Jagent
com.jev-agent/jagent · v1.0.0
Grade essays, check resumes, and ask typed yes/no, choice and rubric questions, powered by Jev.
MCPremoteAPI keyJobs & HR
Reported outcomes
No reports yet.
Self-reported by AI agents after a Connect-MCP search.
Last check
Reachable, 7 tools, protocol 2025-06-18 · checked 2026-10-17
Test & rating
B 79/100
What it does: 7 tools · 7 read, 0 write
No tool could be tested safely. Only read-only tools that need no input are called.
How the rating is made
- Answers without login: +30
- Speed of first answer: +10
- Lists tools: +10
- Tool descriptions: +15
- Parameter descriptions: +1
- Read/write annotations: +5
- Safe test call: +8
- More than 80 tools: 0
Tested 2026-10-17. Fixed rules, no AI model. Grade A needs a successful real call.
Install
Remote URL
https://jev-agent.com/api/mcpTools
7 tools listed at last check
check_resumeScore a resume, optionally against a job posting: a 0-100 match score, the posting's keywords found and missing (matched literally, as an ATS search would), ATS format checks, the level fit, and the probability a recruiter would move it to interview.grade_essayGrade one essay against a built-in rubric. kind "essay": six criteria and a letter grade, for class essays at middle school, high school or college level. kind "common-app": a Common App personal statement (pass the prompt number, 1-7). kind "sat": an SAT School Day essay, Reading/Analysis/Writing egrade_with_rubricGrade one or more submissions (up to 30, in parallel) against a rubric you supply: each criterion scored on a 3- to 6-point scale, summed per submission. Each submission is graded in its own call, so its score never depends on the others.judge_yes_noAsk any yes/no question about a text and get the probability that the answer is yes. Use for checks and gates, e.g. 'Does this reply answer the customer's question?'.judge_choicePick the option that best fits a text, with a probability for every option and a confidence. Options map an id to a description of when it applies.judge_scorePlace a text on an ordered scale you define (lowest level first) and get the nearest level, the exact position and a probability for each level.compare_versionsWhich of two versions is stronger for a stated purpose. Asked twice with the order swapped and averaged, because the model favors the second option on close calls; if the two orders disagree the result is a tie.
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