{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-graphlit","slug":"graphlit","name":"Graphlit","type":"mcp","url":"https://github.com/graphlit/graphlit-mcp-server","page_url":"https://unfragile.ai/graphlit","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-graphlit__cap_0","uri":"capability://tool.use.integration.multi.source.content.ingestion.via.mcp.protocol.bridge","name":"multi-source content ingestion via mcp protocol bridge","description":"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.","intents":["Ingest Slack messages and threads into a searchable knowledge base without manual export","Crawl websites and extract content into structured markdown for RAG pipelines","Connect podcast feeds and automatically transcribe episodes for semantic search","Pull emails from Gmail and preserve conversation context in a unified project","Sync GitHub issues, Jira tickets, and Linear items as structured content sources"],"best_for":["Development teams using MCP-compatible IDEs (Cursor, Windsurf, Cline) who need to build RAG-augmented coding assistants","Knowledge workers building internal search systems across fragmented communication platforms","AI engineers prototyping multi-source knowledge bases without custom ETL pipelines"],"limitations":["Requires active Graphlit API account and valid credentials; no local-only mode","Feed connectors are asynchronous — ingestion latency depends on Graphlit backend processing, not MCP server","No built-in deduplication across feeds — duplicate content from overlapping sources requires manual collection management","Supports only sources explicitly integrated into Graphlit platform; custom source adapters not exposed via MCP"],"requires":["Node.js 18+","Graphlit API key and project ID","MCP client compatible with stdio transport (Cursor, Windsurf, Cline, or Claude Desktop)","Network access to Graphlit API endpoints","Source-specific credentials (Slack token, Gmail OAuth, etc.) configured in Graphlit dashboard"],"input_types":["feed configuration (source type, credentials, URL patterns)","content URLs (web pages, podcast feeds)","structured metadata (collection names, workflow IDs)"],"output_types":["feed resource objects with ingestion status","content resource objects with extracted text and metadata","ingestion job status and error logs"],"categories":["tool-use-integration","automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-graphlit__cap_1","uri":"capability://search.retrieval.semantic.search.and.retrieval.over.ingested.content","name":"semantic search and retrieval over ingested content","description":"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.","intents":["Search across all ingested Slack messages, emails, and documents using natural language queries","Retrieve relevant code snippets and documentation from GitHub/Jira when building features","Find related podcast episodes or transcripts based on semantic similarity to a query","Build RAG pipelines that augment LLM responses with retrieved knowledge base content"],"best_for":["AI engineers building RAG-augmented coding assistants that need semantic search over mixed content types","Teams with fragmented knowledge across multiple platforms (Slack, email, docs) seeking unified search","Developers prototyping LLM agents that require context retrieval without manual prompt engineering"],"limitations":["Search quality depends on embedding model used by Graphlit backend; no control over embedding strategy from MCP client","Relevance ranking is opaque — no access to similarity scores or embedding vectors for custom reranking","No full-text search fallback; semantic search only, which may miss exact phrase matches","Retrieval latency includes Graphlit API round-trip; typical 500ms-2s depending on index size and network"],"requires":["Content must be pre-ingested into Graphlit project via feeds or direct upload","Graphlit API key with read permissions on project contents","MCP client with tool-calling support"],"input_types":["query string (natural language or structured)","collection ID (optional, to scope search)","result limit and offset parameters"],"output_types":["ranked list of content objects with relevance scores","extracted text snippets and source metadata","content IDs for follow-up operations"],"categories":["search-retrieval","memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-graphlit__cap_10","uri":"capability://memory.knowledge.content.memory.and.short.term.context.management","name":"content memory and short-term context management","description":"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.","intents":["Store temporary conversation context or user preferences without permanently indexing them","Create short-lived memory contents for specific conversation sessions","Manage ephemeral context that should not appear in general knowledge base searches"],"best_for":["Conversations that need session-specific context without permanent knowledge base pollution","Temporary analysis or brainstorming sessions that generate context not meant for long-term storage"],"limitations":["Memory contents are stored in Graphlit backend with no local persistence; no offline access","No automatic expiration or cleanup; memory contents must be manually deleted","Memory contents are included in RAG retrieval by default; no way to exclude them from searches","No versioning or history for memory contents; updates overwrite previous versions"],"requires":["Graphlit project with memory management enabled","Graphlit API key with content management permissions"],"input_types":["memory content text","metadata (tags, expiration time if supported)"],"output_types":["memory content resource object","memory content ID for reference in conversations"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-graphlit__cap_2","uri":"capability://text.generation.language.rag.augmented.conversation.with.persistent.chat.history","name":"rag-augmented conversation with persistent chat history","description":"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.","intents":["Have