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An MCP server that provides tools to fetch job recommendations from LinkedIn and Naukri job boards based on search keywords
Both tools have minimal descriptions (under 20 chars penalty applies) and critically underdescribed parameters. Input schemas exist but lack parameter descriptions entirely. No output schema documentation. Parameter 'list_of_keywords' is ambiguous (string vs array?). No error handling guidance. Tools lack verb-noun naming clarity (fetch_linkedin vs search_linkedin_jobs). No pagination, no result limits documented. Fails 80% of definition quality baselines.
Fetches LinkedIn jobs based on a list of keywords
Fetches Naukri jobs based on a list of keywords
Parameter descriptions completely missing. 'list_of_keywords' has no description explaining format (comma-separated string? array? single keyword?), expected length, or examples.
Tool descriptions under 20 characters ('Fetches LinkedIn jobs based on a list of keywords' is 56 chars but lacks WHEN to use, WHAT it returns, and prerequisites). Insufficient for LLM tool selection.
No output schema documented. LLMs cannot plan downstream calls or extract required fields (job title, company, location, link). Baseline: 100% of A+ tools document return types.
No pagination, result limits, or total count returned. Job APIs can return hundreds of results; no guidance on max results or how to iterate. Violates paginated-result pattern.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 32 | 2026-07-28+ | v2 |
Parameter type ambiguity: 'list_of_keywords' is typed as string but name suggests array. Actual implementation in job_api.py passes it directly to Apify as a string, but LLMs will be confused whether to pass 'python,java' or ['python','java'].