fetch-mcp vs wink-embeddings-sg-100d
Side-by-side comparison to help you choose.
| Feature | fetch-mcp | wink-embeddings-sg-100d |
|---|---|---|
| Type | MCP Server | Repository |
| UnfragileRank | 29/100 | 24/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 5 decomposed |
| Times Matched | 0 | 0 |
Implements a Model Context Protocol server that exposes HTTP fetching as standardized tools via stdin/stdout communication. The server registers tool handlers with the MCP SDK, validates incoming requests using Zod schemas, and returns responses formatted according to MCP specification. This enables any MCP-compatible client (Claude, custom agents, etc.) to invoke web fetching without custom HTTP client implementation.
Unique: Implements MCP server pattern with stdio-based communication and Zod schema validation, enabling seamless integration with MCP-aware clients without requiring HTTP server infrastructure or custom protocol negotiation
vs alternatives: Simpler deployment than REST API servers (no port management, firewall rules) and more standardized than custom tool protocols, but less flexible than HTTP APIs for cross-language integration
Uses JSDOM to parse HTML documents into a virtual DOM, then extracts text content while removing HTML markup, scripts, and styling. The Fetcher class instantiates a JSDOM window, traverses the DOM tree, and returns cleaned text. This approach preserves text structure and readability while stripping all HTML artifacts, making content suitable for LLM processing without markup noise.
Unique: Leverages JSDOM's full DOM implementation rather than regex or simple HTML stripping, enabling accurate text extraction from complex nested structures and handling of edge cases like nested tags and entity encoding
vs alternatives: More accurate than regex-based HTML stripping (handles nested tags, entities correctly) but slower than lightweight parsers like cheerio; better for content extraction than for performance-critical scenarios
Integrates TurndownService to convert HTML documents into Markdown format while preserving semantic structure (headings, lists, links, emphasis). The service maps HTML elements to Markdown equivalents and applies configurable rules for handling edge cases. This enables LLMs to work with structured content that retains formatting cues without raw HTML complexity.
Unique: Uses TurndownService's rule-based HTML-to-Markdown mapping rather than simple regex replacement, enabling semantic preservation of document structure (headings, lists, links, emphasis) and handling of edge cases through configurable conversion rules
vs alternatives: Preserves more semantic structure than plain text extraction, making output more useful for LLMs; more reliable than regex-based converters but slower than simple text extraction
Fetches content from a URL, parses the response as JSON using native JSON.parse(), and validates the structure using Zod schemas. If parsing fails, returns an error response. This capability enables agents to reliably consume JSON APIs and validate response schemas before passing data downstream.
Unique: Combines native JSON.parse() with Zod schema validation in a single tool, enabling both parsing and structural validation without requiring separate validation steps or custom error handling in client code
vs alternatives: More robust than raw JSON.parse() (includes validation) but adds latency vs simple parsing; simpler than full OpenAPI client generation but less feature-rich
Fetches HTTP content from a URL using the native fetch API and returns the raw HTML response body. Supports optional custom HTTP headers (User-Agent, Authorization, etc.) to handle authentication, content negotiation, and server-specific requirements. This is the foundational capability that other transformations (text, Markdown, JSON) build upon.
Unique: Exposes native fetch API through MCP tool interface with support for custom headers, enabling agents to handle authentication, content negotiation, and server-specific requirements without custom HTTP client code
vs alternatives: Simpler than full HTTP client libraries (no dependency bloat) but less feature-rich than axios or node-fetch wrappers; native fetch is faster than alternatives but offers fewer convenience methods
Uses Zod schemas to validate all incoming tool requests before processing. Each tool (fetch_html, fetch_json, fetch_txt, fetch_markdown) has a corresponding Zod schema that validates URL format, header structure, and required fields. Invalid requests are rejected with structured error messages before reaching the fetcher logic, preventing malformed requests from consuming resources.
Unique: Implements Zod-based request validation at the MCP server layer before tool execution, providing type-safe input handling and structured error messages without requiring validation logic in individual tool implementations
vs alternatives: More robust than manual validation (catches edge cases) and provides better error messages than simple type checking; adds minimal latency vs runtime validation
Registers four tools (fetch_html, fetch_json, fetch_txt, fetch_markdown) with the MCP SDK and binds request handlers to each tool. The server implements the MCP tool listing protocol (returning tool schemas) and tool calling protocol (executing tools and returning results). This enables MCP clients to discover available tools and invoke them with proper request/response formatting.
Unique: Implements MCP tool registration pattern with static schema definitions and handler binding, enabling clients to discover and invoke tools through a standardized protocol without custom negotiation or discovery mechanisms
vs alternatives: More standardized than custom tool protocols but less flexible than dynamic tool registration; simpler than REST API servers but requires MCP-aware clients
Catches exceptions during fetch operations (network errors, timeouts, parsing failures) and returns structured error responses through the MCP protocol. Errors include descriptive messages indicating the failure type (network error, invalid URL, parsing failure, etc.) without exposing internal stack traces. This enables clients to handle failures gracefully and retry or fallback appropriately.
Unique: Implements error handling at the MCP server layer with descriptive error messages and no stack trace exposure, enabling clients to handle failures gracefully while maintaining security and debuggability
vs alternatives: More user-friendly than raw exception propagation but less detailed than structured error codes; simpler than full retry logic but requires client-side retry implementation
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
fetch-mcp scores higher at 29/100 vs wink-embeddings-sg-100d at 24/100. fetch-mcp leads on adoption and quality, while wink-embeddings-sg-100d is stronger on ecosystem.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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)