The parser handled the messy resume gracefully, still extracting the main identity, work history, education entries, skills, objective, and hobbies despite non-standard dates and raw dash-style bullets.
What was measured
Messy resume handling
Does it degrade gracefully on poorly formatted input?
decisive for this rankingtransformation
Resumes are often inconsistent or poorly formatted, so graceful handling of messy input is a core capability. (3 of 3 judges)
What was given, what came back
Test input: Messy real-world resume — John Kumar · pdf · group: resume-parsing
Input — what we sent
Messy real-world resume — John Kumar
A poorly formatted, inconsistent real-world resume for John Kumar, used to test robustness against noisy structure, inconsistent dates, and mixed-content sections.
Why this input is hard
- · messy formatting robustness
- · section detection fallback
- · inconsistent date parsing
- · soft-skills extraction
- · noise and hallucination control
Also checked on this input — same tool, 3 other criteria
Accuracy✗ FailedIts total experience calculation overstates tenure: it returned 7.3 years even though the resume explicitly says 3 years of experience.Field coverage◐ MixedIt extracted one certification but silently dropped the AWS Basics Coursera 2022 certificate, so the certification coverage was incomplete on the messy resume.Noise in output✗ FailedThe skills output includes hallucinated taxonomy noise, such as Business Education, that does not appear in the resume.
Provenance
- Observation
- adc87f30-2a28-4fd8-a203-a64e455e74e9
- 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: "affinda",
scenario: "resume-parsing"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 9 other tools
measured on Messy resume handling
Airparser◐ MixedParses a poorly formatted resume, but degrades into non-machine-readable skill output rather than a structured array.CVParserPro✓ WorkedThe messy resume was accepted without errors despite inconsistent formatting, missing section headers, and mixed date styles.Extracta.ai✓ WorkedHandles the noisy, inconsistent layout without crashing and still extracts the main sections.Hireability✓ WorkedOn the poorly formatted resume, the parser still recovered the main structure instead of failing outright, including contact data, experience, education, skills, and hobbies.HrFlow◐ MixedIt parses a poorly formatted resume without crashing, but degrades by dropping soft skills, omitting certifications, and losing most task bullets.LlamaParse✓ WorkedThe parser degraded gracefully on the messy resume, handling missing section headers and mixed date formats while still producing structured output.OpenResume✗ FailedOn a poorly formatted resume without bold uppercase section headers, the parser degrades badly: core sections are misassigned or empty instead of being recovered gracefully.Parseur✓ WorkedThe parser still accepted a highly inconsistent resume with missing headers and mixed date formats, and it continued parsing through the messy structure.Skima AI⚠ StruggledThe tool accepted the messy file, but it degraded badly in structure by merging bullets into a run-on responsibility line instead of preserving clean formatting.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com