Katzilla MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond annotations: it discloses the return format ('Returns the Katzilla envelope { data, quality, citation }'), explains quality scoring ('quality scores freshness/uptime/confidence'), and details citation components ('citation carries the source URL, license, and a SHA-256 data hash for audit'). This enriches behavioral understanding without contradicting annotations.

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 efficiently structured in two sentences: the first covers purpose, search methods, source, and updates; the second explains the return format and its components. Every sentence adds critical information (e.g., data scope, search options, return envelope details) with zero waste. It is front-loaded with the core functionality and appropriately sized for the tool's complexity.

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 moderate complexity, rich annotations (covering read-only, non-destructive, idempotent, open-world), 100% schema description coverage, and the presence of an output schema (implied by 'Has output schema: true'), the description is complete. It adds necessary context like the return envelope structure, quality scoring, and citation details, which the output schema likely documents but are usefully summarized. No gaps are evident for effective agent use.

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 100%, with both parameters ('endpoint' and 'value') well-documented in the input schema. The description adds minimal parameter semantics beyond the schema: it mentions 'Search by name, alpha code, or region,' which aligns with the enum values in the schema but does not provide additional syntax, format details, or examples. Given high schema coverage, the baseline score of 3 is appropriate as the description does not significantly enhance parameter understanding.

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 tool's purpose: 'Look up country information including population, capital, region, languages, currencies, and flags.' It specifies the verb ('look up'), resource ('country information'), and detailed scope of data returned. It distinguishes from siblings by focusing on demographics/population data from REST Countries, unlike other tools in the list (e.g., agriculture, crime, economic data tools).

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 provides clear context for usage: 'Search by name, alpha code, or region' and mentions the source and update frequency ('Source: REST Countries, updates annual'). However, it does not explicitly state when to use this tool versus alternatives (e.g., other demographics tools like demographics__census-acs or demographics__eurostat), nor does it provide exclusions or prerequisites. The guidance is helpful but lacks sibling differentiation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.