| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a three-layer bridge pattern that translates incoming MCP protocol requests into Gemini CLI commands and marshals structured responses back through the MCP SDK. The server uses @modelcontextprotocol/sdk to handle MCP protocol handshakes, tool registration, and response serialization, while spawning Gemini CLI processes as child processes to execute analysis tasks. This architecture decouples the MCP client (Claude Desktop) from the Gemini CLI runtime, enabling async request handling and graceful error propagation.
Unique: Uses MCP protocol as the abstraction layer rather than direct Gemini API calls, enabling Claude Desktop to treat Gemini as a pluggable tool without modifying Claude's core. The bridge pattern isolates CLI invocation complexity from the MCP server logic, allowing independent updates to Gemini CLI without MCP server changes.
vs alternatives: Lighter-weight than building a full Gemini API SDK integration into Claude; leverages existing Gemini CLI tooling rather than reimplementing analysis logic, reducing maintenance burden.
Implements a file reference system using @ prefix notation (e.g., @src/main.js, @., @package.json) that resolves file paths and directory structures, then passes them to Gemini CLI for multimodal processing. The system parses @ tokens from user prompts, validates file existence, and constructs Gemini CLI arguments that include file content or directory trees. This enables users to reference local files directly in natural language prompts without manual copy-paste, leveraging Gemini's ability to process large file contexts in a single request.
Unique: Uses @ prefix notation as a lightweight syntax for file references, avoiding the need for separate file upload APIs or complex UI interactions. Integrates directly with Gemini's native file processing capabilities, allowing the CLI to handle multimodal analysis without intermediate transformation.
vs alternatives: Simpler than REST API-based file upload systems (e.g., OpenAI's file API) because it leverages Gemini CLI's built-in file handling; more intuitive than requiring users to manually copy file contents into prompts.
Captures Gemini CLI exit codes, stdout, and stderr, interpreting them to construct meaningful error messages that are returned through the MCP protocol. The system treats non-zero exit codes as failures, extracts error details from stderr, and wraps them in MCP error responses. This approach provides visibility into Gemini CLI failures without requiring users to debug CLI output directly, though error messages depend on Gemini CLI's error formatting.
Unique: Implements error handling at the MCP protocol boundary, translating CLI-level errors into MCP-compatible error responses. This approach isolates error handling from the CLI implementation, allowing the MCP server to provide consistent error semantics regardless of CLI version.
vs alternatives: More user-friendly than raw CLI output because errors are formatted as MCP responses; more transparent than silent failures because all errors are captured and reported.
Provides a sandbox-test tool that routes code snippets to Gemini's isolated execution environment, allowing safe testing and validation of code without running it locally. The system accepts code input via the /sandbox slash command or sandbox-test tool, passes it to Gemini CLI with sandbox execution flags, and returns execution results including stdout, stderr, and exit codes. This leverages Gemini's built-in sandboxing to prevent malicious code execution while enabling rapid code testing within the Claude workflow.
Unique: Delegates code execution to Gemini's managed sandbox rather than implementing a local sandbox, eliminating the need to manage container runtimes or security policies. This approach trades execution speed for safety and simplicity, relying on Gemini's infrastructure for isolation.
vs alternatives: Safer than local code execution because it runs in Gemini's isolated environment; simpler than setting up Docker or other containerization because it requires no local infrastructure.
Exposes Gemini analysis capabilities through two complementary interfaces: natural language tool calls (ask-gemini tool) and structured slash commands (/analyze, /sandbox, /help, /ping). The MCP server registers both tool definitions in the MCP protocol, allowing Claude to invoke either interface based on context. Natural language tools enable flexible, conversational analysis requests, while slash commands provide explicit, structured invocation for power users. Both routes converge on the same underlying Gemini CLI execution logic, providing consistency while supporting different user preferences.
Unique: Provides both natural language and command-based interfaces at the MCP protocol level, allowing Claude to choose the most appropriate invocation method dynamically. This dual-interface design is implemented as separate tool definitions in the MCP server, not as post-processing of a single tool.
vs alternatives: More flexible than CLI-only tools because it supports conversational invocation; more explicit than pure natural language because slash commands provide unambiguous syntax for automation.
Supports dynamic selection between multiple Gemini model variants (gemini-2.5-flash, gemini-pro, gemini-nano) by passing model selection flags to the Gemini CLI. The system allows users to specify which model to use for analysis tasks, enabling trade-offs between speed (flash), capability (pro), and cost/latency (nano). Model selection is passed through MCP tool parameters or environment configuration, and the MCP server constructs appropriate Gemini CLI arguments based on the selected model.
Unique: Exposes model selection as a first-class parameter in the MCP interface, allowing Claude to reason about which model to use based on task requirements. Rather than hardcoding a single model, the system treats model selection as a configurable decision point.
vs alternatives: More flexible than single-model systems because it enables cost-performance optimization per task; more transparent than automatic model selection because users understand which model is being used.
Uses Zod schema validation to define tool parameters and validate inputs before passing them to Gemini CLI. The MCP server registers tools with structured schemas (ask-gemini, sandbox-test, etc.) that specify required parameters, types, and constraints. When Claude invokes a tool, the MCP server validates the parameters against the Zod schema, returning validation errors if parameters are malformed. This ensures that only valid inputs reach the Gemini CLI, reducing downstream errors and improving user experience.
Unique: Integrates Zod validation directly into the MCP tool registration layer, ensuring that all tool invocations are validated before CLI execution. This approach treats validation as a protocol-level concern rather than delegating it to the CLI.
vs alternatives: More robust than CLI-level validation because errors are caught before subprocess spawning; more explicit than implicit validation because schemas are declarative and inspectable.
Provides /ping and /help slash commands that enable users to verify MCP server connectivity and understand available tools without executing analysis tasks. The /ping command sends a test message to the Gemini CLI and returns connection status, confirming that the MCP server, Gemini CLI, and API credentials are all functional. The /help command displays available tools, their parameters, and usage examples. These diagnostic tools reduce troubleshooting time and provide self-service documentation.
Unique: Implements diagnostic commands at the MCP protocol level rather than as separate CLI utilities, allowing users to verify connectivity without leaving Claude Desktop. This integration reduces context switching and makes troubleshooting more accessible.
vs alternatives: More convenient than running separate CLI commands because diagnostics are available within Claude; more user-friendly than reading documentation because help is contextual and interactive.
+3 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 gemini-mcp-tool at 36/100.
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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