| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Uses MCP protocol to expose Agile Luminary stories as first-class IDE resources rather than requiring custom IDE plugins or REST API wrappers. Leverages MCP's resource discovery and tool invocation to provide IDE-agnostic integration that works across any MCP-compatible client.
vs alternatives: Simpler than building native IDE plugins for each editor (VS Code, JetBrains, etc.) because MCP provides a single standardized interface; more lightweight than browser-based project management tools because it brings data into the developer's existing workflow.
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.
Unique: Bridges project management data and AI code assistance by formatting Agile Luminary stories as structured context that AI models can consume, rather than treating stories as separate documentation. Uses MCP's context passing mechanism to make story requirements available to any MCP-compatible AI client without custom integrations.
vs alternatives: More integrated than copying story text into chat prompts because it maintains bidirectional synchronization; more flexible than hardcoded story templates because it adapts to any Agile Luminary story structure.
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.
Unique: Implements MCP tool definitions as a schema-based interface to Agile Luminary, allowing AI models to invoke story queries using standard function-calling syntax rather than requiring custom API wrappers. Abstracts Agile Luminary API complexity behind MCP's tool invocation pattern.
vs alternatives: More composable than REST API clients because MCP tools can be chained with other tools in the same context; more discoverable than direct API calls because tool schemas are self-documenting and available to any MCP-compatible client.
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.
Unique: Implements filtering as a client-side operation on MCP resources, avoiding repeated API calls for each filter variation. Uses MCP resource URI parameters to encode filter state, making filtered views shareable and bookmarkable within the IDE.
vs alternatives: Faster than browser-based filtering because it operates on already-fetched story data; more IDE-native than opening Agile Luminary in a separate tab because filtering happens within the editor's search interface.
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.
Unique: Uses MCP resource metadata to embed file references directly in story objects, enabling IDE navigation without requiring a separate code indexing service. Links are maintained at the MCP layer, making them available to any MCP-compatible IDE.
vs alternatives: More lightweight than code search tools because it relies on explicit story-to-file mappings rather than semantic analysis; more IDE-integrated than external story tracking tools because navigation happens within the editor.
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.
Unique: Implements story updates as MCP tools that can be invoked by AI assistants or developers, enabling both manual and automated status changes. Abstracts Agile Luminary API write operations behind MCP's tool invocation pattern, making updates available to any MCP-compatible client.
vs alternatives: More integrated than manual status updates in Agile Luminary because it happens within the IDE workflow; more flexible than hardcoded status transitions because it supports any Agile Luminary status value.
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.
Unique: Uses MCP to expose story data to AI models in a structured format, enabling AI-assisted planning without requiring custom story analysis tools. Leverages AI's reasoning capabilities to generate actionable task breakdowns from natural language story descriptions.
vs alternatives: More flexible than template-based task generation because AI adapts to story complexity; more integrated than external planning tools because analysis happens within the IDE context.
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Agile Luminary at 24/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities