| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes academic paper and scholarly document search through the Model Context Protocol, allowing LLM agents to query academic databases and retrieve full-text or metadata results. Implements MCP's resource and tool abstractions to standardize how AI systems access scholarly content without direct API integration, using a server-client architecture where scholarmcp acts as the protocol bridge between Claude/other LLMs and backend academic sources.
Unique: Implements scholarly document access as a standardized MCP resource, allowing any MCP-compatible LLM client to query academic sources without custom integrations, using MCP's protocol-level abstraction for tool discovery and resource streaming
vs alternatives: Decouples scholarly API complexity from LLM applications via MCP's standard interface, whereas direct API integration requires per-application credential management and custom parsing logic
Registers academic search and retrieval operations as MCP tools with standardized JSON schemas, enabling LLM clients to discover available search capabilities (e.g., 'search by keyword', 'fetch by DOI', 'filter by publication date') and invoke them with type-safe argument validation. Uses MCP's tool registry pattern where scholarmcp defines tool schemas that Claude and other LLM clients can introspect and call with structured parameters.
Unique: Exposes academic search as MCP tools with introspectable JSON schemas, allowing LLM clients to discover and validate search parameters at runtime rather than hardcoding API calls, following MCP's tool registry pattern for composable agent capabilities
vs alternatives: Provides schema-driven tool discovery and validation compared to direct API calls, reducing integration friction and enabling LLMs to understand available operations without external documentation
Streams scholarly document content through MCP's resource protocol in chunks, allowing LLM clients to retrieve large papers incrementally without loading entire documents into memory or context windows. Uses MCP's resource URI pattern to reference documents and supports partial content retrieval, enabling agents to fetch abstracts, sections, or full text on-demand while managing token budgets.
Unique: Implements MCP resource streaming for academic documents, allowing incremental content delivery that respects LLM context budgets, using MCP's resource URI and streaming abstractions rather than single-request document APIs
vs alternatives: Enables context-aware document retrieval compared to APIs that return full documents, reducing token waste and supporting longer research workflows within fixed context windows
Abstracts multiple academic data sources (PubMed, arXiv, CrossRef, etc.) behind a unified MCP interface, allowing clients to query across sources with a single tool call. Implements source-agnostic search and result normalization, translating source-specific APIs into consistent JSON schemas that LLM clients can consume uniformly without knowing which backend provided the result.
Unique: Aggregates heterogeneous academic APIs (PubMed, arXiv, CrossRef) into a single MCP tool interface with result normalization, allowing LLM clients to query multiple sources without custom per-source integration logic
vs alternatives: Reduces integration burden compared to building separate connectors for each academic database, providing unified search semantics across sources with automatic result normalization
Enables agents to navigate citation relationships between papers, extracting references from a paper and recursively fetching cited works. Implements graph traversal patterns where scholarmcp maintains citation relationships and allows clients to explore 'papers that cite this work', 'papers cited by this work', and 'related papers by co-authors', using MCP tools to expose graph navigation as composable operations.
Unique: Exposes citation graph traversal as MCP tools, allowing agents to navigate research relationships without building custom graph databases, using lazy-loaded citation fetching to manage memory and latency
vs alternatives: Enables citation-aware research discovery compared to keyword-only search, allowing agents to understand research lineage and influence without external knowledge graph infrastructure
Provides author and institution metadata retrieval through MCP tools, allowing agents to fetch researcher profiles, publication histories, and institutional affiliations. Implements author disambiguation (matching 'John Smith' across papers) and institution normalization, returning structured profiles with publication counts, research areas, and co-author networks that help agents understand research context and credibility.
Unique: Provides author and institution lookup as MCP tools with built-in disambiguation and normalization, allowing agents to understand researcher context without querying multiple author databases separately
vs alternatives: Enables credibility assessment and researcher discovery compared to paper-only search, providing author context that helps agents evaluate research quality and find related researchers
Computes semantic similarity between papers and clusters results by research topic using embeddings or topic modeling, exposed through MCP tools. Allows agents to find 'papers similar to this one' or 'papers in the same research area' without explicit keyword matching, using vector similarity or LDA-based clustering to group related work semantically rather than syntactically.
Unique: Exposes semantic similarity and topic clustering as MCP tools, allowing agents to discover related papers without keyword matching, using pre-computed embeddings or on-demand similarity computation
vs alternatives: Enables semantic research discovery compared to keyword-based search, helping agents find relevant work across terminology boundaries and discover adjacent research areas
Extracts and normalizes publication metadata (title, authors, abstract, publication date, journal, volume, pages, DOI) from heterogeneous academic sources into consistent JSON schemas. Handles format variations across sources (e.g., different author name formats, date representations) and validates metadata completeness, allowing agents to work with standardized paper records regardless of source.
Unique: Provides automatic metadata extraction and normalization across heterogeneous academic sources, translating source-specific formats into consistent JSON schemas that agents can consume uniformly
vs alternatives: Reduces data cleaning burden compared to manual parsing of source-specific formats, enabling agents to work with standardized paper records without custom per-source extraction logic
+1 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 scholarmcp at 24/100.
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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