multi-turn conversations with an LLM that automatically retrieves relevant context from ingested knowledge","Maintain separate conversation threads for different projects or topics with independent RAG pipelines","Configure LLM behavior (model, temperature, max tokens) per conversation using Graphlit specifications","Export conversation history with retrieved sources for audit trails or documentation"],"best_for":["Developers building AI-augmented coding assistants that need persistent context across sessions","Teams using Cursor/Windsurf who want IDE-native RAG conversations without switching tools","Knowledge workers needing conversational search over company knowledge bases with source attribution"],"limitations":["Conversation state stored in Graphlit backend only; no local persistence or offline mode","RAG retrieval happens server-side with no visibility into retrieval logic or ranking; cannot customize reranking","Context window management is automatic but opaque; no control over which retrieved documents are included in LLM prompt","Conversation history grows unbounded; no automatic pruning or summarization of old messages"],"requires":["Graphlit project with ingested content and configured specifications (LLM presets)","Graphlit API key with conversation read/write permissions","MCP client with streaming support for real-time response handling"],"input_types":["conversation ID (for continuing existing conversation)","user message (text)","specification ID (LLM configuration preset)","collection ID (optional, to scope RAG retrieval)"],"output_types":["assistant response (text, streamed)","conversation object with updated message history","retrieved content references with source metadata"],"categories":["text-generation-language","memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-graphlit__cap_3","uri":"capability://data.processing.analysis.collection.based.content.organization.and.filtering","name":"collection-based content organization and filtering","description":"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.","intents":["Organize ingested content by project, team, or topic (e.g., separate collections for 'frontend', 'backend', 'devops')","Scope RAG retrieval to specific collections to avoid irrelevant context pollution","Manage access control by assigning different collections to different teams or workflows","Create temporary collections for specific analysis or conversation contexts"],"best_for":["Large organizations with multiple teams needing isolated knowledge bases within a single Graphlit project","Projects with heterogeneous content types requiring semantic partitioning (e.g., docs vs. code vs. chat)","Workflows that need to scope RAG retrieval to domain-specific content"],"limitations":["Collections are logical partitions only; no physical data isolation or separate indices per collection","Collection-scoped searches still query the full project index, then filter results — no performance benefit for large projects","No hierarchical collections; all collections are flat within a project","Collection membership is immutable once content is added; moving content between collections requires deletion and re-ingestion"],"requires":["Graphlit project with content already ingested","Graphlit API key with collection management permissions"],"input_types":["collection name and description","content IDs to add/remove","collection ID for queries"],"output_types":["collection resource objects with metadata","collection membership lists","filtered content results"],"categories":["data-processing-analysis","memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-graphlit__cap_4","uri":"capability://data.processing.analysis.workflow.based.content.processing.and.transformation","name":"workflow-based content processing and transformation","description":"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.","intents":["Automatically extract entities (people, companies, dates) from ingested documents and messages","Summarize long documents or email threads into concise overviews","Classify content by topic, sentiment, or custom categories for better organization","Transform extracted content into structured formats (JSON, tables) for downstream processing"],"best_for":["Teams needing automated content enrichment (tagging, summarization, entity extraction) without custom ML pipelines","Knowledge base builders who want to normalize heterogeneous content types before RAG indexing","Developers building AI agents that require structured data extraction from unstructured sources"],"limitations":["Workflows are defined in Graphlit dashboard, not via MCP; MCP tools only trigger and monitor execution","No visibility into workflow logic or intermediate steps; results are opaque","Processing latency depends on Graphlit backend; no control over execution priority or resource allocation","Workflow results are stored in Graphlit backend only; no streaming or real-time output to MCP client"],"requires":["Graphlit project with workflows pre-configured in dashboard","Graphlit API key with workflow execution permissions","Content must be ingested before workflows can process it"],"input_types":["workflow ID","content ID or collection ID to process","workflow parameters (if configurable)"],"output_types":["workflow execution status and progress","extracted/transformed content with metadata","processing errors and logs"],"categories":["data-processing-analysis","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-graphlit__cap_5","uri":"capability://tool.use.integration.project.configuration.and.specification.management","name":"project configuration and specification management","description":"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.","intents":["Create and configure a new Graphlit project for a specific domain or team","Define LLM specifications (model, temperature, system prompt) for different conversation types","Query project metadata and resource counts to understand knowledge base size and composition","Update project settings (default workflow, retention