{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-vega-lite","slug":"vega-lite","name":"Vega-Lite","type":"mcp","url":"https://github.com/isaacwasserman/mcp-vegalite-server","page_url":"https://unfragile.ai/vega-lite","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-vega-lite__cap_0","uri":"capability://tool.use.integration.mcp.based.tabular.data.persistence.with.in.memory.storage","name":"mcp-based tabular data persistence with in-memory storage","description":"Implements the save_data MCP tool that accepts tabular data (CSV, JSON, or structured records) and persists it in a module-level dictionary keyed by user-provided names. The server maintains session-scoped data in memory without external database dependencies, enabling LLMs to store intermediate datasets during multi-step visualization workflows. Data is retrieved by name in subsequent tool calls, creating a stateful context bridge between conversational turns.","intents":["Store intermediate analysis results from data processing steps so they can be visualized later","Build multi-turn workflows where an LLM transforms data in one step and visualizes in another","Accumulate datasets from multiple sources into named tables for comparative visualization"],"best_for":["LLM agents building exploratory data analysis workflows","Claude Desktop users prototyping data-driven narratives","Teams integrating visualization into conversational AI interfaces"],"limitations":["Data is session-scoped and lost on server restart — no persistence layer","No built-in data validation or schema enforcement — accepts any JSON-serializable structure","In-memory storage scales linearly with dataset size; no pagination or streaming for large tables","No access control or multi-user isolation — all data visible to all MCP clients in same session"],"requires":["Python 3.10+","MCP client (Claude Desktop or compatible)","mcp Python framework 1.0.0+"],"input_types":["JSON array of objects","CSV-formatted strings","Structured records with named columns"],"output_types":["confirmation message with dataset name and row count"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vega-lite__cap_1","uri":"capability://tool.use.integration.vega.lite.specification.generation.from.named.datasets","name":"vega-lite specification generation from named datasets","description":"Implements the visualize_data MCP tool that accepts a Vega-Lite JSON specification template and a reference to a previously saved dataset by name, then merges the data into the spec's data.values field and returns the complete visualization specification. The tool performs JSON schema composition, allowing LLMs to define chart structure (axes, encodings, marks) separately from data, enabling reusable visualization templates and data-driven chart generation without requiring LLMs to construct full Vega-Lite specs from scratch.","intents":["Generate chart specifications dynamically by combining a template structure with runtime data","Create reusable visualization templates that can be applied to different datasets","Allow LLMs to specify chart intent (bar chart, scatter plot, etc.) without manual JSON construction"],"best_for":["LLM agents that need to generate charts without deep Vega-Lite knowledge","Rapid prototyping of data-driven dashboards in conversational interfaces","Teams building visualization-as-a-service features on top of LLMs"],"limitations":["Requires dataset to be pre-saved via save_data tool — no inline data support","No validation of Vega-Lite spec structure — invalid specs pass through to renderer","Template composition is shallow (data.values merge only) — doesn't support nested data transforms","No built-in support for Vega-Lite transforms or aggregations — data must be pre-aggregated"],"requires":["Python 3.10+","Dataset previously saved via save_data tool","Valid Vega-Lite specification JSON (v5.x compatible)","mcp Python framework 1.0.0+"],"input_types":["Vega-Lite specification JSON object","Dataset name string (reference to saved data)"],"output_types":["Complete Vega-Lite specification JSON","PNG image (if --output_type png)","Text representation (if --output_type text)"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vega-lite__cap_2","uri":"capability://image.visual.dual.mode.visualization.output.rendering.png.and.text","name":"dual-mode visualization output rendering (png and text)","description":"Supports two output modes controlled by the --output_type command-line argument: PNG rendering (via Vega-Lite's built-in renderer) for visual output suitable for display in UI clients, and text mode for terminal/log-based environments. The server initializes with the chosen output type at startup and applies it uniformly to all visualize_data calls, enabling deployment flexibility across headless servers, desktop clients, and web interfaces without code changes.","intents":["Render visualizations as PNG images for display in Claude Desktop or web UIs","Output text-based chart representations for terminal-based workflows or logging","Deploy the same server code to different environments (desktop vs headless) with configuration-only changes"],"best_for":["Claude Desktop integration requiring image output","Headless/terminal environments where PNG rendering is unavailable","Multi-environment deployments (dev, staging, production) with different output requirements"],"limitations":["Output type is global and immutable per server instance — cannot mix PNG and text in single session","Text mode output quality depends on terminal width and character set — not suitable for complex charts","PNG rendering requires system dependencies (likely headless browser or image library) not explicitly documented","No streaming or progressive