| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Automatically instruments backend execution paths to generate causal traces showing how data flows through functions, API calls, and decision points. Uses AST analysis and runtime instrumentation to capture the dependency graph between inputs, intermediate states, and outputs without requiring manual annotation. Traces are structured as directed acyclic graphs (DAGs) that can be serialized and replayed for debugging or audit purposes.
Unique: Uses runtime instrumentation combined with AST analysis to automatically capture causal dependencies without manual annotation, creating queryable DAGs that preserve the complete decision path rather than just logging individual events
vs alternatives: Differs from traditional distributed tracing (Jaeger, Datadog) by capturing intra-process causal relationships and decision logic rather than just service boundaries, enabling root-cause analysis at the business logic level
Extracts the evidence, conditions, and decision rules that led to a specific backend outcome, then generates human-readable narratives explaining the decision chain. Analyzes the causal trace to identify which inputs were actually used in the decision (vs. which were available but ignored), reconstructs the logical conditions that were evaluated, and produces structured evidence objects that can be presented to users or AI agents. Supports template-based narrative generation for different audiences (technical, business, regulatory).
Unique: Combines causal trace analysis with template-based narrative generation to produce both structured evidence (for machines) and human-readable explanations (for users), bridging the gap between technical execution traces and business-level decision rationale
vs alternatives: Goes beyond SHAP/LIME model explainability by capturing the full decision chain including rule evaluation, data filtering, and conditional logic in deterministic systems, rather than approximating feature importance in black-box models
Automatically generates Model Context Protocol (MCP) tool definitions from instrumented backend functions and API endpoints, creating structured schemas that describe inputs, outputs, side effects, and decision logic. Analyzes the causal traces and evidence extraction to infer tool semantics (e.g., 'this function filters users by criteria and returns a ranked list'), generates OpenAPI-compatible schemas with proper type definitions, and produces MCP tool manifests that AI agents can consume. Includes automatic documentation generation from code comments and inferred behavior.
Unique: Generates MCP tool schemas by analyzing causal traces and decision evidence rather than just parsing function signatures, enabling schemas that capture semantic meaning (e.g., 'this tool filters and ranks results') and side effects that AI agents need to understand
vs alternatives: More semantically rich than generic OpenAPI generators because it uses execution traces to infer tool behavior and constraints, producing schemas that help AI agents make better decisions about when and how to use tools
Captures immutable state snapshots at each step of a causal trace, enabling developers to inspect the exact state of variables, function arguments, and return values at any point in the execution. Provides a queryable interface to jump to specific trace steps, inspect state diffs between consecutive steps, and replay execution from any checkpoint. Uses structural sharing and delta compression to minimize memory overhead while maintaining full state history.
Unique: Combines immutable state snapshots with structural sharing to enable efficient time-travel debugging without requiring external debugger attachment or process restart, making it practical for production incident investigation
vs alternatives: More practical than traditional debuggers for production systems because it captures complete state history without requiring live process attachment, and more efficient than full execution replay because it uses snapshots rather than re-running code
Integrates with rule engines and decision tree systems to automatically instrument rule evaluation, capture which rules matched/failed, and visualize the decision tree structure with execution paths highlighted. Supports multiple rule engine formats (JSON-based rules, Drools-style syntax, custom DSLs) and generates interactive flowchart visualizations showing the decision path taken during execution. Includes rule conflict detection and coverage analysis to identify unreachable rules or conflicting conditions.
Unique: Automatically instruments rule evaluation to capture which rules matched and in what order, then generates interactive visualizations that show the actual execution path rather than just the static rule structure, enabling business users to understand decisions without code knowledge
vs alternatives: More actionable than static rule documentation because it shows the actual execution path taken for specific inputs, and more comprehensive than simple rule logging because it includes conflict detection and coverage analysis
Provides state management for multi-step backend workflows and pipelines, automatically tracking state transitions, validating state changes against defined schemas, and enabling rollback to previous states. Integrates with causal tracing to record why state changed (which function triggered it, what conditions were met), and supports compensation logic for undoing operations in reverse order. Includes built-in support for saga patterns and distributed transaction coordination across service boundaries.
Unique: Combines state machine validation with causal tracing to record not just state changes but why they happened, enabling both rollback and audit trails that show the decision logic behind each transition
vs alternatives: More comprehensive than basic state machines because it includes compensation logic for distributed transactions and integrates with causal tracing for audit purposes, rather than just validating state transitions
Automatically generates structured logs from causal traces, integrating with standard observability platforms (Datadog, New Relic, CloudWatch, ELK). Converts trace data into structured log entries with proper correlation IDs, trace IDs, and span hierarchies compatible with OpenTelemetry standards. Enables querying and filtering logs by decision evidence, rule matches, and state changes rather than just text search. Includes automatic sampling and aggregation for high-volume systems to reduce storage costs.
Unique: Generates structured logs from causal traces with semantic meaning (decision evidence, rule matches) rather than just converting function calls to log lines, enabling queries that understand business logic rather than just text search
vs alternatives: Richer than generic distributed tracing because it captures decision logic and evidence, and more efficient than logging every function call because it uses intelligent sampling based on decision outcomes
Automatically generates compliance and audit reports from causal traces, decision evidence, and state histories. Supports multiple report formats (PDF, HTML, JSON) and compliance frameworks (GDPR, HIPAA, SOX, Fair Lending). Includes data lineage tracking to show which personal data was used in decisions, automatic redaction of sensitive information, and proof of decision rationale for regulatory review. Generates attestation documents showing that decisions were made according to defined rules and policies.
Unique: Generates compliance reports directly from causal traces and decision evidence, creating proof that decisions were made according to policy, rather than requiring manual documentation or separate audit systems
vs alternatives: More authoritative than manual audit documentation because it's generated from actual execution traces, and more comprehensive than generic audit logging because it includes decision rationale and data lineage
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 footprintjs at 26/100.
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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
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