policies) programmatically"],"best_for":["DevOps engineers automating Graphlit project setup for multiple teams or environments","AI engineers configuring LLM behavior (temperature, model selection) per conversation type","Teams managing multiple knowledge bases with different configurations"],"limitations":["Project creation and configuration is limited to MCP tools; some advanced settings only available in dashboard","Specifications are immutable once created; updating requires creating a new specification and migrating conversations","No versioning or rollback for project configurations; changes are immediate and permanent","Project-level access control is managed by Graphlit API key; no fine-grained permissions per project"],"requires":["Graphlit API key with project creation/management permissions","Graphlit account with sufficient quota for new projects"],"input_types":["project name and description","specification parameters (model, temperature, max tokens, system prompt)","default workflow ID"],"output_types":["project resource object with metadata","specification resource objects","project statistics (content count, feed count, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-graphlit__cap_6","uri":"capability://automation.workflow.feed.based.continuous.content.synchronization","name":"feed-based continuous content synchronization","description":"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.","intents":["Set up a Slack feed that continuously ingests new messages and threads into the knowledge base","Monitor podcast feed subscriptions and automatically transcribe new episodes","Create a website crawler feed that periodically re-indexes pages for updated content","Manage feed credentials and sync schedules without accessing the Graphlit dashboard"],"best_for":["Teams with continuous content streams (Slack, email, Discord) who need always-up-to-date knowledge bases","Content aggregators building searchable archives of podcasts, blogs, or news feeds","Organizations needing to keep internal documentation (wikis, GitHub) synchronized with RAG systems"],"limitations":["Feed sync frequency is determined by Graphlit backend; no control over polling interval or real-time webhooks from MCP client","Feed credentials are stored in Graphlit backend; no local credential management or rotation from MCP","Duplicate content handling is automatic but opaque; no visibility into deduplication logic","Feed failures are logged in Graphlit backend; no real-time error notifications to MCP client"],"requires":["Graphlit API key with feed management permissions","Source-specific credentials (Slack token, Gmail OAuth, etc.) configured in Graphlit dashboard or via MCP","Network access from Graphlit backend to source APIs"],"input_types":["feed type (Slack, Discord, Gmail, website, podcast, etc.)","source credentials or URLs","sync schedule and filtering parameters"],"output_types":["feed resource object with sync status","ingestion statistics (content count, last sync time)","sync errors and warnings"],"categories":["automation-workflow","tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-graphlit__cap_7","uri":"capability://data.processing.analysis.automatic.content.extraction.and.format.normalization","name":"automatic content extraction and format normalization","description":"Graphlit MCP Server's ingestion pipeline automatically extracts and normalizes content from diverse formats into standardized representations. Documents (PDF, DOCX, PPTX) are converted to Markdown; web pages are extracted to Markdown; audio and video are transcribed to text; messages preserve original format with metadata. This normalization happens transparently during ingestion, enabling unified search and RAG retrieval across heterogeneous content types without client-side preprocessing.","intents":["Ingest PDF documents and automatically extract text and structure into searchable Markdown","Crawl websites and extract main content while filtering out navigation and ads","Upload video files and automatically generate transcriptions for semantic search","Preserve Slack message threads with user context and timestamps for conversation-aware retrieval"],"best_for":["Teams with mixed content types (docs, web, video, chat) who need unified search without format-specific tooling","Knowledge base builders who want to normalize content before RAG indexing","Developers building multi-modal RAG systems that need text extraction from documents and media"],"limitations":["Extraction quality depends on content type and source; complex layouts (tables, figures) may not extract perfectly","Transcription accuracy depends on audio quality and language; no control over transcription model or parameters","Markdown conversion is lossy for complex documents (formatting, images, embedded objects are not preserved)","No access to intermediate extraction steps; only final normalized content is returned"],"requires":["Content must be in supported format (PDF, DOCX, PPTX, HTML, MP3, MP4, etc.)","Graphlit API key with content ingestion permissions"],"input_types":["document files (PDF, DOCX, PPTX)","web URLs (HTML)","media files (MP3, MP4, WAV)","message objects (Slack, Discord, email)"],"output_types":["normalized text (Markdown for documents/web, plain text for transcriptions)","extracted metadata (title, author, timestamps, speaker names)","content resource objects with source references"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-graphlit__cap_8","uri":"capability://tool.use.integration.mcp.resource.exposure.and.stdio.based.protocol.bridging","name":"mcp resource exposure and stdio-based protocol bridging","description":"Graphlit MCP Server implements the Model Context Protocol by exposing Graphlit resources (projects, contents, feeds, collections, conversations, workflows, specifications) as MCP resources and tools via stdio transport. The server uses the MCP SDK's McpServer class and StdioServerTransport to establish bidirectional communication with MCP clients (Cursor, Windsurf, Cline), translating MCP requests into Graphlit