{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-agile-luminary","slug":"agile-luminary","name":"Agile Luminary","type":"mcp","url":"https://github.com/AgileLuminary/mcp-agile-luminary","page_url":"https://unfragile.ai/agile-luminary","categories":["mcp-servers","code-editors"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-agile-luminary__cap_0","uri":"capability://tool.use.integration.mcp.based.project.management.story.synchronization","name":"mcp-based project management story synchronization","description":"Implements the Model Context Protocol (MCP) to establish a bidirectional bridge between Agile Luminary project management platform and IDE environments. The MCP server exposes project stories as resources that can be queried, filtered, and synchronized in real-time, allowing IDEs to fetch and display story metadata (title, description, acceptance criteria, status) without leaving the editor. Uses MCP's resource discovery and tool invocation patterns to abstract away HTTP API complexity.","intents":["I want to view my current sprint stories directly in my IDE without switching to a browser tab","I need to fetch story details and acceptance criteria while coding without context switching","I want my IDE to have real-time access to project management data via a standardized protocol"],"best_for":["Development teams using Agile Luminary for project management","IDE users (VS Code, JetBrains, etc.) who want integrated project context","Teams adopting MCP as a standard for tool integration"],"limitations":["Requires Agile Luminary account and API credentials — no offline-first capability","MCP server must be running as a separate process — adds deployment complexity vs native IDE plugins","Story synchronization is pull-based (IDE requests data) — no server-initiated push notifications for real-time updates"],"requires":["Agile Luminary account with API access","MCP-compatible IDE or client (VS Code with MCP extension, Claude Desktop, etc.)","Node.js 16+ or Python 3.8+ (depending on MCP server implementation)","Network connectivity to Agile Luminary API endpoints"],"input_types":["story identifiers (IDs, sprint names)","filter parameters (status, assignee, priority)","project/workspace context"],"output_types":["structured story objects (JSON)","formatted story descriptions with metadata","acceptance criteria lists"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agile-luminary__cap_1","uri":"capability://memory.knowledge.story.to.ide.context.injection","name":"story-to-ide context injection","description":"Automatically injects story metadata (title, description, acceptance criteria, linked code files) into the IDE's context window, making story information available to AI assistants and code completion tools. Implements context enrichment by parsing story objects and formatting them as structured prompts that can be consumed by language models or IDE intelligence features. Enables AI-assisted development where the LLM understands the current story requirements without explicit context passing.","intents":["I want my AI code assistant to understand the current story I'm working on without me copying/pasting requirements","I need acceptance criteria automatically available to code generation tools so they generate compliant code","I want story context to inform code completion suggestions and refactoring recommendations"],"best_for":["Developers using AI-assisted coding tools (GitHub Copilot, Claude, etc.) within their IDE","Teams practicing test-driven development who want acceptance criteria in the AI context","Agile teams where story requirements frequently change and need to stay synchronized with code"],"limitations":["Context injection only works with MCP-compatible AI clients — standard IDE code completion tools cannot consume this context","Story metadata must be manually linked to code files — no automatic file-to-story mapping","Large stories with extensive acceptance criteria may exceed AI context window limits, requiring manual truncation"],"requires":["MCP-compatible IDE with AI assistant integration (Claude Desktop, VS Code with MCP extension)","Agile Luminary story data accessible via MCP server","AI model with sufficient context window (4K+ tokens recommended)"],"input_types":["story objects (from Agile Luminary API)","acceptance criteria text","story descriptions and metadata"],"output_types":["formatted context prompts for AI models","structured story metadata for IDE display","enriched code completion context"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agile-luminary__cap_2","uri":"capability://tool.use.integration.story.status.and.metadata.querying.via.mcp.tools","name":"story status and metadata querying via mcp tools","description":"Exposes Agile Luminary story data through MCP tool definitions, allowing IDE clients and AI assistants to query story status, assignments, priority, and linked resources using standardized function-calling syntax. Implements a schema-based tool registry that maps MCP tool invocations to Agile Luminary API calls, handling authentication, pagination, and error responses transparently. Enables AI assistants to autonomously fetch story information and make decisions based on story state without user intervention.","intents":["I want my AI assistant to check story status and priority before suggesting code changes","I need to query which stories are assigned to me without leaving the IDE","I want AI to automatically