| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Compiles LaTeX source files to PDF using pdflatex or xelatex backend, capturing compilation logs and parsing error/warning messages to surface actionable feedback. The MCP server wraps the LaTeX compiler subprocess, monitors exit codes, and extracts diagnostic information from .log files to report missing packages, syntax errors, and undefined references back to the client.
Unique: Integrates LaTeX compilation as an MCP tool, allowing Claude and other MCP clients to trigger document builds and parse diagnostics programmatically without shell access, enabling AI-assisted debugging of LaTeX errors
vs alternatives: Unlike standalone LaTeX editors, this MCP integration lets AI agents autonomously compile documents, analyze errors, and suggest fixes within a multi-turn conversation context
Searches academic paper repositories (arXiv, CrossRef, or similar APIs) using citation metadata or keywords, downloads PDFs, and organizes them into a local library structure. The server queries external APIs with author/title/DOI information, validates download URLs, and stores papers with metadata for later retrieval and analysis.
Unique: Parses LaTeX bibliography files directly and orchestrates multi-source paper discovery (arXiv, CrossRef, institutional repositories) through a single MCP interface, enabling Claude to autonomously build research libraries without manual DOI lookups
vs alternatives: More integrated than Zotero or Mendeley for LaTeX workflows — directly reads .bib files and triggers downloads programmatically, vs. requiring manual import/export steps
Parses LaTeX bibliography files (.bib, .bibtex) and CSL JSON formats to extract citation metadata (authors, title, year, DOI, URL), validates entries for completeness, and reorganizes citations by category or author. The server uses regex and structured parsing to normalize citation formats and detect missing required fields.
Unique: Integrates bibliography parsing as an MCP tool, allowing Claude to inspect and validate citations in real-time during document editing, and suggest corrections or missing metadata without leaving the conversation context
vs alternatives: More lightweight and AI-integrated than Zotero or JabRef — provides structured citation data directly to LLMs for analysis and correction, vs. requiring manual GUI interaction
Executes Python, R, or MATLAB visualization scripts embedded in or referenced by LaTeX documents, captures output plots/figures, and saves them as image files (PNG, PDF, SVG) suitable for inclusion in LaTeX. The server manages script execution in isolated environments, handles dependencies, and maps generated figures to LaTeX \includegraphics commands.
Unique: Orchestrates script execution as an MCP tool with automatic figure output detection and LaTeX integration, allowing Claude to regenerate plots on-demand and suggest data visualization improvements based on script output
vs alternatives: More flexible than Jupyter notebooks for LaTeX workflows — executes arbitrary scripts and captures outputs for direct LaTeX inclusion, vs. requiring manual export/conversion steps
Generates LaTeX code snippets for including figures (\includegraphics), tables (\begin{table}), and captions, automatically calculating dimensions, positioning, and label references. The server takes image files or table data as input, generates properly formatted LaTeX environments, and optionally inserts them at specified locations in the document.
Unique: Generates contextually-aware LaTeX code for figures and tables based on image dimensions and data structure, and can insert them at specified document locations, enabling Claude to autonomously assemble documents from components
vs alternatives: More automated than manual LaTeX coding — generates proper \includegraphics and \begin{table} blocks with correct dimensions and labels, vs. requiring developers to write boilerplate code
Parses LaTeX source files to extract document structure (sections, subsections, chapters, environments), builds a hierarchical outline, and identifies cross-references (\ref, \cite, \label). The server uses regex or AST-based parsing to map document sections and enables querying specific sections or finding undefined references.
Unique: Parses LaTeX document structure and cross-references as an MCP tool, enabling Claude to understand document organization, identify broken references, and suggest structural improvements without manual inspection
vs alternatives: More programmatic than TeXstudio or Overleaf outline views — provides structured data about document organization to LLMs for analysis and automated refactoring
Manages LaTeX projects with multiple source files (main document, chapters, includes), tracks dependencies, and orchestrates compilation of the root document while handling \input and \include directives. The server maintains a project manifest, resolves file references, and ensures all dependencies are compiled in correct order.
Unique: Tracks LaTeX project dependencies and orchestrates multi-file compilation through MCP, allowing Claude to manage complex document structures and suggest refactoring to improve build times or modularity
vs alternatives: More intelligent than simple shell scripts — understands LaTeX \input/\include semantics and can compile subsets of projects, vs. requiring manual file management
Scans LaTeX source files for \usepackage commands, identifies required packages, checks if they are installed in the local TeX distribution, and provides installation instructions for missing packages. The server parses package declarations, queries the TeX package database, and suggests apt/brew/tlmgr commands for installation.
Unique: Automatically detects missing LaTeX packages and generates platform-specific installation commands through MCP, enabling Claude to diagnose and fix compilation errors without manual package lookup
vs alternatives: More proactive than error messages alone — scans source files upfront and suggests installations before compilation, vs. waiting for compilation to fail
+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 Latex MCP Server at 25/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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