API calls and returning results as MCP responses. This enables IDE-native access to Graphlit capabilities without leaving the editor.","intents":["Access Graphlit knowledge base and RAG capabilities directly from Cursor, Windsurf, or Cline without switching tools","Use Graphlit content retrieval and conversation in Claude Desktop or other MCP-compatible clients","Integrate Graphlit into custom MCP servers or agents that need knowledge base access"],"best_for":["Developers using MCP-compatible IDEs (Cursor, Windsurf, Cline) who want IDE-native RAG and knowledge base access","AI engineers building MCP servers or agents that need to integrate Graphlit knowledge bases","Teams standardizing on MCP as the protocol for AI tool integration"],"limitations":["MCP communication is synchronous over stdio; no support for long-running operations or streaming responses (except for conversation messages)","Resource discovery is limited to MCP's resource listing mechanism; no full-text search over resource names or metadata","Error handling is limited to MCP error codes; detailed Graphlit error messages may be truncated","No built-in rate limiting or request queuing; high-frequency requests may overwhelm the MCP server"],"requires":["MCP client compatible with stdio transport (Cursor, Windsurf, Cline, Claude Desktop, or custom MCP client)","Node.js 18+ runtime for the MCP server","Graphlit API key and project ID configured in environment or config file"],"input_types":["MCP tool calls with parameters","MCP resource requests"],"output_types":["MCP tool results","MCP resource objects","MCP error responses"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-graphlit__cap_9","uri":"capability://tool.use.integration.specification.driven.llm.configuration.and.behavior.control","name":"specification-driven llm configuration and behavior control","description":"Graphlit MCP Server exposes specification management tools that define reusable LLM configuration presets (model, temperature, max tokens, system prompt, top-p, frequency penalty, etc.). Specifications are created once and referenced by conversations and workflows, enabling consistent LLM behavior across multiple interactions without hardcoding parameters. The server provides tools to create, list, and query specifications, allowing dynamic LLM configuration without code changes.","intents":["Define different LLM specifications for different conversation types (e.g., 'creative' vs. 'analytical')","Configure system prompts and model parameters for domain-specific RAG conversations","Reuse LLM configurations across multiple conversations and workflows","Experiment with different model parameters without modifying conversation logic"],"best_for":["AI engineers tuning LLM behavior for specific use cases (coding, writing, analysis)","Teams managing multiple conversation types with different LLM requirements","Developers building configurable AI agents that need to switch LLM behavior dynamically"],"limitations":["Specifications are immutable once created; updating requires creating a new specification and migrating conversations","Limited to LLM parameters exposed by Graphlit; no access to advanced features like function calling or vision models","Specification selection is per-conversation; no global default or automatic selection based on context","No A/B testing or experimentation framework; comparing specifications requires manual conversation creation"],"requires":["Graphlit API key with specification management permissions","Understanding of LLM parameters (temperature, top-p, max tokens, etc.)"],"input_types":["specification name and description","model name (e.g., 'gpt-4', 'claude-3-opus')","temperature, top-p, max tokens, frequency penalty","system prompt"],"output_types":["specification resource object with all parameters","list of specifications in project","specification usage statistics"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","Graphlit API key and project ID","MCP client compatible with stdio transport (Cursor, Windsurf, Cline, or Claude Desktop)","Network access to Graphlit API endpoints","Source-specific credentials (Slack token, Gmail OAuth, etc.) configured in Graphlit dashboard","Content must be pre-ingested into Graphlit project via feeds or direct upload","Graphlit API key with read permissions on project contents","MCP client with tool-calling support","Graphlit project with memory management enabled","Graphlit API key with content management permissions"],"failure_modes":["Requires active Graphlit API account and valid credentials; no local-only mode","Feed connectors are asynchronous — ingestion latency depends on Graphlit backend processing, not MCP server","No built-in deduplication across feeds — duplicate content from overlapping sources requires manual collection management","Supports only sources explicitly integrated into Graphlit platform; custom source adapters not exposed via MCP","Search quality depends on embedding model used by Graphlit backend; no control over embedding strategy from MCP client","Relevance ranking is opaque — no access to similarity scores or embedding vectors for custom reranking","No full-text search fallback; semantic search only, which may miss exact phrase matches","Retrieval latency includes Graphlit API round-trip; typical 500ms-2s depending on index size and network","Memory contents are stored in Graphlit backend with no local persistence; no offline access","No automatic expiration or cleanup; memory contents must be manually deleted","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.041Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=graphlit","compare_url":"https://unfragile.ai/compare?artifact=graphlit"}},"signature":"0XiTJHIQFvbu+jK8HPkzbHdkh38S775KwFLxBEEC6BhQrqFLauzWrVdfvElbommp1RHfqjzRCwb1uu2TDWdCCg==","signedAt":"2026-06-21T20:02:11.825Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/graphlit","artifact":"https://unfragile.ai/graphlit","verify":"https://unfragile.ai/api/v1/verify?slug=graphlit","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}