| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 6 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Executes arbitrary console commands on a running Garry's Mod server through the RCON (Remote Console) protocol, sending commands over a TCP socket connection with authentication. The MCP server translates tool calls into RCON packets, handles response parsing, and returns command output back to the LLM client. This enables real-time server administration and configuration without direct server access.
Unique: Wraps Garry's Mod RCON protocol as an MCP tool, enabling LLM agents to directly execute server commands without custom scripting; integrates authentication and response parsing into the MCP abstraction layer
vs alternatives: Simpler than building custom RCON clients for each use case; MCP standardization allows any MCP-compatible LLM client to manage Garry's Mod servers with the same interface
Executes arbitrary Lua code on the Garry's Mod server by sending it through the RCON interface using the 'lua_run' console command. The MCP server packages Lua code snippets into RCON commands, executes them server-side, and returns any printed output or errors. This allows dynamic scripting and runtime modification of server behavior without restarting.
Unique: Bridges Lua code execution to MCP by wrapping lua_run RCON commands, allowing LLM agents to generate and execute Lua code server-side without manual script uploads or server restarts
vs alternatives: More flexible than static RCON commands for complex logic; faster iteration than uploading Lua files and restarting; enables AI-driven code generation for server-side scripting
Captures a screenshot of the Garry's Mod game window and returns it as a base64-encoded image or file. The MCP server uses OS-level window capture APIs (likely Windows GDI or similar) to grab the active game window, encodes it to PNG/JPEG format, and provides it to the LLM client. This enables visual inspection of server state, player activity, or map conditions without direct server access.
Unique: Integrates OS-level window capture into MCP, allowing LLM clients to request game screenshots on-demand without custom image handling code; enables vision-based game state analysis
vs alternatives: More direct than streaming video or polling game state via RCON; enables vision models to analyze game visuals directly without intermediate processing
Sends input events (mouse clicks, keyboard presses, window focus) to the Garry's Mod game window, simulating user interaction. The MCP server translates tool calls into OS-level input events (Windows SendInput API or equivalent) and applies them to the game window. This enables remote control of the game client for automation, testing, or interactive workflows.
Unique: Wraps OS-level input simulation (SendInput, etc.) as MCP tools, enabling LLM agents to control the game window without custom input handling; integrates with screenshot capture for closed-loop automation
vs alternatives: More direct than scripting game mods for client-side automation; enables AI agents to interact with the game UI and client without modifying game code
Transfers files to and from a remote server via SFTP (SSH File Transfer Protocol), supporting both upload (local to remote) and download (remote to local) operations. The MCP server establishes an SFTP connection using SSH credentials, navigates remote directories, and transfers files with support for binary and text modes. This enables management of server configuration files, logs, and Lua scripts without direct SSH access.
Unique: Integrates SFTP file transfer into MCP, allowing LLM agents to upload/download files without custom SSH clients; supports both text and binary files with directory navigation
vs alternatives: More flexible than RCON-only management for file-based tasks; enables AI agents to deploy scripts and manage server files as part of integrated workflows
Implements the Model Context Protocol (MCP) server specification, exposing all Garry's Mod management capabilities (RCON, Lua, screenshots, SFTP) as standardized MCP tools. The server registers tools with JSON schemas, handles MCP client requests, manages authentication state, and routes tool calls to underlying implementations. This enables any MCP-compatible LLM client (Claude, custom agents) to access Garry's Mod functionality through a unified interface.
Unique: Implements full MCP server specification for Garry's Mod, providing standardized tool schemas and protocol handling; enables seamless integration with any MCP-compatible LLM client without custom adapters
vs alternatives: More standardized than custom API wrappers; MCP enables tool reuse across different LLM platforms and clients; reduces friction for integrating Garry's Mod into multi-tool AI workflows
Automatically loads and parses documents from diverse sources (PDFs, Word docs, HTML, Markdown, code files, databases) into a unified in-memory representation using format-specific loaders and node-based document abstractions. Each document is decomposed into Document objects containing metadata, content, and relationships, enabling downstream processing without format-specific handling in application code.
Unique: Provides a unified loader abstraction (BaseReader interface) that normalizes 100+ data source connectors into a single Document/Node API, eliminating format-specific branching logic in application code. Loaders are composable and chainable, allowing sequential transformations (e.g., load → split → extract metadata → embed).
vs alternatives: Broader out-of-the-box loader coverage than LangChain's document loaders and more structured node-based decomposition than raw text splitting, reducing boilerplate for multi-source RAG pipelines.
Splits documents into semantically coherent chunks using multiple strategies (character-based, token-aware, recursive, semantic) with configurable overlap and chunk size. Preserves document hierarchy and metadata through a node tree structure, enabling retrieval systems to maintain context relationships and enable hierarchical re-ranking or parent-document retrieval patterns.
