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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds that it returns the full Markdown content and canonical link, which is useful behavioral context beyond the annotations. It doesn't contradict them.

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?

Two sentences with no fluff. The primary action and output are front-loaded, and the input guidance is concise. Every word earns its place.

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 simple single-parameter read tool with comprehensive annotations, the description covers the return content and input source. No output schema exists, so explaining the return value is necessary and done. Nothing critical is missing.

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 0%, so the description must compensate. It explains that the slug comes from list_articles or search_articles, giving the agent a clear source for the parameter value, which adds meaning beyond the bare schema.

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 action (read), the resource (article), and the specific outputs (full Markdown content and canonical link). It distinguishes itself from siblings by focusing on retrieving a single article's content rather than listing or searching.

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 tells the agent to supply the slug returned by list_articles or search_articles, effectively instructing when to use this tool (after obtaining a slug). It doesn't explicitly state when not to use it, but the context is clear enough.

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

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

Annotations already declare readOnly, idempotent, open world, and non-destructive behavior. The description adds meaningful behavioral details such as newest-first ordering, case-insensitive exact tag filtering, and that only metadata and links are returned.

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 two sentences, front-loads the core purpose, and every clause adds useful information: source, order, return type, sibling tool, and filter semantics. No redundant text.

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 simple list tool with three parameters and no output schema, the description covers the essential behavior, return type, ordering, and filtering. The limit/offset pagination aspects are adequately represented by the input schema, so no critical context is missing.

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 0%, so the description must compensate. It adds meaning to the tag parameter by specifying it is optional, case-insensitive, and exact-match, but it does not describe limit and offset. Those are fairly inferable from their names and schema constraints, though not explicitly explained.

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 it lists published articles from a specific blog, newest first, and notes it returns metadata and links. It also differentiates itself from get_article, making its purpose distinct from a sibling tool.

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 clear context: use list_articles to browse article metadata, and use get_article to read a full post. It does not explicitly contrast with search_articles, but the usage context is still clear enough for correct selection.

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

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

Annotations already signal a safe read-only, idempotent operation. The description adds genuinely useful behavior beyond annotations: AND-matching ('all words must match'), ranking ('title and tag matches rank higher'), and result shape ('summaries and links, not full articles'). No stated behavior conflicts with the hints.

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?

Two dense sentences front-load the core capability and return contract before adding matching semantics. There is no filler or repeated schema/annotation content.

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?

The description covers search scope, matching behavior, ranking, and the return value contract, which is enough given there is no output schema. Pagination is not described verbally, but limit and offset are self-explanatory and defined in the schema.

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 0%, but the description explains the query parameter's semantics in context: keywords match titles, tags, and content, with all words required and title/tag results ranked higher. limit and offset are standard pagination parameters whose constraints are already visible in the schema, so the description provides sufficient added meaning.

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 names a specific action ('Search'), a clear resource ('published blog articles'), and the searchable fields (titles, tags, content). It also states what the tool returns, which distinguishes it from list_articles and get_article.

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?

It establishes the intended use case—keyword search returning summaries and links—and the explicit 'not full articles' clause steers an agent away from using it when full content is required. It does not explicitly name get_article or list_articles as alternatives, so there is clear context but no exclusion-based routing.

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