Model Context Protocol (MCP) Server Directory

IT teams spend 2.8 hours per incident on manual ServiceNow ticket navigation. This FastMCP server exposes incident CRUD, change request workflows, and CMDB queries to AI agents, enabling autonomous incident triage and resolution directly from Claude Desktop or Cursor IDE.

Three agent communication protocols are battling for dominance in 2026: Google's A2A for agent-to-agent, Anthropic's MCP for tool access, and the Linux Foundation's Agent Plugins for portable skills. Here's how they compare, where they overlap, and the convergence pattern that's winning.

ML teams waste 40% of engineering time rebuilding feature pipelines that already exist. This FastMCP TypeScript server wraps Feast and Redis to expose real-time and batch features to AI agents via MCP, enabling Claude Desktop and Cursor to query feature vectors, detect drift, and trigger retraining — all with sub-5ms P99 latency.

Data analysts spend 60% of their time rediscovering which dbt models exist and how they connect. This FastMCP TypeScript server exposes the dbt Semantic Layer to AI agents, enabling Claude Desktop and Cursor to query metrics, trace lineage, validate models, and trigger incremental runs — reducing data transformation cycle time from days to minutes.

Engineers waste 30% of their time searching for the right dataset, checking its freshness, and understanding its schema. This FastMCP Python server aggregates metadata from Snowflake, BigQuery, dbt, and Git into a unified catalog, enabling AI agents to discover, understand, and validate data assets through a single MCP interface — cutting data discovery from hours to seconds.

Mental health crises require immediate, accurate detection. This MCP server gives AI agents the ability to analyze text patterns for crisis indicators and connect users with appropriate resources.

Climate risk assessment requires processing satellite imagery, weather data, and historical patterns. This MCP server gives AI agents real-time access to climate risk data for property, supply chain, and investment analysis.

Personalized medicine requires analyzing complex genomic data alongside patient history. This workflow uses AI agents to process genomic sequences, predict drug responses, and optimize treatment plans in real-time.

Contract negotiation is slow, expensive, and prone to human bias. This workflow uses multi-agent consensus with blockchain anchoring to automate contract review, redlining, and finalization across multiple parties.

ESG compliance monitoring is manual, slow, and error-prone. This workflow automates real-time ESG data collection, risk scoring, and regulatory reporting using LangGraph orchestration with live market data feeds.

Claims processing eats 20 minutes per case. This workflow uses LLM-powered document extraction, fraud pattern detection, and multi-agent triage to cut processing time to 3 minutes while flagging 94% of fraudulent claims.

Contract review costs law firms $150-400 per hour. This workflow extracts obligations, flags risky clauses, and scores contract risk in 45 seconds — making contract review 50x faster while catching obligations human reviewers miss.