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

Services

get_api_v1_search_services

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
startNo
geoUrnNo
keywordNo
profileLanguageNoen for English, ch for Chinese, etc...
serviceCategoryNoservices ID

Schema Changelog

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

  1. First observed

TDQS

C2.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full disclosure burden. It discloses billing cost ('Billing per call: 1 Credits'), which is genuinely useful operational context for agents planning multi-call workflows. However, it's silent on rate limits, default behavior with no arguments, output expectations, and error semantics — a moderate gap for a search endpoint.

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

Conciseness4/5

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

The description is tightly written at under 20 words with the core purpose front-loaded. Every phrase earns its place, though 'Biling' is a typo and the metadata-style format ('Group:', 'Billing per call:') reads like auto-generated docs rather than intentional structure.

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?

For a tool with 5 parameters, no annotations, and no output schema, this description is inadequate. It fails to explain result semantics, pagination, ordering, or how this search differs from the four other search_* siblings. An agent must guess at filter behavior and expected results, which is a significant gap for a search tool that could benefit from richer context.

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 coverage is only 40%, so the description should compensate for the undocumented params (start, geoUrn, keyword). The phrase 'all filters available' weakly signals that parameters are optional (consistent with 0 required params), but it adds no guidance on formats, combinations, or which params to prefer. This is below the baseline 3 for low schema coverage.

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?

'search services' is a clear verb+object statement of the tool's core function, and '(all filters available)' clarifies that filtering is optional, which is a useful scope signal. However, it doesn't distinguish itself from sibling search tools (search_people, search_companies, search_posts) beyond the 'services' subject, and the description largely restates the tool name.

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?

The only contextual marker is 'Group: Search', which is an implicit category label rather than actionable guidance. There's no mention of when to prefer this over sibling tools like search_people or search_companies, and no exclusions or workflow context provided.

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