snippetDictionary entry summary for a German word from DWDS.de — part of speech (Wortart), the base lemma, and a link to the full dictionary entry. Example: snippet({ query: "Haus" }).
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | German word, e.g. "Haus". |
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | Text response if JSON parsing failed | |
| format | No | Response format (json or text) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / query / descriptionAdded value: +"German word, e.g. \"Haus\"."Input schema / examplesAdded value: +[
+ {
+ "query": "Haus"
+ },
+ {
+ "query": "Liebe"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "JSON dictionary snippet for a German word from DWDS API",
+ "properties": {
+ "body": {
+ "description": "Text response if JSON parsing failed",
+ "type": "string"
+ },
+ "format": {
+ "description": "Response format (json or text)",
+ "type": "string"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only and idempotent behavior. The description adds value by specifying the return content (part of speech, lemma, link), providing behavioral context beyond what annotations offer. No contradictions.
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?
The description is a single sentence plus an example—concise, front-loaded, with no extraneous information. Every word serves a purpose.
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?
Given the tool's simplicity, annotations, and output schema, the description fully covers the needed context. It explains the purpose, example usage, and what the return includes.
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?
Schema coverage is 100% with a clear description for 'query'. The description provides an example ('Haus'), adding slight value beyond the schema, but does not significantly enhance parameter understanding.
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?
Description clearly states the tool provides a 'Dictionary entry summary for a German word from DWDS.de' with specific components (part of speech, lemma, link). This distinguishes it from sibling tools like 'dwds_frequency' or 'corpus_concordance'.
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 getting a quick summary and provides an example, but lacks explicit guidance on when to use versus alternatives or when not to use. No direct comparison with siblings.
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.
Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical; the Polymarket betting tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) also overlap. The server name suggests a German dictionary, but most tools are unrelated, causing confusion about the set's focus.
Tool names lack a consistent pattern: some use verb_noun (ask_pipeworx, compare_entities), some noun_noun (ai_visibility_check, dwds_frequency), some single words (forget, lemma), and some are acronyms (kwic). This mixed convention makes prediction of tool names difficult.
At 36 tools, the set is large but not extreme. However, it attempts to cover too many domains (German language, prediction markets, AI visibility, npm packages, etc.), making it feel bloated and unfocused for a server named 'Dwds'.
For the German dictionary focus implied by the server name, tools are minimal (dwfs_frequency, lemma, snippet) and two are retired. For the broader data platform, there are gaps like no dedicated SEC filing search tool, relying on generic ask_pipeworx. The set feels incomplete for both intended purposes.