"Generating Metrics" matching MCP servers:

Enables AI agents to perform professional-grade deep research by aggregating real-time data from multiple sources, evaluating source credibility, and generating comprehensive reports.

MCP server to manage agents, prompts, sessions, and metrics using strands-hub data stored in S3. Provides tools for listing agents, creating prompt versions, and querying run metrics.

An automated research agent that leverages Google Gemini models and Google Search to perform deep, multi-step web research, generating sophisticated queries and producing citation-rich answers.

An MCP server implementation that integrates AI assistants with Langfuse workspaces, allowing models to query LLM metrics by time range.

Enables AI-assisted coding tools to checkpoint sessions, detect context drift, and restore an earlier state while preserving uncommitted work and generating a recovery handoff for an informed restart.

MCP server for agent network monitoring. Aggregates real-time population counts, transaction volume, and health metrics across multi-service agent infrastructure.

A Model Context Protocol (MCP) server that automates generating LinkedIn post drafts from YouTube videos. This server provides high-quality, editable content drafts based on YouTube video transcripts.

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

Turns any OpenAPI/Swagger specification into a functional MCP server, dynamically exposing API endpoints as LLM tools or generating standalone Python code.

MCP server for safely continuing long Claude Code sessions by cleaning transcripts, removing runtime noise, and generating verified resumable sessions with rollback support.

Enables LLMs to interact with Minds through the Minds SDK, supporting resource and tool templates for listing Minds and generating completions.

Enables interactive stock market analysis using local Deepseek-R1 model through Ollama. Supports querying stock data, generating visualizations, and performing financial analysis via natural language commands in Cursor IDE or terminal.

Enables multi-agent design sessions with a structured workflow for defining voice, generating directions, creating UI elements, and exporting artifacts.

A lightweight Model Context Protocol server that enables creating, managing, and querying model contexts with integrated Datadog metrics and monitoring.

An observability tool for agentic AI pipelines that intercepts MCP and Python tool calls to provide real-time metrics, session replays, and alerts via a local dashboard. It enables centralized monitoring of multiple MCP servers through multiplexer and proxy modes without requiring changes to existing agent code.

A unified enterprise-grade gateway that lets LLM agents call multiple MCP servers through a single REST API, with built-in authentication, rate limiting, logging, and metrics.

A lightweight Model Context Protocol (MCP) server that enables your LLM to capture screenshots of any specified URL and return only the access URL for the captured image. This tool simplifies the process of generating and sharing webpage snapshots, making it perfect for integrating visual capture ca

Connects AI assistants to Warpmetrics telemetry data to monitor AI agent performance, execution runs, and LLM costs. It allows users to query success rates, latency, and spend metrics directly through natural language interfaces.

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.

Enables durable, evidence-backed deliberation and Decision CI by running multi-stage model protocols with detached worker processes, persistent jobs, and SQLite-stored decisions, evidence, outcomes, and quality metrics.