"A tool similar to Cursor with agent-based functionalities" matching MCP servers:

A FastMCP server for Cursor platform APIs enabling spend guardrails, cloud agent monitoring, and inter-agent inbox messaging.

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.

Query Grafana logs, metrics, and dashboards from Cursor. Enables the AI to call your Grafana instance via tools without leaving the editor.

Enables real-time monitoring of Cursor Pro usage limits and API quotas across different AI services. Tracks Sonnet 4.5, Gemini, and GPT-5 request usage with alerts when approaching subscription limits.

Um servidor MCP completo para integração com Sentry no Cursor, oferecendo 27 ferramentas para monitoramento de erros, performance e saúde de aplicações.

A Model Context Protocol server that provides AI assistants access to AWS CloudWatch Logs, enabling browsing, searching, summarizing, and correlating logs across multiple AWS services.

A secure, production-grade MCP server that provides filesystem operations, AST math evaluation, and system diagnostics for LLM agents.

A production-oriented MCP server exposing tools for weather, currency conversion, text statistics, and server introspection, with auto-discovery, rate limiting, and API key auth.

Enables live tracing and observability of MCP tool calls, persisting execution traces to MongoDB and streaming them in real time to a dashboard.

Registers a set of inert, tempting decoy tools with any MCP-capable client so that any call from a hijacked or prompt-injected agent instantly raises an alert with full attack context and a per-call trace token. Also plants worthless canary tokens in honeypot files that trigger zero-false-positive leak alerts when they surface in agent output, files, or outbound requests.

Enables recording and querying MCP tool call traces, including latency, failures, and token costs, per agent run.

Enables checking IP reputations and threat intelligence by querying Chaitin's global honeypot network and millions of defense nodes. Provides real-time IP profiling, malicious behavior detection, and threat alerts based on aggregated security data.

MCP server exposing AI agent observability tools: list, inspect, and deterministically replay recorded agent execution traces for debugging failures.

Exposes rate-limit and usage data for AI coding agent CLIs as MCP tools, enabling querying of usage snapshots and reset times.

Find the step that caused an AI agent’s failure. Ask your assistant why a run failed, see the evidence, and check whether a suggested fix works.

MCP-based production monitoring agent for manufacturing OEE, providing real-time monitoring, downtime root-cause analysis, and automated alerts through integration with SQL Server, MES APIs, and incident management systems.

Enables AI agents to self-report every tool call, token usage, decision, and session grade into a local SQLite store, so users can trace execution, audit reasoning, and see per-session costs in a local dashboard. Also supports proxy mode to intercept and trace MCP tool invocations for agents that cannot self-report.

An MCP server that provides append-only, tamper-evident local receipts for AI agent actions, capturing command executions, outputs, and handoff evidence.

Local-first observability for AI agent stacks. Provides MCP tools for agents to check health, shadow configs, and burnout metrics proactively.

Provides MCP tools to perform Looker health checks (health_pulse, health_analyze, health_vacuum) via the MCP Toolbox connector.

Enables AI apps to inspect and manage a NextDNS account in plain language — profiles, analytics, query logs, blocklists, security and parental control settings — with every change saved and reversible via undo. It runs self-hosted on your own computer or server, keeps per-profile read/write/hidden permissions, and requires double confirmation for deletions or overwrites.

Exposes network-monitoring tools (query metrics, analyze windows, compare, logs, status, runbooks, speed tests) as an MCP server for agentic workflows. Designed with evaluation suites, cost-aware model routing, and semantic tool retrieval.

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.

MCP server for AI agent observability, providing trace and span logging, search, latency/tokens/cost metrics, and anomaly detection using an in-memory buffer.

Enables AI agents to manage prompts and sessions via the ai-agent-logs API, with tools for health checks, login, listing/creating prompts, and retrieving sessions.

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

Enables MCP clients to query and diagnose machine-learning training runs through read-only tools for listing experiments, reading configurations and final metrics, comparing runs, sampling metric curves, and detecting training failures.

Unified MCP safety server that detects prompt injection (75 patterns), scans LLM outputs for leaked secrets/PII, enforces API cost budgets, and creates signed audit trails. Zero ML dependencies, pure Python.

Enables agents to report and query performance metrics to build a community-driven quality database for MCP tools. This server helps agents discover and select the most reliable tools based on success rates and user-reported quality scores.

MCP server that computes trust scores, permission decisions, and silent-failure risk for AI agents with tools for reliability scoring, silent failure detection, permission evaluation, and audit report generation.

Enables local-first observability for MCP agent workflows by recording tool calls, prompts, latency, and token usage in a local SQLite database, and provides tools for inspecting, comparing, exporting, and generating dashboards from traces.

Read-only MCP server for one Docker host: list containers, inspect (without env vars), logs with time windows and search, stats and events. Cannot start, stop, remove or exec.

An MCP server that exposes Estuary Flow pipeline operations as typed tools, enabling agents to inspect pipeline health, publish captures and materializations, edit/restart tasks, and read landed documents via tool calls.

MCP server that provides structured audit logging for AI agent repair tasks via tools to start, record, end, query, and export event traces, with JSONL persistence and SDK integration.

Analyzes AI agent traces to diagnose failures, score performance, and provide actionable fixes using tools like judge_trace and trace_breakdown.

An intelligent triage layer for Cloud Security Posture Management (CSPM). It ingests raw vulnerability and misconfiguration findings from tools like Trivy or Wiz, deduplicates overlapping issues, and uses a rules-based scoring engine (with an optional LLM integration) to output a prioritized, highly actionable ticket list.

A secure, read-only MCP server that enables AI assistants to inspect transactions, vendor performance, wallet balances, and analytics through validated REST API calls.

Enables AI agents to submit GPU job requests, select eligible workers, track execution, and retrieve results via MCP.

MCP server for monitoring, diagnosing, and auto-correcting observability issues across platform services, integrating with Sentry and PostgreSQL for error tracking and billing pipeline health.