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| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes NumPy operations through the Model Context Protocol (MCP) transport layer, translating LLM-generated function calls into native Python NumPy operations with automatic type marshalling and error handling. Uses MCP's resource and tool abstractions to expose NumPy's 200+ array manipulation functions as callable tools with JSON schema validation, enabling stateless computation requests from any MCP-compatible client.
Unique: Implements NumPy as an MCP server resource, allowing LLMs to call NumPy functions directly via standardized tool schemas rather than requiring custom API wrappers or subprocess management. Uses MCP's declarative tool registry to expose NumPy's function signatures with automatic JSON schema generation.
vs alternatives: Simpler integration than custom REST APIs or subprocess-based NumPy runners because it leverages MCP's native tool-calling protocol, eliminating boilerplate serialization code and providing automatic schema validation.
Provides high-level array operations (reshape, transpose, concatenate, split, slice) with automatic dimension inference and broadcasting rules applied transparently. Translates user intent (e.g., 'flatten this 3D array') into optimized NumPy calls, handling edge cases like mismatched dimensions or incompatible shapes with descriptive error messages that guide users toward valid operations.
Unique: Implements automatic shape inference and broadcasting validation at the MCP tool layer, catching dimension mismatches before NumPy execution and providing corrective guidance. Uses NumPy's internal broadcasting rules to validate operations before execution.
vs alternatives: More user-friendly than raw NumPy API calls because it validates shapes and suggests corrections, whereas direct NumPy calls often fail with cryptic dimension mismatch errors.
Exposes NumPy's logical and comparison functions (equal, not_equal, greater, less, logical_and, logical_or, logical_not) through MCP tool definitions with automatic broadcasting and dtype handling. Implements element-wise comparisons that return boolean arrays suitable for masking or conditional operations.
Unique: Implements NumPy comparison and logical operations as MCP tools with automatic broadcasting, enabling agents to create boolean masks and perform conditional logic without direct Python syntax
vs alternatives: Provides NumPy's optimized comparison operations through MCP with broadcasting support, more flexible than simple equality checks while maintaining type safety through schema validation
Exposes NumPy's dtype conversion and inspection functions (astype, dtype property, itemsize, nbytes) through MCP tool definitions with automatic dtype validation and conversion safety checks. Implements schema-based dtype specification supporting all NumPy scalar types (int, float, complex, bool, string, datetime).
Unique: Implements NumPy dtype conversion and inspection as MCP tools with schema-based dtype specification and metadata exposure, enabling agents to validate and convert array types without direct Python code
vs alternatives: Provides NumPy's dtype system through MCP with full scalar type support and metadata inspection, more comprehensive than simple type casting while maintaining safety through schema validation
Exposes NumPy's linear algebra module (numpy.linalg) through MCP, enabling matrix operations like inversion, eigenvalue decomposition, SVD, QR factorization, and solving linear systems. Handles numerical stability concerns (singular matrices, ill-conditioned systems) with fallback strategies and returns decomposition results with condition number warnings when appropriate.
Unique: Wraps numpy.linalg operations with automatic condition number computation and numerical stability warnings, alerting users when matrices are ill-conditioned or near-singular. Provides fallback strategies (e.g., regularization suggestions) for problematic inputs.
vs alternatives: More robust than direct NumPy calls because it proactively warns about numerical issues and suggests mitigation strategies, whereas raw numpy.linalg often silently produces inaccurate results for ill-conditioned matrices.
Implements statistical functions (mean, median, std, percentile, correlation, covariance) with axis-aware reduction, allowing aggregation along specific dimensions while preserving others. Automatically handles NaN/Inf values with configurable strategies (skip, fill, propagate) and returns both point estimates and confidence intervals when requested.
Unique: Implements axis-aware reduction with configurable NaN handling strategies at the MCP layer, allowing LLMs to specify how to treat missing data without requiring separate preprocessing steps. Automatically computes and returns data quality metadata (NaN counts, valid sample sizes).
vs alternatives: More flexible than pandas for MCP integration because it exposes NumPy's axis parameter directly, enabling fine-grained control over reduction dimensions without requiring a separate DataFrame abstraction.
Exposes NumPy's random module (numpy.random) through MCP with support for multiple distributions (normal, uniform, exponential, Poisson, etc.) and explicit seed control for reproducible results. Maintains separate random state per request or allows persistent state management for sequential sampling workflows.
Unique: Implements explicit seed control at the MCP tool layer, allowing LLM agents to request reproducible random sequences by specifying seeds, whereas typical API-based RNG services don't expose seed parameters. Supports both stateless (seed-based) and stateful (persistent generator) modes.
vs alternatives: More reproducible than cloud-based RNG APIs because seeds are explicit and deterministic, enabling LLM agents to generate identical random sequences across multiple runs for testing and validation.
Provides polynomial fitting (numpy.polyfit) and evaluation (numpy.polyval) capabilities, allowing LLM agents to fit polynomial curves to data and evaluate them at new points. Includes automatic degree selection heuristics and residual analysis to help users choose appropriate polynomial orders without overfitting.
Unique: Wraps numpy.polyfit with automatic residual analysis and degree selection heuristics, helping LLM agents choose appropriate polynomial orders by computing R² and suggesting regularization when overfitting is detected. Exposes both fitting and evaluation in a single tool.
vs alternatives: Simpler than scipy.interpolate for basic polynomial fitting because it requires fewer parameters and provides automatic guidance on degree selection, whereas scipy requires explicit interpolation method specification.
+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 NumPy Computation Server at 30/100.
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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