Polish Academic MCP

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the data source (Polish government portal), dataset count (43,000+), academic flag (has_research_data=true), and return format (JSON with specific fields like title, category, license, institution, download stats). However, it lacks details on rate limits, authentication needs, or error handling.

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 essential information in two efficient sentences. The first sentence covers purpose and scope, while the second details return format and academic data flag. There is no wasted text, and every sentence adds value.

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 (search with 5 parameters, no output schema, and no annotations), the description is mostly complete. It covers the data source, scope, academic flag, and return format. However, it lacks details on pagination behavior (implied by parameters but not explained), error cases, or example usage, which would enhance completeness.

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 already documents all parameters thoroughly. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain query syntax or category mappings). The baseline score of 3 is appropriate as the schema handles the heavy lifting.

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 specific action ('Search'), the target resource ('Polish government open data portal (dane.gov.pl)'), and distinguishes it from siblings by specifying the portal's scope (43,000+ datasets from ministries, municipalities, and public institutions). It also mentions the academic dataset flag, which further differentiates it from generic search 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 implies usage context by mentioning the portal's scope and academic datasets, but it does not explicitly state when to use this tool versus alternatives like 'dane_get_dataset' or other search tools in the sibling list. No exclusions or prerequisites are provided.

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