"Information about SEO (Search Engine Optimization)" matching MCP servers:

Enables analyzing SEO page performance using data from Google Search Console, Google Analytics, and PostHog to identify weak CTR, decaying rankings, and other issues, providing actionable recommendations.

Provides secure, agentic Excel analysis and calculation capabilities, enabling users to inspect workbook structure, perform statistical computations, write formulas, and generate outputs through whitelisted MCP tools with audit trails and backups.

The complete open-source SEO data stack for AI agents, integrating Google Search Console, Google Analytics 4, Bing Webmaster Tools, and Microsoft Clarity with built-in analyst tools like cannibalization check, low-hanging fruit, and content gap analysis, all self-hosted and free.

Enables local offline machine learning workflows, including CSV profiling and cleaning, classifier and neural network training, hyperparameter optimization, evaluation metrics, confusion matrix plots, Streamlit dashboards, and HTML reports through MCP tools.

Enables analysis of raw SEO data by ingesting nested JSON datasets and converting them into clean, actionable metrics through the Model Context Protocol.

Syncs Google Search Console performance data into a local ClickHouse database and exposes prepared SEO analysis tools to AI agents. Enables diagnosing traffic drops, finding striking-distance keywords, detecting cannibalization, auditing indexing, and generating weekly SEO reports through natural language.

Superhuman data-driven science. Allows agents to upload any tabular dataset, specify a target column, and get validated predictive patterns (with p-values, effect sizes, and context from literature) that surface feature interactions and subgroup effects you'd otherwise miss. Many discoveries already made, free for open data!

Enables AI assistants to read and modify Power BI Desktop files programmatically, including querying models, creating measures, and managing relationships through natural language.

An MCP server that gives models semantic context about a data warehouse, including table meaning, units, lineage, and health, to reduce guessing.

A lightweight, powerful, and flexible workflow engine that executes tasks defined in YAML configuration files.

A high-performance Model Context Protocol server for AI integration with Metabase analytics platforms. Features response optimization, robust error handling, and comprehensive data access tools.

An MCP server that enables AI assistants to interact with Tenzir, a data pipeline engine for security operations, including executing pipeline queries, managing OCSF schemas, and generating parsers.

Enables optimal freight-mode selection (air, sea-LCL, full container) by solving 3D bin-packing, cost optimization, and delivery-risk analysis behind two tools: solve for deterministic planning and explain for hallucination-proof natural-language narration of results.

Autonomous cross-source inconsistency and contradiction intelligence engine for AI agents. Detects specification drift, SemVer conflicts, and infrastructure mismatches across code, docs, and deployments.

Enables AI agents to work with a real Excel-compatible spreadsheet engine, allowing them to create and manipulate structured data blocks, write and evaluate formulas, and save genuine .xlsx files with live formulas for human verification and reuse.

MCP server for Durable energy trading analytics that connects Claude Desktop to Snowflake data, enabling natural-language queries about spreads, constraints, generators, and prices.

Bridges Claude (or any MCP client) to manufacturing data via the i3X standard, enabling natural language queries about equipment status, hierarchy, and historical trends.

Enables AI agents to protect, unprotect, and search sensitive data using Kustodyan's contextual transform engine with role-based access controls.

A tutorial implementation MCP server that enables analysis of CSV and Parquet files. Allows users to summarize data and query file information through natural language interactions.

Enables LLM clients to access scheduling data, KPIs, routes, and trigger optimization algorithms via MCP.