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linkdapi-the-best-linkedin-professional-data-api

LinkdAPI status

get_status

status of LinkdAPI service Group: system. Billing per call: 1 Credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It only mentions 'Group: system' and 'Billing per call: 1 Credits', but gives no details about what the response contains, whether it requires authentication, or any side effects. This is insufficient for a mutation-free status check; agents need to know if it returns a simple health indicator or detailed metrics.

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 extremely concise, consisting of one short sentence plus metadata. No fluff or redundancy. The structure is front-loaded with the core purpose, followed by billing info. It earns a perfect score for efficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple zero-parameter status check tool, the description is almost sufficient. It reveals the tool's purpose and even mentions billing cost. Without an output schema, it could name the response format (e.g., JSON with state, uptime), but for a system status endpoint, the description is adequate. A score of 4 reflects that it covers the essentials, though a bit more detail on what 'status' includes would be nice.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are zero parameters to describe, so the description has no obligation to explain input semantics. The schema is empty, so no additional info is needed. Baseline is 4.

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 states 'status of LinkdAPI service' which clearly indicates this tool provides the API's operational status. It distinguishes itself from sibling tools that are all data retrieval endpoints by focusing on service health rather than domain data. However, it lacks a strong verb like 'check' or 'retrieve', making it slightly less explicit.

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?

No guidance is provided on when to use this tool versus alternatives. It doesn't mention that it's for health checks, pre-requisites, or that it should be called before other API operations. The description offers no exclusions or alternatives, leaving the agent to infer usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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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.

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