Featured Posts
get_api_v1_posts_featuredGet all featured posts for a given profile using its URN. Group: Posts. Billing per call: 1 Credits.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| urn | No |
get_api_v1_posts_featuredGet all featured posts for a given profile using its URN. Group: Posts. Billing per call: 1 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
| urn | 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?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It mentions billing (1 Credit) but lacks details on pagination, error handling, or the nature of 'featured' posts. The read-only nature is implied by 'Get,' but not explicitly stated, and there is no discussion of potential side effects or limits.
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 a single, focused sentence immediately followed by relevant metadata (Group and Billing). It is front-loaded, contains no fluff, and every part serves a purpose.
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 tool is simple with one parameter, and the description covers the core purpose. However, there is no output schema or mention of response format, pagination, or what 'featured' means in this context. For a minimal GET endpoint, it is adequate but leaves some ambiguity for the agent.
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 0% description coverage for the 'urn' parameter, but the tool description clarifies that the URN belongs to a profile. This adds some meaning beyond the plain schema, though it does not elaborate on format, constraints, or how to obtain the URN. Given the low coverage, this slight compensation earns a middle score.
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 'Get all featured posts for a given profile using its URN,' specifying the verb, resource (featured posts), and scope (profile URN). It distinguishes from siblings like get_api_v1_posts_all by explicitly mentioning 'featured,' making the purpose unambiguous.
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 provides no guidance on when to use this tool versus alternatives like get_api_v1_posts_all or get_api_v1_posts_info. It merely states the function without mentioning exclusions, prerequisites, or context for selection among the many post-related tools.
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.