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

Posts

get_api_v1_search_posts

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

Input Schema

TableJSON Schema

Name	Required	Description
`start`	No
`sortBy`	No	either "relevance" or "date_posted"
`keyword`	No
`datePosted`	No	can be "past-24h" "past-week" "past-month" "past-year"
`fromMember`	No	profile URN
`contentType`	No	can be "videos" or "photos" or "jobs" or "liveVideos" or "documents" or "collaborativeArticles"
`authorCompany`	No	Company ID
`authorIndustry`	No	industries ID
`authorJobTitle`	No	can be any title
`mentionsMember`	No	profiles URN e.g "ACoAAAAKXBwBikfbNJww68eYvcu2dqDYJhHbp4g"
`fromOrganization`	No	Companies ID e.g "1337,1441"
`mentionsOrganization`	No	Companies ID e.g "1337,1441"

Schema Changelog

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

First observedAug 19, 2026

TDQS

C2.9/5.0

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 behavioral burden. It only adds a billing note and the fact that all filters are available. It does not disclose result types, pagination/offset behavior, default sorting, error cases, or any operational constraints.

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 short and front-loaded with the core purpose: 'search Posts (all filters available)'. The 'Group' and 'Billing' details are concise and useful metadata. There is no fluff, though it sacrifices helpful detail for brevity.

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 12-parameter search endpoint with no output schema and no annotations, the description is incomplete. It does not explain pagination via 'start', expected response structure, default sort behavior, or any operational caveats. The schema covers parameters, but the description lacks broader operational context.

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

Parameters3/5

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

Schema description coverage is 83%, and the schema already documents parameter meanings with examples, so the description does not need to repeat them. The description adds little beyond the general notion of 'all filters' but does not need to compensate given rich 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?

The description clearly states the tool 'searches Posts,' identifying the specific verb/resource. It distinguishes from similar search endpoints for companies/people/schools/services. The phrase '(all filters available)' is vague but the action and resource are unambiguous.

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 get_api_v1_posts_all or other search endpoints. The description implies usage through 'search Posts' but provides no context, exclusions, or comparative instructions.

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

All linkdapi-the-best-linkedin-professional-data-api tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

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.

Resources

Need Help?