datamuse-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| find_synonymsB | Find synonyms (words with similar meanings) for a given word. Uses WordNet and corpus data. Optionally bias results toward a specific topic. |
| find_antonymsA | Find antonyms (words with opposite meanings) for a given word using WordNet relationships. |
| find_rhymesA | Find words that rhyme with a given word. Supports perfect rhymes and near-rhymes. Useful for poetry, songwriting, and creative writing. |
| find_similar_soundingA | Find words that sound similar to the input. Useful for spelling correction, finding homophones, creating puns, or phonetic matching. |
| find_by_patternA | Find words matching a spelling pattern with wildcards. Use '' for any number of characters, '?' for exactly one character. Examples: 't??k' matches talk/tank/tick, 'unable' matches unable/unforgettable. |
| find_related_wordsA | Find words semantically related to the input. Can find words by meaning similarity, statistical associations (triggers), or hierarchical relationships (hypernyms/hyponyms). |
| find_adjectives_for_nounB | Find adjectives commonly used to describe a noun. Based on Google Books Ngrams data. Example: 'ocean' → deep, blue, vast, pacific. |
| find_nouns_for_adjectiveA | Find nouns commonly described by an adjective. Based on Google Books Ngrams data. Example: 'yellow' → sun, light, fever, pages. |
| get_word_infoA | Get detailed information about a word including: definitions, part of speech, syllable count, pronunciation (IPA), and usage frequency. |
| autocompleteA | Get word suggestions for a partial input. Includes intelligent spelling correction and phonetic fallbacks when exact prefix matches aren't found. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 10 tools
Each tool targets a distinct lexical relationship (synonyms, antonyms, rhymes, sound-alikes, pattern, related words, adjective-noun, noun-adjective, info, autocomplete) with no overlap. An agent can easily select the correct tool based on the desired operation.
Most tools follow a 'find_' prefix pattern (e.g., find_synonyms, find_rhymes), but 'get_word_info' and 'autocomplete' deviate. This is a minor inconsistency in an otherwise coherent naming scheme.
10 tools is a well-scoped set for a word query server. Each tool provides essential functionality without redundancy, making the surface manageable and purposeful.
The tools cover a broad range of word relationships: synonyms, antonyms, rhymes, phonetic matching, pattern matching, semantic relations, collocations, dictionary info, and autocomplete. There are no obvious gaps for common lexical queries.