Profile interests
get_api_v1_profile_interestsGet Interests for given profile by URN Group: Profile. Billing per call: 1 Credits.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| urn | No | profile URN |
get_api_v1_profile_interestsGet Interests for given profile by URN Group: Profile. Billing per call: 1 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
| urn | No | profile URN |
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?
The only behavioral trait disclosed is the cost ('Billing per call: 1 Credits'). No other behaviors are mentioned, such as authentication requirements, read-only nature, rate limits, or side effects. With no annotations, the description carries the burden but falls short.
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 concise, consisting of one clear sentence plus a billing note. It avoids unnecessary details and is well-structured, making it easy to parse.
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 does not explain what the response contains beyond 'Interests', lacking details about format, structure, or possible errors. Since there is no output schema, this omission leaves the tool's expected return ambiguous, reducing its overall completeness.
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 single parameter 'urn' is clearly described in both the schema and the description as 'profile URN'. The schema provides full coverage with an example and type. The description reinforces its purpose, leaving no ambiguity.
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 that the tool fetches interests for a given profile identified by URN. It uses a specific verb ('Get') and resource ('Interests') and distinguishes itself from other sibling tools that handle different entities (e.g., articles, companies, posts).
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 does not provide explicit guidance on when to use this tool versus alternatives. It only states what it does and includes a billing note, without mentioning use cases, prerequisites, or comparison to other profile-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.