches and tracks Claude.ai usage data including session and weekly limits with automated daily logging and persistent session management. It allows users to monitor their usage history directly through Claude Code integrated tools.

An open-source MCP server that gives parents real-time oversight and guardrails over their children's AI agent interactions, with features like content filtering, spiral detection, and approval flows.