Search Naukri.com for job listings in India. Returns job title, company, location, salary range, experience required, and description.
| Name | Required | Description | Default |
|---|---|---|---|
| keywords | Yes | Job title or skills (e.g. "Python developer", "product manager") | |
| location | No | City or region (e.g. "Bangalore", "Mumbai", "Remote") | |
| maxResults | No | Max jobs (default 25) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It adequately states the tool returns specific job fields, which implies a read-only search operation. However, it does not disclose potential rate limits, pagination behavior, or result ordering. A score of 3 reflects adequate but minimal transparency.
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 a single, clear sentence with no wasted words. It effectively conveys the tool's purpose and return data. This level of conciseness is optimal for an AI agent.
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 and no annotations, the description provides a reasonable set of information: it states the source (Naukri.com), geographic scope (India), and returned fields. It does not mention result limits beyond the maxResults default or any API specifics, but it is largely complete for a straightforward search tool. A score of 4 indicates it is slightly above adequate.
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?
The input schema has 100% description coverage, so the baseline is 3. The description adds no additional meaning beyond what the schema already provides for the parameters (keywords, location, maxResults). It does mention return fields, which is helpful context but not parameter-specific. The schema descriptions are sufficient, hence a score of 3.
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 clearly states it searches Naukri.com for job listings in India and lists specific return fields (title, company, location, salary, experience, description). This verb+resource combination effectively distinguishes it from sibling tools like search_linkedin_jobs (LinkedIn) and scrape_ats_jobs (ATS scraping).
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 Indian job searches on Naukri.com but provides no explicit guidance on when to use this tool over alternatives. Sibling tool names are listed in context, but the description itself lacks directives like 'Use this for Indian Naukri jobs; for global jobs use search_linkedin_jobs.' A neutral score of 3 is appropriate as the usage context is implied but not articulated.
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.