fetch story details when I mention a story ID in chat"],"best_for":["AI-assisted development workflows where assistants need autonomous access to project data","Teams using Claude or other MCP-compatible AI models for code generation","Developers who want story information available via natural language queries in their IDE"],"limitations":["Tool calling requires explicit AI model support for function invocation — not all models handle MCP tools equally","Query latency depends on Agile Luminary API response times — no local caching of story data","Tool definitions must be manually maintained if Agile Luminary API schema changes"],"requires":["MCP client with tool-calling support (Claude Desktop, VS Code with MCP extension)","Agile Luminary API credentials with read permissions","AI model capable of function calling (Claude 3+, GPT-4, etc.)"],"input_types":["story IDs or names","filter parameters (status, assignee, sprint)","natural language queries"],"output_types":["story metadata (JSON objects)","status updates","assignment information","priority and timeline data"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agile-luminary__cap_3","uri":"capability://search.retrieval.ide.native.story.filtering.and.search","name":"ide-native story filtering and search","description":"Provides filtering and search capabilities within the IDE to query Agile Luminary stories by status, assignee, sprint, priority, and custom fields. Implements client-side filtering logic that works with MCP resource discovery, allowing developers to narrow story lists without making multiple API calls. Supports both simple keyword search and structured filtering using query parameters passed through MCP resource URIs.","intents":["I want to find all stories assigned to me in the current sprint without opening Agile Luminary","I need to search for stories by keyword or tag while staying in my IDE","I want to filter stories by status (in progress, blocked, ready for review) to prioritize my work"],"best_for":["Individual developers managing their own task list within the IDE","Teams with large backlogs who need quick story lookup without browser context switching","Developers using multiple projects who want unified story search across workspaces"],"limitations":["Search is limited to story metadata — cannot search story comments or linked code changes","Filtering performance degrades with large backlogs (1000+ stories) due to client-side filtering","Custom field filtering requires knowledge of Agile Luminary field names — no UI-driven filter builder"],"requires":["MCP server with story listing capability","IDE with MCP client support and search/filter UI","Agile Luminary API access with read permissions"],"input_types":["search keywords","filter parameters (status, assignee, sprint, priority)","custom field values"],"output_types":["filtered story lists","story metadata with matching criteria highlighted","story counts by filter"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agile-luminary__cap_4","uri":"capability://tool.use.integration.story.to.code.file.linking.and.navigation","name":"story-to-code file linking and navigation","description":"Establishes bidirectional links between Agile Luminary stories and code files in the IDE, allowing developers to navigate from a story to relevant code and vice versa. Implements file linking through MCP resource metadata that includes file paths and line numbers, enabling IDE features like 'go to story' and 'show related stories' for the current file. Uses code analysis or manual annotations to identify which files implement which stories.","intents":["I want to click on a story in my IDE and jump to the relevant code files","I need to see which stories are implemented in the current file I'm editing","I want to understand the relationship between code changes and the stories they address"],"best_for":["Teams practicing story-driven development with clear story-to-code mappings","Developers working on large codebases where story context is scattered across multiple files","Teams using branch naming conventions or commit messages that reference story IDs"],"limitations":["File linking requires manual configuration or code analysis — no automatic detection of story-to-file relationships","Links can become stale if files are moved or renamed without updating story metadata","Works only for files in the current workspace — cannot link to external repositories or dependencies"],"requires":["MCP server with file linking metadata","IDE with file navigation support (VS Code, JetBrains)","Story metadata that includes file paths or code references"],"input_types":["story objects with file references","code file paths","line number references"],"output_types":["file navigation links","story-to-file mappings","related story lists for current file"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agile-luminary__cap_5","uri":"capability://tool.use.integration.story.update.and.status.change.from.ide","name":"story update and status change from ide","description":"Allows developers to update story status, add comments, and modify metadata directly from the IDE without switching to Agile Luminary. Implements write operations through MCP tool invocations that map to Agile Luminary API endpoints, handling authentication and validation transparently. Supports common workflows like marking stories as 'in progress', 'blocked', or 'ready for review' with optional comment attachment.","intents":["I