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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol (MCP) specification to expose Buildkite's REST API as a standardized tool registry that MCP-compatible clients (Claude Desktop, VSCode, GitHub Copilot, Goose, Zed Editor) can discover and invoke. The server translates MCP tool invocations into authenticated Buildkite API calls, handles response marshaling, and returns structured JSON results through stdio or HTTP transport layers. This abstraction eliminates the need for clients to implement Buildkite API authentication and request formatting directly.
Unique: Uses mark3labs/mcp-go v0.31.0 framework to implement full MCP specification compliance, enabling bidirectional tool discovery and invocation without custom protocol handling. Supports both stdio and HTTP transports in a single binary, allowing deployment as desktop companion or server.
vs alternatives: Provides standardized MCP interface to Buildkite, whereas direct API clients require custom authentication and request handling per tool; MCP abstraction enables any MCP-compatible client to access Buildkite without modification.
Exposes two tools (get_pipeline, list_pipelines) that query Buildkite's pipeline API to retrieve full pipeline definitions, including steps, environment variables, branch configuration, and metadata. The server caches pipeline metadata in memory to reduce API calls for repeated queries. Responses include pipeline ID, name, repository URL, and step definitions in structured JSON format, enabling AI tools to understand pipeline structure for analysis or modification recommendations.
Unique: Directly maps Buildkite's GraphQL/REST pipeline API responses to MCP tool outputs, preserving full step definitions and environment variable structures. In-memory caching layer reduces API calls for repeated pipeline queries within a session.
vs alternatives: Provides structured pipeline metadata through MCP, whereas raw Buildkite API requires clients to handle authentication and pagination; MCP abstraction enables AI tools to reason about pipeline structure without API knowledge.
Implements MCP tool registration mechanism that exposes 20+ Buildkite tools (pipelines, builds, jobs, clusters, tests, artifacts) as discoverable MCP tools with JSON schema definitions. The server registers tools with mark3labs/mcp-go framework, which handles tool discovery requests from MCP clients and returns tool names, descriptions, and parameter schemas. Enables MCP clients to discover available Buildkite operations and understand required parameters without external documentation.
Unique: Registers 20+ Buildkite tools with mark3labs/mcp-go framework, providing JSON schema definitions for each tool's parameters. Enables MCP clients to discover tools and validate parameters without external documentation.
vs alternatives: Provides tool discovery through MCP protocol, whereas alternatives require manual documentation or API exploration; MCP discovery enables clients to understand available operations programmatically.
Implements error handling layer that catches Buildkite API errors (authentication failures, not found, rate limits) and translates them into MCP-compliant error responses with descriptive messages. The server formats all responses (success and error) according to MCP protocol specification, ensuring clients receive consistent, parseable responses. Enables MCP clients to handle errors gracefully and provide meaningful feedback to users.
Unique: Translates Buildkite API errors into MCP-compliant error responses with descriptive messages, ensuring clients receive consistent error format regardless of underlying API failure. Implements error handling at MCP protocol level.
vs alternatives: Provides MCP-compliant error responses, whereas alternatives may return raw API errors or inconsistent formats; MCP abstraction ensures clients can handle errors uniformly.
Implements get_build and list_builds tools that retrieve build execution records from Buildkite, including status (passed/failed/running), timestamps, commit information, and branch metadata. The server translates MCP parameters (pipeline slug, build number, filters) into Buildkite API queries and returns paginated results. Supports filtering by branch, state, and commit to enable targeted queries of build history without retrieving entire datasets.
Unique: Translates MCP tool parameters into Buildkite API filter queries, enabling AI tools to retrieve targeted build subsets without fetching entire history. Preserves commit and branch metadata for correlation with source code changes.
vs alternatives: Provides filtered build history through MCP, whereas raw Buildkite API requires clients to implement pagination and filtering logic; MCP abstraction enables AI tools to query build status without API expertise.
Exposes get_jobs and get_job_logs tools that retrieve individual job records and their execution logs from Buildkite builds. The server queries the Buildkite API for job metadata (status, duration, agent name) and raw log output, returning logs as plain text or structured JSON. Enables AI tools to analyze job failures, performance issues, or error patterns by examining actual execution output without requiring access to external log storage systems.
Unique: Directly exposes Buildkite's job log API through MCP, preserving raw log output for AI analysis without intermediate parsing or transformation. Separates job metadata retrieval from log fetching to enable selective queries.
vs alternatives: Provides job logs through MCP without requiring external log aggregation systems, whereas alternatives require integration with ELK, Datadog, or similar; MCP abstraction enables AI tools to access logs directly from Buildkite.
Implements test engine tools (list_test_runs, get_test_run, get_failed_test_executions, get_test) that query Buildkite's test analytics API to retrieve test execution records, including pass/fail status, duration, and failure reasons. The server translates MCP parameters into Buildkite test engine API queries and returns structured test data. Enables AI tools to identify flaky tests, analyze failure patterns, and correlate test failures with code changes.
Unique: Integrates with Buildkite's Test Engine API (separate from main CI API) to provide structured test result data, including failure reasons and flakiness metrics. Enables AI tools to perform test-level analysis without parsing unstructured log output.
vs alternatives: Provides structured test results through MCP, whereas alternatives require parsing test framework output or integrating with separate test management systems; MCP abstraction enables AI tools to analyze test failures directly from Buildkite.
Exposes cluster management tools (get_cluster, list_clusters, get_cluster_queue, list_cluster_queues) that retrieve information about Buildkite agent clusters and job queues. The server queries the Buildkite API for cluster configuration, queue status, and agent availability. Enables AI tools to understand job routing, identify queue bottlenecks, and make recommendations for cluster scaling or queue optimization.
Unique: Provides cluster and queue APIs through MCP, enabling AI tools to reason about job routing and infrastructure capacity without direct Buildkite API access. Separates cluster discovery from queue status queries for flexible monitoring.
vs alternatives: Provides cluster metrics through MCP, whereas alternatives require custom monitoring integrations with Prometheus or CloudWatch; MCP abstraction enables AI tools to understand infrastructure status directly from Buildkite.
+4 more capabilities
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 Buildkite at 23/100.
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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