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

Annotations already mark this as read-only, idempotent, and non-destructive; the description adds substantial useful behavior: semicolon-delimited CSV or SDMX-ML XML parsing, variable column names, substring matching, the 1-500 row cap/default, rejection of >5MB distributions with a fallback pointer, and no-auth access. This goes well beyond the annotation safety profile.

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 dense but every sentence earns its place: it covers source IDs, format normalization, header discovery, filtering, row limits, large-file behavior, and authentication in a compact block. Key operational guidance is front-loaded and no filler is present.

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?

For a data-fetch tool with an output schema, the description is complete: it explains the required upstream lookup, format variance, how to discover column names, how to filter, limit behavior, error handling for large distributions, and authentication status. An agent has enough context to call this tool correctly without outside knowledge.

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 six parameters thoroughly, including defaults, constraints, and interdependence between filter_column and filter_value. The description reinforces the workflow but adds little beyond what the parameter descriptions already provide, so the baseline 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 states a specific verb and resource: 'Fetch rows from one KSH dataset distribution', identifies the required IDs and their source, and clarifies the parsed output shape. It is clearly distinguishable from the sibling metadata/search tools (hungary-ksh.reference.dataset_metadata, hungary-ksh.reference.dataset_search) by centering on distribution-level row retrieval.

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 gives a clear workflow: obtain IDs from hungary-ksh.dataset_metadata, call once without filter_column to inspect the header, then filter by column/value. It does not explicitly say 'use X instead of Y', but it provides strong contextual guidance by pointing to the prerequisite metadata tool and describing the filter-first exploration pattern.

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