Search LinkedIn companies by keyword. Returns company name, description, followers, and logo.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number, 1-100 (default: 1). 10 results per page. | |
| query | Yes | Search keyword (max 500 characters) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full behavioral disclosure burden. It mentions return fields (name, description, followers, logo) but omits key behavioral traits such as whether the operation is read-only, authentication requirements, rate limits, or pagination specifics beyond the schema. The word 'search' implies read-only, but no explicit safety guarantees are provided.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise yet informative sentences. It front-loads the action and return summary without any filler or redundant details, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-param search tool, the description is minimally adequate: it states what is searched, how (by keyword), and what is returned. However, it lacks usage guidance and behavioral transparency, which leaves gaps in selection confidence and invocation safety. No output schema exists, so the return field enumeration is helpful but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both params (query and page) already well-documented in the input schema. The description adds minimal value for parameter understanding, only indirectly reinforcing the role of 'query' as a keyword. Baseline 3 applies due to complete schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Search') and resource ('LinkedIn companies'), with the keyword-based scope clearly stated. It is easily distinguished from sibling tools like search_linkedin (general) and search_linkedin_jobs (jobs), and its functionality is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for keyword-based company searches but provides no explicit guidance on when to prefer this tool over alternatives like search_linkedin or linkedin_company_details. No when-not-to-use conditions or alternative suggestions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly scoped by platform and resource (e.g. search_twitter vs twitter_user_tweets vs twitter_tweet_details). A few pairs like twitter_tweet_comments vs twitter_user_replies or facebook_page_posts vs search_facebook_posts could cause minor confusion, but descriptions generally clarify the distinction.
The dominant pattern is snake_case with a platform_prefix_resource suffix, and search_* consistently marks search operations. Minor deviations include noun-style names like amazon_best_sellers and place_photos, and the odd get_ skill/comments tools, but the overall convention is predictable.
74 tools is far beyond the typical well-scoped MCP server, even for a multi-platform API aggregator. The breadth is justified by the many platforms covered, but an agent will face a very large action space, and this could reasonably be split into per-platform servers.
The server provides strong lifecycle coverage for its read-only domain: search, profile/details, posts, and engagement data across most platforms. Gaps exist for some platforms (e.g. no LinkedIn person profile, no Facebook event details, no Truth Social profile/search, no Reddit subreddit-specific tools), but the core workflows are well covered.
