define_wordLook up a word's definition, pronunciation, part of speech, and usage examples. Returns meaning, phonetic spelling, and example sentences.
| Name | Required | Description | Default |
|---|---|---|---|
| word | Yes | The word to look up |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "oneOf": [
+ {
+ "properties": {
+ "found": {
+ "description": "Word not found in dictionary",
+ "enum": [
+ false
+ ],
+ "type": "boolean"
+ },
+ "hint": {
+ "description": "Suggestion for retry or explanation of why word was not found",
+ "type": "string"
+ },
+ "word": {
+ "description": "The word that was looked up",
+ "type": "string"
+ }
+ },
+ "required": [
+ "word",
+ "found",
+ "hint"
+ ],
+ "type": "object"
+ },
+ {
+ "properties": {
+ "found": {
+ "description": "Word found in dictionary",
+ "enum": [
+ true
+ ],
+ "type": "boolean"
+ },
+ "meanings": {
+ "description": "List of meanings organized by part of speech",
+ "items": {
+ "properties": {
+ "definitions": {
+ "description": "Up to 3 definitions for this part of speech",
+ "items": {
+ "properties": {
+ "definition": {
+ "description": "Definition of the word",
+ "type": "string"
+ },
+ "example": {
+ "description": "Usage example sentence",
+ "type": [
+ "string",
+ "null"
+ ]
+ }
+ },
+ "required": [
+ "definition",
+ "example"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "part_of_speech": {
+ "description": "Part of speech (e.g., noun, verb, adjective)",
+ "type": "string"
+ }
+ },
+ "required": [
+ "part_of_speech",
+ "definitions"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "phonetic": {
+ "description": "Phonetic spelling of the word",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "word": {
+ "description": "The word that was looked up",
+ "type": "string"
+ }
+ },
+ "required": [
+ "word",
+ "found",
+ "phonetic",
+ "meanings"
+ ],
+ "type": "object"
+ }
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "word": "serendipity"
+ },
+ {
+ "word": "ephemeral"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds return-value details (meaning, phonetic spelling, example sentences), which provides some behavioral context, but no edge-case behaviors or limitations are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences convey the tool's purpose and output without extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one well-documented parameter, comprehensive read-only annotations, and an output schema, the description provides sufficient context for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The sole parameter 'word' is fully described in the schema with 100% coverage. The description confirms the word is the lookup target but adds no additional parameter semantics beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'look up' and identifies the resource as 'a word's definition, pronunciation, part of speech, and usage examples,' which clearly differentiates it from sibling tools like get_synonyms.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for obtaining word definitions and related linguistic information. It does not explicitly state when to prefer this over get_synonyms or other alternatives, but the context is clear enough for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have near-identical purposes: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), and ask_pipeworx_grounded are the same router with only an evidence-extraction difference. polymarket_edges, bet_research, and polymarket_arbitrage also overlap heavily in surfacing mispricings, and ai_visibility_check is essentially a single-entity version of scan_competitor_ai_presence.
There are recognizable patterns: ask_pipeworx_*, polymarket_*, verb_noun pairs like define_word, get_synonyms, resolve_entity. However, conventions are mixed across the set — ask_pipeworx_beta uses a suffix, ai_visibility_check vs scan_competitor_ai_presence are phrased in different styles, and memory tools (remember/recall/forget) follow yet another pattern. Readable but not predictable.
33 tools is heavy for a single server, and many of them are meta-tools (discover_tools, suggest_questions, ask_pipeworx variants, pipeworx_trending, pipeworx_feedback, memory tools) that inflate the surface. The count would be defensible if each tool were orthogonal, but the overlap in research/Polymarket/memory areas means several tools do not earn their place.
For the actual Pipeworx data-access domain, coverage is quite rich: lookup, grounded answers, deep research, entity profiles, comparisons, claim validation, subscriptions, memory, and discovery tools all exist. Minor gaps remain (e.g., no tool to manage account/API keys, patents soft-fail), but the core query-research-monitor lifecycle is well covered. The server name 'dictionary' is misleading — only two tools serve a dictionary purpose — yet the inferred domain from descriptions is a data gateway, for which the surface is strong.