dataset_infoVersion, Datenstand, Lizenz (CC BY 4.0), Zitiervorschlag, Methodik-Link, Modell-/Bewertungszahlen und die stabilen Download-URLs (CSV/JSON) des RadarScore-Datensatzes. / Dataset metadata, license and citation.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the content returned (version, license, URLs, etc.) and implies a read-only operation, but does not mention potential staleness, authentication, or side effects. It is adequate for a simple metadata lookup but lacks depth.
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?
One concise sentence in German with an English summary tail. Front-loaded with key terms, efficient, though the bilingual repetition adds slight length without additional meaning.
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?
For a zero-parameter metadata retrieval tool with no output schema, the description covers all expected information: what is included and the stable download URLs. It is complete enough for an agent to understand its purpose and expected result.
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?
No parameters exist (0 params, schema coverage 100%), so baseline is 4. The description itself does not need to explain parameters since there are none.
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?
Provides specific verb 'metadata' and lists exact fields (version, date, license, citation, methodology link, model scores, download URLs) for the RadarScore dataset. Clearly distinguishes from sibling tools that operate on robots.
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?
No explicit guidance on when to use this tool versus others. While it's a metadata resource, the description does not state that it should be used to obtain dataset context before querying data or compare with alternatives like search_robots.
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.
Each tool targets a distinct operation: compare, dataset metadata, single record retrieval, import cost calculation, and search. There is no overlap in purpose; even search and compare differ in that search returns compact rows while compare does side-by-side analysis.
Three tools follow a verb_noun pattern (compare_robots, search_robots, get_robot), but two use noun-based names (dataset_info, import_cost). This mixed convention is still readable but not consistently predictable.
5 tools is well-scoped for a dataset-focused server, covering search, retrieval, comparison, metadata, and a specialized calculation. Each tool earns its place without redundancy.
For a read-only evidence-based dataset server, the surface is complete: users can search, fetch full records, compare models, access metadata, and estimate import costs. No obvious gaps given the domain purpose.