Fetch full LinkedIn profile details by profile URL: experience, education, skills, headline, and location. No login or cookies required.
| Name | Required | Description | Default |
|---|---|---|---|
| profileUrls | Yes | LinkedIn profile URLs (e.g. ["https://www.linkedin.com/in/satyanadella"]) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavior. It mentions 'no login or cookies required' indicating ease of access. However, it does not address rate limits, error handling for invalid URLs, data freshness, or whether only public data is returned. Some gaps exist.
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?
Single concise sentence front-loads the action and resource, then lists key return fields and the benefit of no login. Every part adds value; no wasted words. Could be slightly more structured, but very efficient.
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?
Given no output schema, description lists some returned fields (experience, education, skills, headline, location) but does not explain full structure, handling of multiple URLs, or error scenarios. Basic completeness for a simple fetch tool, but could improve.
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?
Input schema has 100% description coverage for the single parameter 'profileUrls' with an example format. The tool description does not add much beyond the schema; it repeats the idea of fetching by URL but does not elaborate on format variations or batch behavior. Baseline 3 due to high 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?
Description clearly states 'Fetch full LinkedIn profile details by profile URL' and lists specific fields (experience, education, skills, headline, location). Distinguishes from sibling tools like get_linkedin_company and search_linkedin_employees by focusing on individual profile URLs.
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?
Implies use case (fetching profile details) and mentions no login/cookies, but does not explicitly state when to use this tool over alternatives or when not to use it (e.g., for company profiles or searching for candidates). Lacks explicit guidance.
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 target unique data sources or specific actions (e.g., search_zillow vs. get_zillow_property_details are clearly sequential). A few LinkedIn-related tools (find_linkedin_candidates vs. search_linkedin_employees) have overlapping purposes but their descriptions clarify distinct use cases. Overall, confusion is minimal and descriptions resolve ambiguity.
All tools follow a consistent verb_noun pattern in snake_case, using verbs like search, get, find, scrape, analyze, lookup, resolve, and verify. The pattern is predictable across the entire set, making it easy for an agent to infer function from name.
With 32 tools, the server exceeds the 'too many' threshold of 25+. While the broad scope of web data mining justifies some diversity, the count is unwieldy and could overwhelm an agent's selection process. A smaller, more focused set per domain would improve coherence.
The toolset covers a wide range of data retrieval needs: company research, real estate, job listings, academic research, and government records. For a read-only data aggregation service, there are no major lifecycle gaps, though some subdomains like social media scraping only cover Reddit and LinkedIn, missing other platforms. Overall, it is reasonably complete for its stated purpose.