want to mark a story as 'in progress' when I start working on it from my IDE","I need to add a comment to a story explaining a blocker without leaving the editor","I want to update story status to 'ready for review' when I push my code"],"best_for":["Developers who want to minimize context switching between IDE and project management tools","Teams using automated workflows that trigger on story status changes","Agile teams practicing continuous status updates throughout the day"],"limitations":["Write operations require elevated API permissions — not all Agile Luminary accounts support programmatic updates","No conflict resolution if story is updated simultaneously in Agile Luminary and IDE","Bulk updates are not supported — only single-story operations available"],"requires":["Agile Luminary API credentials with write permissions","MCP server with tool definitions for story updates","IDE with MCP client support for tool invocation"],"input_types":["story ID","new status value","comment text","metadata fields to update"],"output_types":["updated story object","confirmation of status change","error messages if update fails"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agile-luminary__cap_6","uri":"capability://planning.reasoning.ai.assisted.story.decomposition.and.task.generation","name":"ai-assisted story decomposition and task generation","description":"Leverages AI models (via MCP context) to analyze stories and suggest task breakdowns, acceptance criteria refinements, or implementation approaches. The MCP server provides story content to AI assistants, which can then generate subtasks, estimate effort, or identify dependencies without explicit user prompts. Implements planning-reasoning patterns where AI understands story requirements and proposes structured work plans.","intents":["I want AI to suggest how to break down a large story into smaller tasks","I need AI to identify potential edge cases or acceptance criteria I might have missed","I want AI to estimate effort or identify dependencies in a story before starting work"],"best_for":["Teams practicing AI-assisted planning and estimation","Developers working on complex stories who want AI-generated task breakdowns","Agile teams using AI for story refinement and backlog grooming"],"limitations":["AI suggestions are only as good as story descriptions — poorly written stories produce poor decompositions","No automatic feedback loop — AI suggestions are not validated against actual story implementation","Requires manual review of AI-generated tasks — cannot automatically create subtasks in Agile Luminary"],"requires":["MCP-compatible AI model (Claude 3+, GPT-4, etc.) with planning capabilities","Story data accessible via MCP server","Sufficient context window for story analysis (4K+ tokens)"],"input_types":["story title and description","acceptance criteria","story metadata (priority, complexity)"],"output_types":["suggested task breakdowns","refined acceptance criteria","implementation approaches","effort estimates","dependency analysis"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Agile Luminary account with API access","MCP-compatible IDE or client (VS Code with MCP extension, Claude Desktop, etc.)","Node.js 16+ or Python 3.8+ (depending on MCP server implementation)","Network connectivity to Agile Luminary API endpoints","MCP-compatible IDE with AI assistant integration (Claude Desktop, VS Code with MCP extension)","Agile Luminary story data accessible via MCP server","AI model with sufficient context window (4K+ tokens recommended)","MCP client with tool-calling support (Claude Desktop, VS Code with MCP extension)","Agile Luminary API credentials with read permissions","AI model capable of function calling (Claude 3+, GPT-4, etc.)"],"failure_modes":["Requires Agile Luminary account and API credentials — no offline-first capability","MCP server must be running as a separate process — adds deployment complexity vs native IDE plugins","Story synchronization is pull-based (IDE requests data) — no server-initiated push notifications for real-time updates","Context injection only works with MCP-compatible AI clients — standard IDE code completion tools cannot consume this context","Story metadata must be manually linked to code files — no automatic file-to-story mapping","Large stories with extensive acceptance criteria may exceed AI context window limits, requiring manual truncation","Tool calling requires explicit AI model support for function invocation — not all models handle MCP tools equally","Query latency depends on Agile Luminary API response times — no local caching of story data","Tool definitions must be manually maintained if Agile Luminary API schema changes","Search is limited to story metadata — cannot search story comments or linked code changes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.6,"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":"2026-06-17T09:51:02.370Z","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=agile-luminary","compare_url":"https://unfragile.ai/compare?artifact=agile-luminary"}},"signature":"QrY0Dg/HkYMbSO1yWu1wiX/9UoXl54gPVRp3HVe0zmRnM62VspB2gHaxKtHd6MLYSTUv9kFXSbIXCuODr3ScCA==","signedAt":"2026-06-21T10:44:46.623Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agile-luminary","artifact":"https://unfragile.ai/agile-luminary","verify":"https://unfragile.ai/api/v1/verify?slug=agile-luminary","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"}}