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

Schools

get_api_v1_search_schools

search schools (all filters available) Group: Search. Billing per call: 1 Credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
startNo
keywordNo

Schema Changelog

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

  1. First observed

TDQS

C2.2/5.0
Behavior1/5

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

With no annotations, the description carries the burden of behavioral disclosure. It only states 'search schools' and billing but doesn't indicate whether it's read-only, pagination behavior, rate limits, or what the response contains. It also doesn't explain what 'all filters available' means operationally. This is a significant gap for a tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short (two sentences), which is concise, but it doesn't front-load critical information. It merges purpose with billing and group info, and the structure is not well-organized. It's not verbose, but it's under-specified, so it doesn't fully earn its place. It's acceptable in length but lacks substance.

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

Completeness1/5

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

Given the tool's simplicity (2 params, no output schema, no annotations), the description is severely incomplete. It fails to explain the parameters, the response format, any behavioral aspects, or even what 'schools' means in this context. A minimal search tool should at least describe the parameters and typical output. The description is far from sufficient.

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

Parameters1/5

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

Schema description coverage is 0%, so the description must compensate. It doesn't explain the 'start' and 'keyword' parameters at all. The phrase 'all filters available' is vague and doesn't map to the actual parameters. The description adds no semantic meaning beyond the schema's example values.

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 (search) and resource (schools), distinguishing it from sibling search tools for people, companies, posts, and services. The title 'Schools' is vague, but the 'search schools' phrase provides a specific verb+resource. However, it doesn't specify the scope or any limitations, so it's not a 5.

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 given on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or comparisons with other search tools. The 'Group: Search' and billing info are metadata but not usage directives. The only context is the resource type, which implies use for school searches.

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