get_articleGet the direct answer, the causes the article covers, and its FAQ for one article, by slug (from search_articles). Returns a summary, not the full text — the article URL has the complete step-by-step fix and the sources.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | No | Article slug, e.g. "why-is-my-phone-on-sos". | |
| query | No | Optional. If slug is unknown or wrong, the closest match to this text is returned instead. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds behavioral context: it returns a summary, not full text, and that the article URL contains the complete step-by-step fix and sources. No contradictions.
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 that are front-loaded and concise, with no unnecessary words. 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?
For a simple tool with two parameters and no output schema, the description fully explains what is returned and what is not. It also points to the article URL for more details, making it complete.
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 baseline is 3. The description adds that the slug is from search_articles and that the query is a fallback, but does not significantly enhance 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 clearly states it retrieves the direct answer, causes, and FAQ for a single article by slug. It distinguishes from siblings by specifying that it returns a summary, not the full text, and that the slug comes from search_articles.
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 explicitly mentions that the slug should come from search_articles and that an optional query can be used if the slug is unknown. It does not directly state when not to use it, but the context is sufficient for an agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource: search_articles finds articles, get_article retrieves a specific one, list_topics exposes categories, and list_calculators handles calculators. Dependencies are explicit (slug from search_articles, topic from list_topics), so an agent can select confidently.
All four tools follow a consistent snake_case verb_noun pattern using imperative verbs (get, list, list, search) followed by plural nouns. No stylistic deviations or vague names.
Four tools is appropriate for a content-site server: two article operations, one topic index, and one calculator index. Every tool serves a distinct user journey and none feel redundant.
The server covers the core discover-and-retrieve workflow: browse topics, search articles, fetch article details, and find calculators. Since calculators are client-side interactive tools needing only a URL, listing them is sufficient, and returning article URLs for full fixes is a deliberate design rather than a gap.