Get population on 1 January by country code and year
Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"geo": "<geo>",
"year": "<year>"
},
"name": "get_population"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| geo | Yes | ||
| year | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden but only discloses the reference date (1 January). It says nothing about the unit or scale of the returned figure (persons vs thousands), the data source/vintage, or what happens with an unknown country code or year.
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 short sentence, front-loaded with the verb and resource, with zero filler. Nothing wasted — the problem is under-specification, not verbosity.
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?
A simple two-parameter tool, but with no annotations, no output schema, and 0% schema coverage, the description is the only source of truth and leaves key gaps: input formats and the shape/units of the result. It is not sufficient for an agent to call this reliably without trial and error.
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 description coverage is 0% — the schema offers only bare titles 'Geo' and 'Year'. The description adds that 'geo' is a country code and that 'year' selects the reference year, which is some compensation, but it never specifies the code format (ISO alpha-2/alpha-3/numeric) or the accepted year format, so the agent can still easily pass invalid values.
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?
States a specific verb (get) and resource (population) along with the exact measurement reference ('on 1 January'), which is a meaningful disambiguation for population data. No sibling tools exist, so there is no differentiation to make, which keeps it just below a 5.
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 never says when to use this tool, what preconditions apply, or what to do for other population measures (mid-year, by region, by age). Usage is only implied by the required parameters, leaving the agent to infer everything.
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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