"Managing game servers using Pelican panel" matching MCP servers:

Predicts video game scores (0-100 scale, similar to Metacritic ratings) using machine learning based on game characteristics like price, release month, tags, genres, and platforms, trained on Steam game data.

Enables LLM clients to access crop yield predictions from RGB imagery, topic-based PDF literature, and soil lab/moisture CSV data.

Enables human-coordinated multi-agent workflows for Earth-observation research, Cube Chess 512 self-play benchmarking, and reversible visual QA through WebMCP tools with verification and approval gates.

Provides tools to interact with the OpenCTI platform, including checking if a campaign exists.

Provides AI agents with direct access to KingsPlaybook's sports data including confirmed lineups, player projections, game lines, and pick history via native MCP tools.

Enables querying YouTube gaming opportunity rankings to answer 'what should I record?' by providing tools for top games, game breakdowns with AI video ideas, and live topic checks.

Enables MCP clients to call OpenRouter Fusion, which queries a panel of models in parallel and synthesizes a single answer using a judge model, useful for research and critical decisions.

An MCP server that lets Claude and other clients query DataDive niches, keywords, competitors, and Rank Radar data using your API key, including tools for managing dives and rank radars.

An MCP server that enables saving, retrieving, and managing research content using ChromaDB vector storage and semantic search.

Enables AI clients to produce probabilistic forecasts with prediction intervals for numeric time series such as sales, demand, traffic, and sensor readings, using a panel of zero-shot foundation models or an accuracy-weighted ensemble. Supports up to 64 series per call, optional covariates, horizons up to 512 steps, and usage or credit checks, all without training or feature engineering.

Exposes 26 structured analytical tools over Climate Finance Update datasets, enabling fund-level financial analysis, portfolio aggregations, and data quality diagnostics without requiring the client to write data access code.

Enables AI clients to read and write ESPN fantasy sports leagues using your own session cookies, including managing rosters, lineups, waiver claims, and free agent adds without a browser.

Enables AI agents to audit Steam store pages for algorithmic health, forecast sales cones with P10/P50/P90 scenarios, and retrieve genre benchmarks for indie game market analysis.

Automatically organizes and merges learned CS knowledge into a graph structure using Claude for entity/relation extraction and SQLite for storage, with tools for ingestion, querying, merging, and managing nodes and edges.

Cross-game market intelligence and currency conversion across six MMOs, enabling unified searches, portfolio tracking, and farming optimization via MCP.

A Python MCP server providing 11 tools for querying KenPom basketball analytics, including efficiency ratings, game predictions, four factors, and more through Claude.

Enables querying live cross-platform gaming market data — Steam players, Twitch/YouTube viewership, prices, discounts, hype, and the aggro metric — through 8 read-only tools for summaries, rankings, genre rollups, game details, and search.

Enables AI assistants to answer tennis tactical questions using shot-by-shot charted match data, producing game plans, serve and return tendencies, scouting reports, match forecasts, and explanations of why a match turned. Exposes read-only tools for player reports, head-to-head game plans, match breakdowns, and tactical leaderboards, each with confidence margins and links to full pages.

Remote MCP server for the Torn City API v2, enabling querying of player, faction, market, and other game data, plus aggregated intelligence reports, all via natural language.

Econometrics MCP server for regression, causal inference, time series, panel data, machine learning, and broader statistical analysis workflows.

Enables AI agents to control Rigol MSO5000 oscilloscopes through VISA, including acquisition, channels, trigger, timebase, waveform generator, display, and front-panel controls, with risk-based permission gating for direct SCPI operations.

Enables AI assistants to research the Asphalt 8 game engine through class-catalog lookup, Ghidra-based decompilation, notes, card writing, and quality checks, saving work to local drafts for later export and review.

Provides AI assistants with access to authentic World of Tanks Blitz game data including tank statistics, armor values, map terrain features, equipment, crew skills, and camouflages parsed directly from local game files.

Provides econometric modeling capabilities through R, enabling AI assistants to perform sophisticated analyses including linear regression, panel data models, instrumental variables regression, and diagnostic tests.

Enables Windows users to control WaveMetrics Igor Pro through COM, exposing MCP tools for executing commands, inspecting waves/variables/procedures, managing dialogs and compilation errors, querying local documentation, and using persistent local session memory.

Enables users to check whether a chosen university subscribes to a given journal or paper — via search, DOI, journal title, database, and journal-package queries — returning library holdings plus clickable links the user opens themselves. It supports multiple schools through a picker panel and only ever inspects holdings, never downloading full text.

Public read-only MCP server exposing today's free sports-betting projections, track record, methodology, engine versions, and per-game model reads from the Olympus Bets Analytics platform.

A comprehensive League of Legends data ecosystem that provides access to champion statistics, item data, and patch history via the Model Context Protocol. It enables analysis of game mechanics, build optimization, and meta tracking through real-time data integration.

Enables searching, reading, annotating, and managing a Zotero library from any MCP client.

Enables managing research-in-flight as a lab notebook with proposals, experiments, and gap signals, using filesystem storage and append-only semantics.