"How to generate HTML code from Figma design files" matching MCP servers:

Echo tool MCP server that extracts and logs W3C Trace Context from requests to verify propagation from Claude Code.

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

MCP server that provides read-only analysis of Claude Code session transcripts, enabling queries about project activity, session details, and tool usage.

A Model Context Protocol server for Zoho Site24x7 that uses a JavaScript sandbox to execute API calls, enabling management of monitors, accounts, and MSP/BU tenancy through natural language.

Maintenance, recovery, and observability for Claude Code. CLI + MCP server + dashboard.

Surface Claude Code token usage, estimated cost, and plan-limit status in any MCP client. Enables agents to query usage data from local logs and Anthropic API.

Claude Code token usage and cost visibility for MCP-capable agents.

Fetches Langfuse observability traces directly into a VS Code coding agent's context, enabling querying and viewing trace data through natural language.

Read-only MCP server that surfaces UniFi network data from a UCG-Fiber console to Claude, enabling monitoring of hosts, sites, devices, clients, ISP metrics, and device stats via read-only API tools.

Enables MCP clients to query and triage security findings published to BigQuery and correlate them with execution, sandbox, and diagnostic traces in Cloud Logging. Exposes tools, resources, and prompts for filtering issues by severity, status, repository, or CWE, retrieving patch diffs and verification results, and diagnosing why a remediation or verification run failed.

MCP server that transforms raw log query results into structured incident reports (Markdown, JSON, HTML) with correlation to git commits and tickets.

Provides structured access to Claude Code agent log files, allowing agents to query assistant lines and tool calls without reading raw JSONL data.

Ask your AI agent about your MCP servers: which tools fail, how a release compares, what clients look for and do not find. Reads from mcpspan with a read token.

An open-source MCP server that collects errors from browser console and terminal, then uses OpenRouter's free AI models to generate summaries for AI agents.

Records runtime events from any app via zero-code instrumentation and exposes them to LLMs through MCP for autonomous debugging.

Enables LLMs to analyze logs by extracting patterns, redacting secrets, and providing token-efficient summaries from files, Docker containers, or journald.

Enables grading a website's security headers with per-gap fixes and a README badge, and analyzing logs, code, configs or suspicious messages to determine what happened, how severe it is, and what to fix first. Requests are masked for sensitive data and automatically retried across multiple AI providers when one refuses, subject to a defensive-use-only policy.

Exposes configured log files as MCP tools, enabling agents to list, query, and follow logs from local and SSH sources.

Enables interaction with Sentry organizations, projects, and issues directly from MCP clients like Claude Code.

Analyzes how search engines and AI systems discover, crawl, cite, and convert traffic via CLI and MCP tools.

Enables comprehensive analysis of Apache Spark event logs from S3, HTTP, or local sources, providing performance metrics, resource monitoring, shuffle analysis, and automated optimization recommendations with interactive HTML reports.

Handles log file management and analysis with tools to list, read, search, filter, and generate analytics on log files.

An MCP server for retrieving and filtering logs from Mezmo with a quota-conscious design and intelligent defaults. It enables users to discover and query logs by application, level, and custom time ranges to streamline debugging while minimizing API usage.

A containerized server that enables AI clients to perform automated network packet analysis, protocol inspection, and traffic forensics using Wireshark/tshark. It features a stateless design that synchronizes PCAP files directly from GitHub for secure and ephemeral analysis sessions.

Wireshark MCP server for Windows that enables AI agents to capture live packets, analyze pcap files, apply display filters, reassemble TCP/UDP streams, generate statistics, and export packets or protocol objects.

MCP server to start, monitor, search, and detect errors in logs from any project, even those without log files.

Enables business services to push error logs and generate diagnostic reports using MCP tools for log searching, code querying, and report generation.

Enables querying logs and metrics with plain SQL from any MCP client via pooml's read-only query API.

An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.

Provides cryptographically signed, tamper-evident compute usage receipts via a Model Context Protocol server, exposing tools to record usage, verify receipts, list the ledger, and verify ledger integrity without requiring any account, API key, or single cloud provider.

Enables classification of GPU workloads as training, inference, or idle from telemetry data, with tools for one-shot classification, benchmarking, and audit log verification.

8 standalone MCP servers for SMBs — Guardrails (prompt injection, PII, secrets detection), FinOps (AI cost tracking and budgets), Observability (agent tracing), Smart Router (17+ models, 9 providers), Trust Score (governance scoring 0-100), Memory (persistent agent memory), ThinkSecure (runtime security + SOC2/GDPR audit), and A2A Lite (agent-to-agent protocol). 37 tools total, TNC credits

Web analytics and AI session replay for agents: visitors, breakdowns, funnels, goals, revenue attribution, and AI-found site issues. Connects to the hosted Flowsery MCP server.

Provides MCP tools to create and query SigNoz trace funnels, enabling agents to measure their own step conversion rates from within the agent loop.

Model Context Protocol (MCP) server for debugging, analyzing, and diagnosing Kubernetes clusters directly from AI agents.

Read-only MCP server for Grafana Loki: lets AI agents (Claude Code, Cursor, Copilot) discover labels, read bounded LogQL pages, count lines by label or time, and export logs — across several Loki stands

MCP server exposing rlg (RustLogs) log files as tools for on-call / SRE agent workflows. Three tools over JSON-RPC stdio — tail_log (last N events, filtered by level/target), filter_log (predicate filtering), summarize_errors (grouped error taxonomy). Pure Rust. MIT OR Apache-2.0.

MCP server for Kibana / Elasticsearch — log search, aggregations, index discovery, and dashboard browsing. Hits Elasticsearch REST API directly for log queries; falls back to Kibana Console proxy when no direct ES URL is configured. Supports ApiKey auth (best for agents), Basic auth, and anonymous access. All 5 tools are read-only (readOnlyHint: true). Returns structured JSON (outputSchema).

Relays error logs from other MCP servers when an LLM call fails and the returned error message is unclear, helping the model handle errors more intelligently.

Provides Claude with read-only access to PC diagnostics including hardware specs, live performance metrics, disk usage, network checks, and more, enabling natural language queries about PC health and performance.