About
get_api_v1_profile_aboutabout this profile such last update and verification info Group: Profile. Billing per call: 1 Credits.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| urn | No |
get_api_v1_profile_aboutabout this profile such last update and verification info Group: Profile. 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only names the kind of data (last update, verification info) and notes billing, but does not disclose side effects, required permissions, potential errors, readonly nature, or the response shape. This is insufficient for a tool whose endpoint group has many similar read operations.
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 short, but it wastes tokens on 'Group: Profile' and the billing line, which do not help the agent. The useful part is limited to 'about this profile such last update and verification info'. It is under-specified rather than efficiently structured.
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?
With one parameter, no output schema, and no annotations, the description must be richer to be useful. It provides only a partial content overview and omits return values, error conditions, and parameter meaning. The tool name suggests it is a read operation, but the description is too thin to give the AI agent enough context to confidently select and use the tool.
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 defines one parameter, 'urn', with an example but no explanation, and schema description coverage is 0%. The description does not mention the urn parameter at all or explain how to identify the target profile. It fails to compensate for the missing schema documentation, leaving the agent without semantic guidance for this required input.
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 states the resource ('this profile') and specific content ('about this profile such last update and verification info'), which clarifies it returns 'about' data for a profile. The endpoint name and title reinforce the 'get' semantics, but the description itself lacks an explicit verb and does little to distinguish this from the many other profile_* siblings beyond the word 'about'.
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?
There is no guidance on when to use this tool versus alternatives such as get_api_v1_profile_overview or get_api_v1_profile_full. The 'Group: Profile' and billing note are not usage guidelines; they neither describe prerequisites nor mention exclusions.
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.