Retrieve machine-readable 2023 legislative election results for Luxembourg, including national totals and breakdowns by circonscription (constituency).
Get machine-readable 2023 legislative election results, national and per circonscription.
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Changes observed during successful MCP inspections.
v0.7.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description mentions 'machine-readable' but does not disclose behavioral traits beyond that. With no annotations provided, the description carries the full burden, yet it omits details about read-only nature, rate limits, authentication, or data format.
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 succinct sentence that conveys the essential information without filler. It is appropriately sized and front-loaded.
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 (no parameters, no output schema), the description is largely complete. It specifies the data scope (national and per circonscription) and year. Minor gaps like exact format are acceptable for a straightforward tool.
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 zero parameters and schema description coverage is 100%. Per guidelines, zero parameters baseline is 4. The description adds no parameter detail, but none is needed.
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 the verb 'Get' and the specific resource 'machine-readable 2023 legislative election results, national and per circonscription'. It distinguishes from sibling tools that focus on other data domains like parking or weather.
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 does not explicitly state when to use this tool versus alternatives, nor does it provide exclusion criteria or prerequisites. However, the specificity of '2023 legislative election results' implies its use case, but guidance is absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.