Unique: Implements a node-tree abstraction that preserves document hierarchy and enables parent-document retrieval patterns. Supports multiple splitting strategies (recursive, semantic, code-aware) with pluggable custom splitters, and automatically propagates metadata through the node tree.
vs alternatives: More sophisticated than LangChain's text splitters because it preserves hierarchical relationships and supports semantic splitting; better for complex document structures than simple character-based splitting.
LlamaIndex scores higher at 40/100 vs gmod-mcp at 22/100. gmod-mcp leads on ecosystem, while LlamaIndex is stronger on adoption and quality. However, gmod-mcp offers a free tier which may be better for getting started.
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Processes documents containing mixed content (text, images, tables, code) by extracting and understanding each modality separately, then synthesizing information across modalities. Uses vision models for image understanding, specialized parsers for tables and code, and integrates results into a unified document representation for retrieval and generation.
Unique: Integrates vision models, table parsers, and code extractors into a unified multi-modal document processing pipeline that synthesizes information across modalities. Preserves modality-specific structure (table schemas, code formatting) while enabling cross-modal retrieval and generation.
vs alternatives: More comprehensive multi-modal support than text-only RAG; built-in vision integration reduces boilerplate for document understanding compared to manual vision API calls.
Enables streaming of LLM responses token-by-token and real-time retrieval updates, allowing applications to display partial results as they become available. Supports streaming from retrieval (progressive document discovery) and generation (token-by-token output) with backpressure handling and cancellation support for responsive user experiences.
Unique: Provides first-class streaming support for both retrieval and generation with automatic backpressure handling and cancellation. Enables progressive result display without custom async/streaming code in application layer.
vs alternatives: More integrated streaming support than manual LLM API streaming; built-in retrieval streaming and backpressure handling reduce complexity compared to custom streaming implementations.
Tracks API costs for LLM calls, embeddings, and other operations with per-query and per-session cost attribution. Provides cost optimization recommendations (e.g., batch processing, model selection, caching) and enables cost-aware query planning to balance quality and expense. Integrates with multiple LLM providers to normalize cost tracking across models.
Unique: Provides automatic cost tracking across multiple LLM providers with per-query attribution and cost optimization recommendations. Integrates with query execution to enable cost-aware planning without manual cost calculation.
vs alternatives: More integrated cost tracking than manual API billing review; built-in optimization recommendations reduce guesswork for cost reduction.
Enables building custom RAG pipelines by composing modular components (retrievers, synthesizers, agents, tools) through a declarative or programmatic API. Supports complex workflows with branching, loops, and conditional logic, with automatic dependency resolution and execution optimization. Pipelines are reusable, testable, and can be deployed as APIs or batch jobs.
Unique: Provides a flexible pipeline composition API supporting both declarative and programmatic definitions, with automatic dependency resolution and execution optimization. Enables complex workflows with branching and conditional logic without custom orchestration code.
vs alternatives: More flexible pipeline composition than fixed RAG architectures; better workflow support than manual component chaining.
Generates embeddings for documents/nodes using pluggable embedding providers (OpenAI, Hugging Face, local models) and stores them in a unified vector store interface that abstracts over multiple backends (Pinecone, Weaviate, Milvus, FAISS, Chroma, etc.). The abstraction layer enables switching vector stores without changing application code, and handles batching, retry logic, and metadata indexing.
Unique: Provides a unified VectorStore interface that abstracts 10+ vector database backends, enabling zero-code switching between providers. Handles embedding batching, retry logic, and metadata propagation automatically. Supports both cloud and local embedding models through a pluggable EmbedModel interface.
vs alternatives: Broader vector store coverage and more seamless provider switching than LangChain's vectorstore integrations; better abstraction consistency across backends than using raw vector store SDKs directly.
Retrieves semantically similar documents from vector stores using embedding-based similarity search, with optional re-ranking, filtering, and fusion strategies (hybrid search combining dense and sparse retrieval). Supports multiple retrieval modes (similarity, MMR, fusion) and enables custom retrieval logic through a pluggable Retriever interface that can combine multiple strategies.
Unique: Implements a pluggable Retriever abstraction supporting multiple retrieval strategies (similarity, MMR, fusion, custom) that can be composed and chained. Built-in support for re-ranking via LLM or cross-encoder, and hybrid search combining dense and sparse retrieval without custom integration code.
vs alternatives: More flexible retrieval composition than LangChain's retrievers; built-in re-ranking and fusion strategies reduce boilerplate for advanced retrieval pipelines.
+6 more capabilities