| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes LLM API calls directly within spreadsheet cells using a custom formula syntax (e.g., =PROMPTLOOP(prompt, model, parameters)), enabling users to process entire columns of data through language models without leaving their spreadsheet application. The system maintains bidirectional data binding between cells and API responses, automatically handling rate limiting, retry logic, and result caching to prevent duplicate API calls on formula recalculation.
Unique: Implements LLM execution as native spreadsheet formulas with automatic result caching and retry logic, eliminating the need for users to learn APIs or switch applications—the spreadsheet itself becomes the orchestration layer
vs alternatives: Faster context-switching than Zapier/Make (no workflow builder UI) and more accessible than Python scripts, but slower than dedicated batch processing APIs due to per-cell execution overhead
Abstracts API differences across OpenAI, Anthropic, Cohere, and other LLM providers through a unified parameter interface, allowing users to swap models (GPT-4, Claude, Command) within spreadsheet formulas without rewriting prompts or handling provider-specific authentication. The system translates common parameters (temperature, max_tokens, top_p) to provider-native formats and manages separate API keys per provider, enabling cost optimization by routing requests to the cheapest available model.
Unique: Implements a thin abstraction layer that translates unified parameter syntax to provider-native APIs, enabling model swapping without formula changes—similar to ORM patterns in databases but for LLM providers
vs alternatives: More flexible than single-provider tools (Copilot, ChatGPT) but less feature-complete than dedicated multi-provider frameworks (LangChain) due to spreadsheet formula constraints
Allows users to define custom functions (e.g., SENTIMENT_ANALYSIS, ENTITY_EXTRACTION) that encapsulate a prompt template, model selection, and output parsing logic. These functions can be reused across multiple spreadsheets and shared with team members, reducing duplication and enabling consistent prompt logic across projects. Functions support parameter binding, allowing callers to override specific aspects (model, temperature, output schema) without modifying the underlying prompt.
Unique: Implements user-defined functions as first-class abstractions in spreadsheets, enabling prompt logic encapsulation and reuse without requiring programming knowledge
vs alternatives: More accessible than LangChain's custom tools or OpenAI's custom GPTs but less flexible than general-purpose programming functions which support arbitrary logic and composition
Supports parameterized prompt templates using placeholder syntax (e.g., {{column_name}}, {{A1}}) that dynamically inject spreadsheet cell values into prompts at execution time. The system parses template strings, validates that referenced cells exist, and performs string interpolation before sending the final prompt to the LLM API, enabling reusable prompt patterns across multiple rows without manual editing.
Unique: Implements lightweight template substitution directly in spreadsheet formulas using cell references, avoiding the need for external template engines while maintaining spreadsheet-native data binding
vs alternatives: Simpler than Jinja2 or Handlebars templating but less powerful; more accessible to non-programmers than prompt frameworks like LangChain's PromptTemplate
Queues multiple LLM API calls triggered by spreadsheet formulas and executes them with configurable rate limiting (e.g., max 10 requests/second) and exponential backoff retry logic to handle transient API failures. The system tracks request state (pending, success, failed, retrying) per cell and prevents duplicate API calls if a formula is recalculated, using content-based deduplication to identify identical requests.
Unique: Implements transparent batch queuing and retry logic at the spreadsheet formula level, hiding API complexity from users while maintaining cell-level visibility into request state
vs alternatives: More user-friendly than raw API batch endpoints (no JSON formatting required) but less sophisticated than dedicated job orchestration systems (Temporal, Airflow) which offer fine-grained control and observability
Caches LLM API responses at the cell level using a content hash of the prompt as the cache key, preventing redundant API calls when formulas are recalculated or spreadsheets are reopened. Users can manually invalidate cache entries per cell or globally, and the system tracks cache hit/miss rates to show cost savings. Cache is persisted in PromptLoop's backend, not in the spreadsheet itself, enabling cache sharing across users editing the same sheet.
Unique: Implements transparent, content-addressed caching at the spreadsheet cell level with backend persistence, enabling cache sharing across users without requiring explicit cache management
vs alternatives: More convenient than manual result storage (copy-paste) but less flexible than application-level caching (Redis, Memcached) which supports TTL, invalidation policies, and distributed cache invalidation
Accepts a JSON schema definition from the user and validates LLM responses against that schema, extracting structured fields (e.g., sentiment, confidence, entities) from unstructured LLM output. The system uses schema-based prompting techniques (e.g., appending schema to the prompt or using function calling APIs) to encourage the LLM to output valid JSON, then parses and validates the response, returning individual fields as separate cell values or a single JSON object.
Unique: Integrates JSON schema validation directly into spreadsheet formulas, enabling structured data extraction without requiring users to write parsing logic or handle JSON manually
vs alternatives: More accessible than regex-based parsing or custom Python scripts but less flexible than dedicated data extraction tools (Zapier, Make) which support multiple output formats and error recovery strategies
Tracks API costs for each LLM call (based on token counts and provider pricing) and aggregates costs by model, provider, and time period. The system displays cost dashboards showing total spend, cost per row, and cost trends, enabling users to identify expensive operations and optimize spending. Cost data is tied to individual cells, allowing users to see which spreadsheet operations are most expensive.
Unique: Provides cell-level cost attribution and aggregation directly in spreadsheets, making API spending transparent without requiring external billing dashboards or manual cost calculation
vs alternatives: More granular than provider-native billing dashboards (which show account-level costs only) but less sophisticated than dedicated FinOps tools (Kubecost, CloudZero) which support complex cost allocation and chargeback models
+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.
PromptLoop scores higher at 45/100 vs Supabase at 42/100. PromptLoop leads on adoption and quality, while Supabase is stronger on ecosystem.
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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