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| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Integrates heterogeneous data sources (financial databases, regulatory filings, corporate records, web sources) into a unified document store accessible via MCP protocol. Uses a source-agnostic indexing layer that normalizes metadata and content formats, enabling cross-source search and retrieval without requiring clients to manage individual API connections or authentication.
Unique: Implements MCP as the integration layer, allowing LLM clients to access aggregated documents without custom middleware — the protocol itself handles source abstraction and context window management
vs alternatives: Avoids vendor lock-in to proprietary document platforms by using open MCP standard, enabling any MCP-compatible LLM to access consolidated due diligence data
Parses unstructured documents (PDFs, Word files, regulatory filings) to extract key entities, financial metrics, risk factors, and contractual terms into structured formats (JSON, tables). Uses pattern matching, NLP-based entity recognition, and domain-specific parsers for financial statements and legal clauses to normalize heterogeneous document formats into queryable data structures.
Unique: Exposes extraction as MCP tools callable by LLMs, allowing agents to iteratively extract, validate, and re-extract data with context-aware refinement rather than one-shot batch processing
vs alternatives: Tighter integration with LLM reasoning than standalone extraction APIs — the LLM can reason about extraction confidence and request re-extraction with clarifying context
Analyzes organizational documents (org charts, board minutes, shareholder records, management bios) to extract stakeholder information, identify key decision-makers, and map organizational structure. Implements relationship mapping to identify conflicts of interest, related-party transactions, and governance issues. Flags unusual ownership structures or control mechanisms requiring legal review.
Unique: Implements relationship mapping across stakeholders to identify conflicts of interest and related-party transactions, with governance assessment flagging unusual control mechanisms or ownership structures.
vs alternatives: Automates organizational analysis that would otherwise require manual review of multiple documents, while maintaining governance flags for items requiring legal judgment.
Analyzes multiple documents (e.g., target company financials vs. industry benchmarks, current contracts vs. proposed amendments) to identify discrepancies, inconsistencies, and missing information. Uses semantic comparison and structured data diffing to highlight gaps in due diligence coverage and flag material differences that require investigation.
Unique: Operates on extracted structured data within the MCP context, allowing LLM agents to reason about gaps and request targeted re-extraction or additional document retrieval to fill identified holes
vs alternatives: Integrates gap identification into the LLM's reasoning loop rather than as a separate reporting tool, enabling dynamic investigation workflows
Scans documents and extracted data for predefined risk categories (financial, legal, operational, regulatory, reputational) and assigns severity scores based on materiality, frequency, and business impact. Uses rule-based detection, keyword matching, and LLM-based reasoning to identify issues and contextualize them within the deal scope.
Unique: Embeds risk assessment as an MCP tool callable during LLM reasoning, enabling agents to iteratively investigate flagged issues and request additional analysis rather than generating static risk reports
vs alternatives: Integrates risk identification into the LLM's decision-making loop, allowing agents to prioritize investigation and ask follow-up questions about flagged issues
Generates structured due diligence reports by combining extracted data, comparative analyses, risk assessments, and LLM-generated insights into customizable templates (executive summary, detailed findings, risk matrix, recommendation). Uses template engines to format output and supports multiple output formats (PDF, Word, HTML) for stakeholder distribution.
Unique: Integrates LLM-generated narrative insights with structured data and templates via MCP, allowing agents to generate context-aware reports that combine quantitative findings with qualitative analysis
vs alternatives: Combines template-based structure with LLM reasoning to produce reports that are both consistent (via templates) and contextually relevant (via LLM insights)
Enables LLM clients to ask natural language questions about due diligence documents and receive answers grounded in extracted data and document content. Uses retrieval-augmented generation (RAG) to fetch relevant document excerpts and structured data, then uses LLM reasoning to synthesize answers with citations and confidence levels.
Unique: Exposes Q&A as an MCP tool, allowing LLM agents to ask follow-up questions and refine understanding iteratively within a single conversation context rather than requiring separate document retrieval steps
vs alternatives: Tighter integration with LLM reasoning than document search APIs — the LLM can ask clarifying questions and refine queries based on previous answers
Coordinates multi-step due diligence workflows (document collection → extraction → analysis → risk assessment → reporting) via MCP, managing state, dependencies, and error handling across steps. Enables definition of custom workflows as sequences of MCP tool calls with conditional logic and parallel execution where applicable.
Unique: Implements workflow orchestration as MCP tools, allowing LLM agents to define and execute workflows dynamically rather than requiring static workflow definitions
vs alternatives: Enables LLM agents to adapt workflows based on deal characteristics and findings, rather than executing fixed workflows
+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 Due Diligence Assistant at 32/100.
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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