dwds_frequencyHow common a German word is: its absolute hit count and per-million frequency in the DWDS reference corpus. Answers "how frequent is the German word X", "how common is ". Example: dwds_frequency({ query: "Haus" }).
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | German word or lemma, e.g. "Haus". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "Haus"
+ },
+ {
+ "query": "gehen"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds that it returns absolute hit count and per-million frequency from the DWDS reference corpus, which is useful context. No contradictions or missing behavioral traits.
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 extremely concise, with two sentences and a brief example. The most important information (outputs and purpose) is front-loaded. Every word earns its place.
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 and the absence of an output schema, the description fully explains what the tool returns (hit count and per-million frequency). No additional context is necessary for an agent to use it 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 input schema has 100% coverage with a clear description of the 'query' parameter. The description adds an example and clarifies it accepts a German word or lemma, but this does not provide significant additional meaning beyond the schema.
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 clearly states that the tool computes how common a German word is, providing absolute hit count and per-million frequency. It also includes example questions and a usage example, making the purpose unambiguous and distinct from siblings like corpus_concordance or kwic.
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 implicitly tells when to use the tool (to answer frequency questions) but does not explicitly state when not to use it or mention alternatives. However, given the clarity of purpose, it is adequate.
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.