Who spends most on lobbying in Germany: top organisations, totals per field of interest and type.
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "lobbying_ranking"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| field | No | ||
| limit | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It hints that results are aggregated per organisation/field/type, but says nothing about ordering (is it descending by spend?), whether results are paginated, what happens when limit is exceeded, or the data source/recency. For an aggregation tool with zero annotation coverage this is a significant gap.
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?
A single front-loaded sentence that covers the core deliverable with no filler or repetition. It is tight, though the compressed 'type' clause is ambiguous enough that the brevity comes at some cost to clarity.
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 no output schema, no annotations, and two undocumented parameters, the description should carry the load but does not. It never explains the shape of the response (ranked list? grouped totals?), how results are ordered or limited, or what values 'field' accepts, leaving an agent guessing before invocation.
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 0% on two parameters. The phrase 'field of interest' loosely maps to the 'field' parameter but never states accepted values, that it is optional, or what happens when omitted; the 'limit' parameter (default 25) is not mentioned at all. The description does not compensate for the coverage gap.
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 states a concrete resource and analytical angle: a ranking of lobbying spenders in Germany broken down into top organisations, totals per field of interest, and type. That is more specific than a name restatement and lets an agent recognise it as an aggregate/ranking tool rather than a lookup. It stops short of naming how it differs from siblings like search_lobby_register or get_lobby_register_entry.
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 sentence is phrased as a question ('Who spends most...'), which only implicitly suggests the use case. There is no explicit when-to-use, no statement of when a sibling such as search_lobby_register would be preferred, and no prerequisites or scope caveats.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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