Reactions
get_api_v1_profile_reactionsGet all reaction for given profile by URN Group: Profile. Billing per call: 1 Credits.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| urn | No | ||
| cursor | No |
linkdapi-the-best-linkedin-professional-data-api
by com.jojapi
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.8/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full behavioral disclosure burden. It does mention billing cost, but it does not disclose pagination behavior, cursor handling, response shape, authorization needs, rate limits, or whether 'all' reactions are truly returned in one call.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: the first sentence identifies the action, and the second sentence adds cost information. There is no redundant repetition of the tool name or unnecessary filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, no annotations, and an undocumented cursor parameter, so the description is not sufficiently complete. The agent can understand the basic purpose but cannot determine result shape, pagination requirements, or other behavioral constraints.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds some meaning for 'urn' by saying it identifies the profile, but with 0% schema description coverage it completely ignores the 'cursor' parameter. This leaves a significant gap for an agent trying to invoke the tool correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (get all reactions) and the target resource (a profile identified by URN), making a distinction from post/article/comment reaction tools. It is specific enough for an agent to know it retrieves Profile-level reactions, though it does not explicitly contrast it with sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 like post likes, article reactions, or company reactions. The description also does not explain cursor usage or how to handle pagination if multiple calls are needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
C2.7/5.0
Disambiguation3/5
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.
Naming Consistency3/5
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.
Tool Count2/5
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.
Completeness4/5
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.