@todoforai/puppeteer-mcp-server vs vectra
Side-by-side comparison to help you choose.
| Feature | @todoforai/puppeteer-mcp-server | vectra |
|---|---|---|
| Type | MCP Server | Repository |
| UnfragileRank | 26/100 | 38/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Exposes Puppeteer's browser automation capabilities through the Model Context Protocol (MCP), allowing LLM agents to control a headless Chrome/Chromium instance via standardized tool calls. Implements MCP server transport layer that translates LLM function-calling requests into Puppeteer API invocations, managing browser lifecycle, page state, and screenshot/DOM capture for agent feedback loops.
Unique: Implements MCP server transport layer specifically for Puppeteer, enabling direct LLM agent control of browser automation without custom integration code. Uses MCP's standardized tool schema to expose Puppeteer methods as callable functions, with built-in screenshot and DOM evaluation capabilities for agent feedback.
vs alternatives: Provides MCP-native browser automation (compatible with Claude and other MCP clients) whereas raw Puppeteer requires custom API wrappers; simpler integration than Selenium-based MCP servers due to Puppeteer's JavaScript-native design.
Provides MCP tools for navigating to URLs, waiting for page load conditions, and interacting with page elements (click, type, select, scroll). Implements Puppeteer's page navigation API with configurable wait strategies (networkidle, domcontentloaded) and element interaction via CSS selectors or XPath, returning success/failure status and error details to the agent.
Unique: Wraps Puppeteer's page navigation and interaction APIs in MCP tool schema, exposing configurable wait strategies and element targeting (CSS/XPath) as discrete agent-callable functions. Includes error propagation to agent with specific failure reasons (element not found, timeout, navigation blocked).
vs alternatives: More flexible than Selenium-based automation (supports XPath and CSS equally) and faster than Playwright MCP due to Puppeteer's lighter footprint; native MCP integration means no custom client code needed.
Enables agents to extract page content via DOM queries, JavaScript evaluation, and screenshot capture. Implements Puppeteer's page.evaluate() for arbitrary JavaScript execution, page.$() for DOM element selection, and page.screenshot() for visual state capture. Returns structured data (text, HTML, JSON) or base64-encoded images for agent processing.
Unique: Combines Puppeteer's page.evaluate(), page.$(), and page.screenshot() into MCP tools with structured output formatting. Supports arbitrary JavaScript execution for complex data extraction while maintaining agent-friendly error handling and output serialization.
vs alternatives: More powerful than simple DOM parsing (supports JavaScript evaluation) and more flexible than screenshot-only approaches; native MCP integration eliminates custom client code for screenshot handling and base64 encoding.
Manages multiple browser pages/tabs within a single browser instance, allowing agents to switch between pages, open new pages, and maintain separate DOM/navigation contexts. Implements Puppeteer's browser.newPage() and page management, with context switching via page identifiers. Each page maintains independent cookies, localStorage, and navigation history.
Unique: Exposes Puppeteer's multi-page browser model through MCP tools, allowing agents to manage page lifecycle (create, switch, close) with explicit context tracking. Each page maintains independent DOM, cookies, and navigation state, enabling parallel workflows.
vs alternatives: Enables true multi-page workflows whereas single-page MCP servers require sequential navigation; more memory-efficient than multiple browser instances while maintaining isolation.
Provides tools for reading, setting, and clearing cookies and session storage across pages. Implements Puppeteer's page.cookies() and page.setCookie() APIs, allowing agents to persist authentication tokens, manage session state, and simulate returning users. Supports cookie attributes (domain, path, expiry, secure, httpOnly).
Unique: Wraps Puppeteer's cookie management APIs in MCP tools with full attribute support (domain, path, expiry, secure, httpOnly). Enables agents to manage session state across page interactions without re-authentication.
vs alternatives: More complete than screenshot-based session validation; provides programmatic session control vs manual cookie jar management in other automation frameworks.
Allows agents to intercept, monitor, and modify network requests/responses via Puppeteer's request interception API. Implements request.abort(), request.continue(), and request.respond() to block ads, mock API responses, or log network activity. Provides visibility into network timing, status codes, and response bodies for debugging and validation.
Unique: Exposes Puppeteer's request interception API through MCP tools, enabling agents to abort, continue, or respond to network requests with custom data. Includes network monitoring for debugging and validation without requiring external proxy tools.
vs alternatives: More integrated than external proxy-based interception (no separate tool setup); more flexible than simple request blocking (supports response mocking and modification).
Provides isolated browser contexts (separate cookies, cache, storage) for parallel or independent workflows. Implements Puppeteer's browser.createIncognitoBrowserContext() or context-based isolation, allowing agents to run multiple independent sessions without cross-contamination. Each context has its own cookies, localStorage, sessionStorage, and IndexedDB.
Unique: Exposes Puppeteer's browser context API through MCP tools, enabling agents to create isolated browser contexts with separate cookies, storage, and cache. Supports incognito mode for privacy-focused testing.
vs alternatives: More memory-efficient than multiple browser instances; provides true isolation without process-level overhead; simpler than manual cookie/storage management for multi-user scenarios.
Captures and exposes browser console output (logs, warnings, errors) and page errors to agents for debugging and validation. Implements Puppeteer's page.on('console'), page.on('error'), and page.on('pageerror') event listeners, streaming console messages and uncaught exceptions to the agent for real-time monitoring.
