Katzilla MCP

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

The annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it specifies 'No API key required,' 'Source: OpenLigaDB (Public), updates daily,' and details about the return format ('Returns the Katzilla envelope { data, quality, citation } — quality scores freshness/uptime/confidence; citation carries the source URL, license, and a SHA-256 data hash for audit'). This includes rate limits (implied by 'updates daily'), data freshness, and auditability, which are not covered by 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 front-loaded with the core purpose in the first sentence, followed by additional details in a structured manner. Every sentence adds value: the first states the purpose, the second lists data types and leagues, the third covers API key and source, and the fourth explains the return format. There is no wasted text, and it efficiently conveys necessary information without redundancy.

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 complexity (4 parameters, 100% schema coverage, annotations, and an output schema), the description is complete enough. It covers the purpose, usage context, behavioral traits (like no API key and daily updates), and return format. With annotations providing safety hints and an output schema likely detailing the Katzilla envelope, the description does not need to explain return values further, making it well-rounded for 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?

The input schema has 100% description coverage, with each parameter (type, league, season, matchday) well-documented in the schema. The description does not add significant meaning beyond the schema, as it only mentions data types ('match results, current tables, and team data') and leagues ('Bundesliga (bl1), 2. Bundesliga (bl2), DFB Pokal, Champions League') which are already covered by the schema's enum and descriptions. Given the high schema coverage, the baseline score of 3 is appropriate.

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 explicitly states the tool's purpose: 'Free German and European football (soccer) data from OpenLigaDB. Get match results, current tables, and team data for Bundesliga (bl1), 2. Bundesliga (bl2), DFB Pokal, Champions League, and many other leagues.' It uses specific verbs ('get') and resources ('match results, current tables, team data'), and distinguishes itself from sibling tools by focusing on football data from OpenLigaDB, unlike other sports tools like 'league-schedules' or 'live-scores'.

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 when to use the tool: for accessing football data from specific leagues (e.g., Bundesliga, Champions League) via OpenLigaDB. It mentions 'No API key required' and 'updates daily,' which are useful usage considerations. However, it does not explicitly state when not to use it or name alternatives among sibling tools, such as 'sports__league-schedules' or 'sports__live-scores,' for different types of sports data.

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