rendering — entire visualization must be generated before output"],"requires":["Python 3.10+","--output_type argument set to 'png' or 'text' at server startup","For PNG mode: system dependencies for Vega-Lite rendering (browser/image library)"],"input_types":["command-line argument string ('png' or 'text')"],"output_types":["PNG binary image data (if output_type=png)","ASCII/text representation (if output_type=text)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vega-lite__cap_3","uri":"capability://tool.use.integration.mcp.protocol.stdio.communication.with.json.rpc.message.handling","name":"mcp protocol stdio communication with json-rpc message handling","description":"Implements the MCP server specification using the mcp Python framework (v1.0.0+), communicating with MCP clients via stdio streams using JSON-RPC 2.0 message format. The server.py module registers handlers for list_tools and call_tool via @server decorators, which are invoked by the MCP client to discover available tools and execute them. This architecture enables seamless integration with Claude Desktop and other MCP-compatible clients without requiring HTTP servers or custom protocol implementation.","intents":["Enable Claude Desktop to discover and invoke visualization tools natively","Integrate with any MCP-compatible client without custom API implementation","Maintain stateful tool context across multiple LLM turns in a conversation"],"best_for":["Claude Desktop users wanting native visualization integration","Teams building MCP-compatible tool ecosystems","Developers integrating with LLM platforms that support MCP"],"limitations":["Stdio-based communication is synchronous and single-threaded — no concurrent tool invocations","No built-in authentication or authorization — relies on client-side access control","Protocol overhead of JSON-RPC adds latency per tool call (typically <100ms)","Unimplemented handlers (list_resources, read_resource, list_prompts, get_prompt) return errors, limiting extensibility"],"requires":["Python 3.10+","mcp Python framework 1.0.0+","MCP-compatible client (Claude Desktop, or custom implementation)","Stdio communication channel (typically subprocess or pipe)"],"input_types":["JSON-RPC 2.0 request messages"],"output_types":["JSON-RPC 2.0 response messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vega-lite__cap_4","uri":"capability://tool.use.integration.tool.capability.advertisement.and.schema.definition","name":"tool capability advertisement and schema definition","description":"The list_tools handler advertises available tools (save_data and visualize_data) to MCP clients with full schema definitions including parameter names, types, descriptions, and required fields. This allows clients to present tool options to users and validate inputs before invocation. The schema definitions are embedded in the tool metadata returned by list_tools, enabling LLMs to understand tool capabilities and construct appropriate invocations without external documentation.","intents":["Allow Claude Desktop to display available visualization tools in the UI","Enable LLMs to understand tool parameters and construct valid invocations","Provide schema-based validation hints to clients before tool execution"],"best_for":["Claude Desktop users who need tool discovery in the UI","MCP client developers building tool selection interfaces","Teams documenting tool capabilities programmatically"],"limitations":["Schema definitions are static and defined at server startup — no dynamic schema generation","No input validation on server side — relies on client to enforce schema constraints","Schema format is MCP-specific — not compatible with OpenAPI or JSON Schema standards directly","No versioning mechanism for tool schemas — breaking changes require server restart"],"requires":["Python 3.10+","mcp Python framework 1.0.0+","MCP client that supports tool schema inspection"],"input_types":["none (server-initiated advertisement)"],"output_types":["Tool metadata array with schema definitions (JSON)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vega-lite__cap_5","uri":"capability://automation.workflow.async.server.initialization.and.stdio.stream.binding","name":"async server initialization and stdio stream binding","description":"The main(output_type) async function in server.py initializes the MCP server and binds it to stdio streams for communication with the MCP client. It uses asyncio.run() to execute the async initialization, setting up the server's event loop and stream handlers. The entry point in __init__.py parses the --output_type command-line argument and invokes main(), creating a complete initialization pipeline from CLI invocation to active MCP server ready to receive tool calls.","intents":["Start the MCP server as a subprocess from Claude Desktop or other MCP clients","Configure output mode (PNG vs text) at server startup time","Establish stdio communication channel between client and server"],"best_for":["Claude Desktop users launching the server via configuration","DevOps teams deploying the server in containerized environments","Developers integrating the server into custom MCP client implementations"],"limitations":["Async initialization adds complexity — requires Python 3.10+ asyncio support","No graceful shutdown mechanism documented — relies on process termination","Output type is immutable after initialization — cannot change modes without restarting","No health check or readiness probe — client must assume server is ready after subprocess launch"],"requires":["Python 3.10+","mcp Python framework 1.0.0+","Subprocess or pipe for stdio communication","--output_type argument ('png' or 'text')"],"input_types":["command-line arguments"],"output_types":["active