Unique: Streams browser console output and page errors to agents via MCP tools, providing real-time visibility into JavaScript execution. Captures console.log/warn/error and uncaught exceptions without requiring manual page inspection.
vs alternatives: More integrated than DevTools Protocol inspection (no separate tool needed); provides structured error data vs screenshot-based debugging.
+1 more capabilities
Stores vector embeddings and metadata in JSON files on disk while maintaining an in-memory index for fast similarity search. Uses a hybrid architecture where the file system serves as the persistent store and RAM holds the active search index, enabling both durability and performance without requiring a separate database server. Supports automatic index persistence and reload cycles.
Unique: Combines file-backed persistence with in-memory indexing, avoiding the complexity of running a separate database service while maintaining reasonable performance for small-to-medium datasets. Uses JSON serialization for human-readable storage and easy debugging.
vs alternatives: Lighter weight than Pinecone or Weaviate for local development, but trades scalability and concurrent access for simplicity and zero infrastructure overhead.
Implements vector similarity search using cosine distance calculation on normalized embeddings, with support for alternative distance metrics. Performs brute-force similarity computation across all indexed vectors, returning results ranked by distance score. Includes configurable thresholds to filter results below a minimum similarity threshold.
Unique: Implements pure cosine similarity without approximation layers, making it deterministic and debuggable but trading performance for correctness. Suitable for datasets where exact results matter more than speed.
vs alternatives: More transparent and easier to debug than approximate methods like HNSW, but significantly slower for large-scale retrieval compared to Pinecone or Milvus.
Accepts vectors of configurable dimensionality and automatically normalizes them for cosine similarity computation. Validates that all vectors have consistent dimensions and rejects mismatched vectors. Supports both pre-normalized and unnormalized input, with automatic L2 normalization applied during insertion.
vectra scores higher at 38/100 vs @todoforai/puppeteer-mcp-server at 26/100.
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Unique: Automatically normalizes vectors during insertion, eliminating the need for users to handle normalization manually. Validates dimensionality consistency.
vs alternatives: More user-friendly than requiring manual normalization, but adds latency compared to accepting pre-normalized vectors.
Exports the entire vector database (embeddings, metadata, index) to standard formats (JSON, CSV) for backup, analysis, or migration. Imports vectors from external sources in multiple formats. Supports format conversion between JSON, CSV, and other serialization formats without losing data.
Unique: Supports multiple export/import formats (JSON, CSV) with automatic format detection, enabling interoperability with other tools and databases. No proprietary format lock-in.
vs alternatives: More portable than database-specific export formats, but less efficient than binary dumps. Suitable for small-to-medium datasets.
Implements BM25 (Okapi BM25) lexical search algorithm for keyword-based retrieval, then combines BM25 scores with vector similarity scores using configurable weighting to produce hybrid rankings. Tokenizes text fields during indexing and performs term frequency analysis at query time. Allows tuning the balance between semantic and lexical relevance.
Unique: Combines BM25 and vector similarity in a single ranking framework with configurable weighting, avoiding the need for separate lexical and semantic search pipelines. Implements BM25 from scratch rather than wrapping an external library.
vs alternatives: Simpler than Elasticsearch for hybrid search but lacks advanced features like phrase queries, stemming, and distributed indexing. Better integrated with vector search than bolting BM25 onto a pure vector database.
Supports filtering search results using a Pinecone-compatible query syntax that allows boolean combinations of metadata predicates (equality, comparison, range, set membership). Evaluates filter expressions against metadata objects during search, returning only vectors that satisfy the filter constraints. Supports nested metadata structures and multiple filter operators.
Unique: Implements Pinecone's filter syntax natively without requiring a separate query language parser, enabling drop-in compatibility for applications already using Pinecone. Filters are evaluated in-memory against metadata objects.
vs alternatives: More compatible with Pinecone workflows than generic vector databases, but lacks the performance optimizations of Pinecone's server-side filtering and index-accelerated predicates.
Integrates with multiple embedding providers (OpenAI, Azure OpenAI, local transformer models via Transformers.js) to generate vector embeddings from text. Abstracts provider differences behind a unified interface, allowing users to swap providers without changing application code. Handles API authentication, rate limiting, and batch processing for efficiency.
Unique: Provides a unified embedding interface supporting both cloud APIs and local transformer models, allowing users to choose between cost/privacy trade-offs without code changes. Uses Transformers.js for browser-compatible local embeddings.
vs alternatives: More flexible than single-provider solutions like LangChain's OpenAI embeddings, but less comprehensive than full embedding orchestration platforms. Local embedding support is unique for a lightweight vector database.
Runs entirely in the browser using IndexedDB for persistent storage, enabling client-side vector search without a backend server. Synchronizes in-memory index with IndexedDB on updates, allowing offline search and reducing server load. Supports the same API as the Node.js version for code reuse across environments.
Unique: Provides a unified API across Node.js and browser environments using IndexedDB for persistence, enabling code sharing and offline-first architectures. Avoids the complexity of syncing client-side and server-side indices.
vs alternatives: Simpler than building separate client and server vector search implementations, but limited by browser storage quotas and IndexedDB performance compared to server-side databases.
+4 more capabilities