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

Annotations already mark the tool read-only, open-world, idempotent, and non-destructive. The description adds substantial behavioral context beyond those flags: it identifies the SPARQL endpoint, states result ordering (most recent first), lists exactly what each result contains, and discloses open access with no auth required. Minor caveats like rate limits or pagination are not mentioned, but the core operational behavior is well disclosed.

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 information-dense but tightly structured: core search action and scope first, then returned fields, examples, ordering, date filter, identifier format, and data-source/access notes. Every sentence earns its place; there is no filler or restatement of the title.

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 read-only keyword search tool with a full input schema and an output schema, the description is complete: it covers data source, search scope, result contents, ordering, optional date filtering, identifier conventions, and authentication. The agent has everything needed to select and call the tool correctly.

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 coverage is 100%, so the baseline is 3; the description adds extra semantic value by giving real keyword examples, explaining the CELEX identifier format, and describing how from_date filters results. This helps the agent pick valid values even beyond the schema descriptions.

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 opens with a specific verb and resource ('Search the official EUR-Lex Cellar SPARQL endpoint') and immediately scopes the operation to keyword matching in English titles. It names the returned artifact types (regulations, directives, decisions) and the CELEX identifiers, making the tool's purpose unambiguous and easy to distinguish from sibling tools like eurlex.legislation.recent or by_type.

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 intended invocation context is clear: use this when searching EU legislation by English-title keyword, optionally constrained by from_date. Concrete examples ('artificial intelligence' → AI Act, 'GDPR' → GDPR) reinforce when the tool fits. It does not explicitly say when to prefer eurlex.legislation.detail/recent/by_type, so it stops short of full guidance.

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