get_articleFetch the full clean text of one article by its slug (the last path segment of an article URL).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Article slug, e.g. "my-article-title" |
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| date | No | ||
| slug | Yes | ||
| text | No | ||
| title | Yes | ||
| summary | No |
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 establish readOnly, idempotent, and non-destructive behavior. The description adds useful behavioral context by specifying the output is 'full clean text' and explaining how a slug maps to a URL segment, going beyond the structured annotations.
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?
A single, focused sentence that leads with the core behavior and immediately clarifies the identifier format. No filler or redundant restating of the tool name.
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-only tool with annotations and an output schema, the description covers what the tool does and how the parameter is derived. Nothing essential is missing for an agent to invoke it 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% and the schema already gives an example. The description supplements this by explicitly defining a slug as 'the last path segment of an article URL', which clarifies the expected format and makes correct invocation easier.
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?
Describes a specific action ('Fetch the full clean text of one article') on a specific resource identified by slug. The phrase 'full clean text' distinguishes it from generic URL fetching and from list/search siblings that return multiple or partial results.
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 the tool is for retrieving one article when you already have its slug, and the sibling names suggest alternatives like listing or searching. However, it does not explicitly state when not to use this tool or name an alternative, leaving usage partially to inference.
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.
Search and search_articles overlap significantly, both retrieving article matches with slightly different result formats. Fetch and get_article also both return full article text but are distinguished by id versus slug. Descriptions help resolve these overlaps, but an agent could still pick the wrong tool without close attention.
Most tools follow a snake_case verb_noun pattern like get_article and search_articles, but fetch and search are bare verbs, and popular_content is a noun phrase with no verb. The pattern is readable but not fully consistent.
Six tools is a well-scoped set for a publication content gateway: search, retrieval by two identifier types, recent listing, popular content, and article search. Each tool serves a distinct practical need without bloat.
The core content access workflows are covered: search, retrieve full text by search id, retrieve by slug, list recent, and list popular. Minor gaps exist, such as no pagination or filtering, and search_articles results don't explicitly feed into get_article or fetch, but agents can typically work around these.