It does not fully cover the baseline fields on a standard resume: Name and Phone remain unpopulated, even though email, experience, education, and skills are extracted.
What was measured
Field coverage
Are name, email, phone, experience, education, and skills extracted?
decisive for this rankingtransformation
A resume parser should extract the key canonical fields; missing them means it is not doing the main job well. (3 of 3 judges)
What was given, what came back
Test input: Clean single-column resume — Rugved Nichite · pdf · group: resume-parsing
Input — what we sent
Clean single-column resume — Rugved Nichite
A professionally structured single-column resume for Rugved Nichite, used as the baseline input for parser accuracy across standard resume fields.
Why this input is hard
- · baseline field extraction
- · contact info accuracy
- · work experience parsing
- · education and CGPA extraction
- · skills and certifications extraction
Output — unretouched
Also checked on this input — same tool, 4 other criteria
Accuracy✗ FailedFails to populate Phone for an Indian-format number, leaving the field blank despite the number being present in the resume text.Accuracy✗ FailedFails to extract the candidate name even when it is present in the source; the Name field is left empty.Accuracy✗ FailedMisplaces GPA/CGPA into Date and leaves GPA blank, so academic data is routed to the wrong field.Output quality◐ MixedOn a clean single-column resume, the output is mostly usable but still partial because the Name field is blank and Phone/Location are missing.
Provenance
- Observation
- 64985f7c-5468-4d7f-bf98-820b763118d2
- Evidence run
- cbbef4db-964c-49fa-a57f-a2977822bdfc
- Study
- Parse resumes into structured data using an API
- Research task
- 86b9jm30n
- Tested at
- not recorded
- Source
- first-party
- Evidence state
- verified
- Proof shown
- input + output shown
- Cost / latency
- not captured
- Repeat run
- not captured
- Tester
- not captured
The last three rows are honest blanks, not placeholders — our capture has no field for them yet.
Query this
get_evidence({
tool: "openresume",
scenario: "resume-parsing"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 9 other tools
measured on Field coverage
Affinda✓ WorkedOn the clean resume, the parser covered the standard resume sections expected by the benchmark: identity/contact, work experience, education, skills, and certifications.Airparser✓ WorkedCovers the baseline resume fields end to end: name, email, phone, work experience, education, and skills.CVParserPro✓ WorkedOn the baseline resume, CVParserPro extracted the core benchmark field set: name, email, phone, total experience, education, and skills; the report also says both certifications were captured.Extracta.ai✓ WorkedCovers the standard resume fields needed for the baseline test: name, email, phone, experience, education, and skills are all present.Hireability✓ WorkedThe clean resume returned the core resume sections expected for benchmarking: name, email, phone, experience, education, and skills were all present, along with location, LinkedIn, summary, certifications, and competencies.HrFlow✓ WorkedOn the clean resume, it extracted the core resume fields: name, email, phone, work experience, education, and skills.LlamaParse✓ WorkedThe clean output included the core resume fields name, email, phone, work experience, education, and skills.Parseur✓ WorkedOn the baseline resume, the parser populated the core resume fields: name, email, phone, work experience, education, and skills.Skima AI✓ WorkedOn the clean resume, the parser returned the six core fields expected for resume coverage: name, email, phone, work experience, education, and skills.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com