@todoforai/puppeteer-mcp-server vs wink-embeddings-sg-100d
Side-by-side comparison to help you choose.
| Feature | @todoforai/puppeteer-mcp-server | wink-embeddings-sg-100d |
|---|---|---|
| Type | MCP Server | Repository |
| UnfragileRank | 24/100 | 24/100 |
| Adoption | 0 | 0 |
| Quality |
| 0 |
| 0 |
| Ecosystem | 0 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 5 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
Provides pre-trained 100-dimensional word embeddings derived from GloVe (Global Vectors for Word Representation) trained on English corpora. The embeddings are stored as a compact, browser-compatible data structure that maps English words to their corresponding 100-element dense vectors. Integration with wink-nlp allows direct vector retrieval for any word in the vocabulary, enabling downstream NLP tasks like semantic similarity, clustering, and vector-based search without requiring model training or external API calls.
Unique: Lightweight, browser-native 100-dimensional GloVe embeddings specifically optimized for wink-nlp's tokenization pipeline, avoiding the need for external embedding services or large model downloads while maintaining semantic quality suitable for JavaScript-based NLP workflows
vs alternatives: Smaller footprint and faster load times than full-scale embedding models (Word2Vec, FastText) while providing pre-trained semantic quality without requiring API calls like commercial embedding services (OpenAI, Cohere)
Enables calculation of cosine similarity or other distance metrics between two word embeddings by retrieving their respective 100-dimensional vectors and computing the dot product normalized by vector magnitudes. This allows developers to quantify semantic relatedness between English words programmatically, supporting downstream tasks like synonym detection, semantic clustering, and relevance ranking without manual similarity thresholds.
Unique: Direct integration with wink-nlp's tokenization ensures consistent preprocessing before similarity computation, and the 100-dimensional GloVe vectors are optimized for English semantic relationships without requiring external similarity libraries or API calls
vs alternatives: Faster and more transparent than API-based similarity services (e.g., Hugging Face Inference API) because computation happens locally with no network latency, while maintaining semantic quality comparable to larger embedding models
@todoforai/puppeteer-mcp-server scores higher at 24/100 vs wink-embeddings-sg-100d at 24/100. @todoforai/puppeteer-mcp-server leads on adoption and quality, while wink-embeddings-sg-100d is stronger on ecosystem.
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Retrieves the k-nearest words to a given query word by computing distances between the query's 100-dimensional embedding and all words in the vocabulary, then sorting by distance to identify semantically closest neighbors. This enables discovery of related terms, synonyms, and contextually similar words without manual curation, supporting applications like auto-complete, query suggestion, and semantic exploration of language structure.
Unique: Leverages wink-nlp's tokenization consistency to ensure query words are preprocessed identically to training data, and the 100-dimensional GloVe vectors enable fast approximate nearest-neighbor discovery without requiring specialized indexing libraries
vs alternatives: Simpler to implement and deploy than approximate nearest-neighbor systems (FAISS, Annoy) for small-to-medium vocabularies, while providing deterministic results without randomization or approximation errors
Computes aggregate embeddings for multi-word sequences (sentences, phrases, documents) by combining individual word embeddings through averaging, weighted averaging, or other pooling strategies. This enables representation of longer text spans as single vectors, supporting document-level semantic tasks like clustering, classification, and similarity comparison without requiring sentence-level pre-trained models.
Unique: Integrates with wink-nlp's tokenization pipeline to ensure consistent preprocessing of multi-word sequences, and provides simple aggregation strategies suitable for lightweight JavaScript environments without requiring sentence-level transformer models
vs alternatives: Significantly faster and lighter than sentence-level embedding models (Sentence-BERT, Universal Sentence Encoder) for document-level tasks, though with lower semantic quality — suitable for resource-constrained environments or rapid prototyping
Supports clustering of words or documents by treating their embeddings as feature vectors and applying standard clustering algorithms (k-means, hierarchical clustering) or dimensionality reduction techniques (PCA, t-SNE) to visualize or group semantically similar items. The 100-dimensional vectors provide sufficient semantic information for unsupervised grouping without requiring labeled training data or external ML libraries.
Unique: Provides pre-trained semantic vectors optimized for English that can be directly fed into standard clustering and visualization pipelines without requiring model training, enabling rapid exploratory analysis in JavaScript environments
vs alternatives: Faster to prototype with than training custom embeddings or using API-based clustering services, while maintaining semantic quality sufficient for exploratory analysis — though less sophisticated than specialized topic modeling frameworks (LDA, BERTopic)