MCP server listening on stdio"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vega-lite__cap_6","uri":"capability://image.visual.vega.lite.specification.composition.and.data.binding","name":"vega-lite specification composition and data binding","description":"The visualize_data tool accepts a Vega-Lite specification template (JSON object with chart structure, encodings, marks, etc.) and merges a previously saved dataset into the spec's data.values field. This composition approach allows the LLM to define chart structure separately from data, then bind them at visualization time. The tool performs shallow JSON merging, inserting the data array into the spec without modifying other fields, enabling template reuse across different datasets.","intents":["Create chart templates that can be reused with different datasets","Generate visualizations by combining a fixed chart structure with dynamic data","Allow LLMs to reason about chart design independently from data preparation"],"best_for":["Teams building reusable visualization templates","LLM agents that generate charts from structured data","Rapid prototyping of data-driven narratives"],"limitations":["Composition is shallow — only merges data.values, doesn't support nested transforms","No validation of Vega-Lite spec structure — invalid specs pass through","No support for Vega-Lite transforms or aggregations — data must be pre-aggregated","Template variables or parameterization not supported — specs must be complete except for data"],"requires":["Valid Vega-Lite v5.x specification JSON","Dataset previously saved via save_data tool","data.values field in spec (or will be created if missing)"],"input_types":["Vega-Lite specification JSON object","Dataset name string"],"output_types":["Complete Vega-Lite specification JSON with data bound"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-vega-lite__cap_7","uri":"capability://memory.knowledge.dataset.name.based.reference.and.retrieval","name":"dataset name-based reference and retrieval","description":"Implements a naming system where datasets saved via save_data are stored in a module-level dictionary keyed by user-provided names, and visualize_data retrieves them by name. This design allows LLMs to refer to datasets symbolically (e.g., 'sales_data', 'monthly_metrics') rather than passing large data objects between tool calls, reducing message size and improving readability of tool invocation sequences. The naming system is implicit and unvalidated — any string is accepted as a dataset name.","intents":["Reference datasets by name across multiple visualization requests","Reduce message size by using symbolic references instead of embedding data","Build multi-step workflows where data is saved once and visualized multiple times"],"best_for":["Multi-turn conversations where datasets are reused across multiple visualizations","Workflows that generate multiple charts from the same underlying data","LLM agents building exploratory analysis pipelines"],"limitations":["No namespace isolation — dataset names are global across all MCP clients in session","No validation of dataset existence — visualize_data will fail silently if name doesn't exist","No listing or discovery of saved datasets — LLM must remember dataset names","Name collisions silently overwrite previous datasets — no versioning or history"],"requires":["Dataset previously saved via save_data tool with matching name","Dataset name string (any non-empty string accepted)"],"input_types":["dataset name string"],"output_types":["dataset array (if found), or error (if not found)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","MCP client (Claude Desktop or compatible)","mcp Python framework 1.0.0+","Dataset previously saved via save_data tool","Valid Vega-Lite specification JSON (v5.x compatible)","--output_type argument set to 'png' or 'text' at server startup","For PNG mode: system dependencies for Vega-Lite rendering (browser/image library)","MCP-compatible client (Claude Desktop, or custom implementation)","Stdio communication channel (typically subprocess or pipe)","MCP client that supports tool schema inspection"],"failure_modes":["Data is session-scoped and lost on server restart — no persistence layer","No built-in data validation or schema enforcement — accepts any JSON-serializable structure","In-memory storage scales linearly with dataset size; no pagination or streaming for large tables","No access control or multi-user isolation — all data visible to all MCP clients in same session","Requires dataset to be pre-saved via save_data tool — no inline data support","No validation of Vega-Lite spec structure — invalid specs pass through to renderer","Template composition is shallow (data.values merge only) — doesn't support nested data transforms","No built-in support for Vega-Lite transforms or aggregations — data must be pre-aggregated","Output type is global and immutable per server instance — cannot mix PNG and text in single session","Text mode output quality depends on terminal width and character set — not suitable for complex charts","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"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":"2026-06-17T09:51:04.689Z","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=vega-lite","compare_url":"https://unfragile.ai/compare?artifact=vega-lite"}},"signature":"U2a4S1dpzivw2cuE+dQyKHS657neQ0AwJ1zHMiOaVV0sOoYaWcCPmtyIRwr1It+sICOC27TUDqYe5ltrBBvUCQ==","signedAt":"2026-06-20T22:43:14.145Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vega-lite","artifact":"https://unfragile.ai/vega-lite","verify":"https://unfragile.ai/api/v1/verify?slug=vega-lite","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"}}