Info
get_api_v1_articles_article_infoGet article by url Group: articles. Billing per call: 1 Credits.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | No |
get_api_v1_articles_article_infoGet article by url Group: articles. Billing per call: 1 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
| url | 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?
No annotations are provided, so the description must disclose behavioral traits. It mentions billing cost ('Billing per call: 1 Credits'), which is useful, but does not indicate read-only nature, rate limits, or any side effects. The description is too brief to fully convey behavior.
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 extremely concise: two short sentences. Every word earns its place. It is front-loaded with the core function, and the billing note is a useful extra. No fluff.
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 tool with a single parameter, no output schema, and no annotations, the description is minimal but might be adequate given simplicity. However, it lacks context on expected response format, error cases, or usage examples. Given the tool's simple nature, a 3 might be arguable, but the lack of any parameter guidelines and behavioral notes makes it incomplete.
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?
The schema has one parameter 'url' with zero description coverage. The description adds minimal value by saying 'Get article by url' and providing an example in the schema, but it does not explain what format the URL should be in, whether it must be a LinkedIn article, or other constraints. Since coverage is 0%, the description should compensate but does not.
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 'Get article by url' clearly states the action and target resource. It distinguishes from siblings like get_api_v1_articles_all (list all) and get_api_v1_articles_article_reactions (reactions), by specifying it's for a single article by URL.
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?
No guidance on when to use this tool versus alternatives. It does not specify that it retrieves a specific article, nor does it mention alternative tools for listing or searching articles. The context is implied but not explicit.
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.
Most tools target distinct resources (profiles, posts, companies, jobs), but there is notable overlap among profile-related endpoints (about, overview, details, full) and company insights vs. employees_data vs. insights. An agent could struggle to pick the right one without reading fine-grained descriptions.
The naming follows a consistent snake_case pattern starting with 'get_api_v1_', making it predictable. However, there are typos ('siilar', 'campany'), mixed terms (lookup vs. search vs. get), and extremely long redundant prefixes that reduce clarity, though the overall style is uniform.
With 50 tools, this is well above the 25-tool threshold, making the surface feel heavy and overwhelming. While the domain is broad (LinkedIn data), many endpoints could be consolidated (e.g., profile about/overview/details/full) to reduce the count without losing functionality.
For a read-only LinkedIn data API, the coverage is quite comprehensive: profiles, posts, companies, jobs, searches, geos, skills, and services are all represented. Obvious gaps are minimal—only a few advanced search filters or batch operations could be missing, but core data retrieval is well covered.