Kolada MCP Server

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

Annotations already provide safety information (readOnlyHint: true, destructiveHint: false, idempotentHint: true), so the bar is lower. The description adds valuable context beyond annotations: it explains the interpretation of correlation values ('Värden nära 1 = starkt positivt samband...' - values near 1 = strong positive relationship...), which helps the agent understand output semantics. It doesn't mention rate limits or authentication needs, but with good annotation coverage, this is sufficient.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by an example and interpretation guidance. Every sentence adds value (clarifying the statistical method, providing a concrete use case, explaining result interpretation). It could be slightly more structured but remains efficient.

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 (statistical correlation with 5 parameters) and rich schema/annotations (100% coverage, clear enums, safety hints), the description is reasonably complete. It explains what the tool does and how to interpret results. The main gap is no output schema, but the description compensates by explaining correlation value semantics. It doesn't cover all edge cases but provides enough for basic 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 all parameters well-documented in the schema (e.g., 'gender' with enum descriptions). The description doesn't add any parameter-specific information beyond what the schema provides. According to guidelines, when schema coverage is high (>80%), the baseline score is 3 even without param info in the description.

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: 'Beräkna Pearson-korrelation mellan två KPIs' (Calculate Pearson correlation between two KPIs). It specifies the exact statistical method (Pearson correlation) and resource (KPIs), distinguishing it from siblings like 'get_kpi' (retrieval) or 'analyze_kpi_across_municipalities' (different analysis scope). The example further clarifies the application domain.

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 implies usage context through the example ('T.ex. korrelation mellan lärartäthet och skolresultat' - e.g., correlation between teacher density and school results), suggesting it's for exploring relationships between metrics. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_kpi_trend' (trend analysis) or 'compare_municipalities' (geographic comparison), nor does it mention prerequisites or exclusions.

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