Katzilla MCP

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

Annotations already cover read-only, non-destructive, idempotent, and open-world hints. The description adds valuable context beyond annotations: it specifies the source (Bank of Canada), update frequency (daily), return format (Katzilla envelope with data, quality, citation), and details on quality scoring and citation contents (URL, license, SHA-256 hash). This enhances transparency 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 front-loaded with the core purpose, followed by source, update frequency, and return format details. Every sentence adds value: the first defines the tool, the second specifies source and updates, and the third explains the return structure. No wasted words, making it efficient and well-structured.

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 (financial data retrieval), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (implied by the return format description), the description is complete. It covers purpose, source, update behavior, and output structure, compensating for any gaps. No additional explanation is needed for basic usage.

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%, so the schema fully documents parameters (series, startDate, endDate). The description does not add parameter-specific semantics beyond what the schema provides, such as explaining series naming conventions or date format implications. It mentions common series examples, but these are already listed in the schema's description for the series parameter.

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: retrieving 'interest rates and exchange rates from the Bank of Canada,' with specific examples (policy interest rate, prime rate, government bond yields, CAD exchange rates). It clearly distinguishes from siblings by focusing on Bank of Canada data, unlike other economic tools (e.g., economic__ecb-rates for European Central Bank or economic__exchange-rates for general exchange rates).

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 it: for Bank of Canada interest and exchange rate data, updated daily. It implies alternatives by specifying the source, but does not explicitly name when not to use it or list specific sibling alternatives (e.g., economic__ecb-rates for European data).

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