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

Post Info

get_api_v1_posts_info

Get post info using its URN. Group: Posts. Billing per call: 1 Credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urnNo

Schema Changelog

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

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the behavioral burden. It indicates a read-only retrieval via "Get" and notes billing, but it does not disclose output contents, error conditions, authentication requirements, or rate limits. This is a minimally acceptable disclosure for a simple get-by-URN tool.

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 and front-loaded with the core action: "Get post info using its URN". The additional metadata threads (Group, Billing) are short and non-redundant. Every sentence earns its place.

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

Completeness3/5

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

Given a very simple 1-parameter tool with no annotations and no output schema, the description is minimally complete: it states the source, input, and cost. However, it lacks any guidance about expected output shape, where to get the URN, or distinctions from sibling post endpoints, making it not fully complete.

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?

Schema description coverage is 0%, and the description only says "using its URN". It adds no meaning beyond the already-named parameter `urn`; it does not explain what a URN is, how to obtain it, or whether it refers to a LinkedIn post URN. A basic example existed in the schema but no semantic enrichment.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

"Get post info using its URN" clearly states a specific action with a specific input, distinguishing this from sibling post-related tools like get_api_v1_posts_all or get_api_v1_posts_comments. The scope is precise: retrieving one post's information by URN.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage: when you have a URN of a post and need its info. There is no explicit when-to-use vs alternatives or exclusions, though it is clear enough for the narrow purpose. It does not state that this tool should not be used for comments, likes, or all posts.

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.

Resources