multi-source content ingestion via mcp protocol bridge
Graphlit MCP Server acts as a stdio-based protocol bridge that translates MCP client requests into Graphlit Knowledge API calls, enabling ingestion of content from Slack, Discord, Gmail, websites, podcasts, and document storage platforms. The server registers content ingestion tools that map to Graphlit's feed system, which creates persistent data connectors for each source. Content is automatically extracted to normalized formats (Markdown for documents/web, transcription for audio/video, preserved format for messages) and stored in a project container with configurable workflows.
Unique: Implements MCP as a first-class integration pattern rather than a wrapper, exposing Graphlit's feed system (persistent data connectors with automatic content extraction) directly through MCP tools, enabling IDE-native content ingestion without leaving the editor. Uses StdioServerTransport for direct process communication, avoiding HTTP overhead and enabling tight coupling with MCP clients.
vs alternatives: Unlike REST-only knowledge APIs, Graphlit's MCP server integrates content ingestion directly into developer workflows (Cursor, Windsurf) with persistent feeds that continuously sync sources, whereas alternatives require manual API calls or separate ETL tools.
semantic search and retrieval over ingested content
Graphlit MCP Server exposes content retrieval tools that query the Graphlit Knowledge API's vector search engine, which embeds all ingested content and enables semantic similarity matching across documents, messages, web pages, and media transcriptions. Searches return ranked results with relevance scores, source metadata, and extracted text snippets. The retrieval pipeline integrates with Graphlit's RAG system, allowing LLM clients to augment prompts with contextually relevant content from the knowledge base.
Unique: Integrates semantic search as a first-class MCP tool rather than requiring separate API calls, enabling IDE-native retrieval workflows. Searches across heterogeneous content types (documents, messages, transcriptions, code) with unified ranking, whereas most RAG systems require separate indices per content type.
vs alternatives: Provides semantic search over multi-source knowledge bases (Slack + email + docs + code) in a single query, whereas alternatives like Pinecone or Weaviate require custom ETL to normalize content types before indexing.
content memory and short-term context management
Graphlit MCP Server supports short-term memory contents that store temporary user inputs and conversation context within a project. These memory contents are distinct from persistent ingested content and are designed for ephemeral context that should not be permanently indexed. The server provides tools to create and manage memory contents, enabling conversations to maintain context without polluting the permanent knowledge base.
Unique: Distinguishes short-term memory contents from persistent ingested content, enabling conversations to maintain session-specific context without polluting the permanent knowledge base. Memory contents are stored in the same project but marked as temporary.
vs alternatives: Provides explicit short-term memory management separate from persistent content, whereas alternatives like LangChain require manual context management or separate memory stores.
rag-augmented conversation with persistent chat history
Graphlit MCP Server exposes conversation management tools that create and maintain chat sessions with integrated RAG pipelines. Each conversation maintains message history and automatically retrieves relevant content from the knowledge base to augment LLM responses. The server handles conversation state management (storing messages, managing context windows) and coordinates with Graphlit's specification system (LLM configuration presets) to control model behavior, temperature, and token limits per conversation.
Unique: Implements RAG conversations as stateful MCP resources with integrated retrieval pipelines, rather than stateless tool calls. Conversation state (message history, retrieved documents, context window) is managed server-side by Graphlit, enabling multi-turn interactions without client-side context management. Specifications system allows per-conversation LLM configuration without hardcoding model parameters.
vs alternatives: Unlike LangChain or LlamaIndex which require client-side conversation state management and custom retrieval logic, Graphlit's MCP conversations are fully managed server-side with built-in RAG, reducing client complexity and enabling seamless IDE integration.
collection-based content organization and filtering
Graphlit MCP Server exposes collection management tools that enable organizing ingested content into named groups with independent metadata and access controls. Collections act as logical partitions within a project, allowing users to scope searches, conversations, and workflows to specific subsets of content. The server provides tools to create collections, add/remove content, and query collection membership, enabling fine-grained content organization without duplicating data.
Unique: Implements collections as first-class MCP resources with independent metadata and query scoping, enabling IDE-native content organization. Unlike folder-based systems, collections are semantic groupings that don't require physical data movement, allowing flexible reorganization without ETL.
vs alternatives: Provides logical content partitioning without duplicating data or creating separate indices, whereas document management systems (Notion, Confluence) require manual folder hierarchies and don't support semantic scoping of search results.
workflow-based content processing and transformation
Graphlit MCP Server exposes workflow management tools that define and execute processing pipelines for ingested content. Workflows are configured in the Graphlit dashboard and referenced via MCP tools; they can include extraction (entity recognition, summarization), transformation (format conversion, normalization), and enrichment (metadata tagging, classification) steps. The server allows querying workflow definitions and monitoring execution status, enabling content processing without custom code.
Unique: Exposes Graphlit's workflow system as MCP tools, enabling IDE-native content processing without leaving the editor. Workflows are pre-configured in Graphlit dashboard (not code-based), allowing non-technical users to define processing pipelines while developers trigger them via MCP.
vs alternatives: Provides declarative content processing pipelines (extraction, summarization, classification) without requiring custom code or ML infrastructure, whereas alternatives like Unstructured.io or LlamaIndex require client-side orchestration and model selection.
project configuration and specification management
Graphlit MCP Server exposes project and specification management tools that configure the knowledge base container and LLM behavior. Projects are the top-level resource that contains all ingested content, feeds, collections, and conversations; specifications are LLM configuration presets (model, temperature, max tokens, system prompt) that control behavior across conversations and workflows. The server provides tools to query and update project settings and create/list specifications, enabling configuration without dashboard access.
Unique: Exposes Graphlit's project and specification system as MCP tools, enabling programmatic configuration of knowledge bases and LLM behavior without dashboard access. Specifications decouple LLM configuration from conversation logic, allowing multiple conversation types to use different models/parameters from a single project.
vs alternatives: Provides declarative LLM configuration management (specifications) that can be reused across conversations, whereas alternatives like LangChain require hardcoding model parameters in code or managing them separately.
feed-based continuous content synchronization
Graphlit MCP Server exposes feed management tools that create and monitor persistent data connectors to external sources (Slack, Discord, Gmail, websites, podcasts). Feeds are configured once and continuously sync new content from their sources into the Graphlit project without manual intervention. The server provides tools to create feeds, monitor sync status, and manage feed credentials, enabling hands-off content ingestion for sources that produce continuous streams of data.
Unique: Implements feeds as persistent, server-managed data connectors that continuously sync sources without client intervention, rather than one-time bulk imports. Feeds abstract away source-specific APIs (Slack, Gmail, podcasts) behind a unified interface, enabling multi-source knowledge bases without custom ETL.
vs alternatives: Provides continuous content synchronization from multiple sources (Slack, email, podcasts, websites) with unified ingestion, whereas alternatives like Zapier require separate automations per source and don't integrate with RAG systems.
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