It correctly extracts the core personal and work-history values and the dedicated spoken-languages list, but leaves education marks/CGPA embedded in the education description string and keeps certification names in lowercase exactly as written instead of normalizing them.
What was measured
Accuracy
Are extracted values correct and complete?
decisive for this rankingtransformation
Correct and complete values are the essence of resume parsing, so this directly determines whether the tool succeeds. (3 of 3 judges)
What was given, what came back
Input — what we sent
No input — this is a capability finding
The observation is about the tool itself rather than one test input, so there is nothing to show on this side by design.
Output — unretouched
No output artifact
The verdict rests on the tester's written observation alone — no file was captured for this cell.
Provenance
- Observation
- 9adf5e09-d701-4a1f-a976-629d1e5b409e
- Evidence run
- cbbef4db-964c-49fa-a57f-a2977822bdfc
- Study
- Parse resumes into structured data using an API
- Research task
- 86b9jm30n
- Tested at
- not recorded
- Source
- aggregate-synthesis
- Evidence state
- observed
- Proof shown
- no artifact
- 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: "extracta-labs"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 0 other tools
measured on Accuracy
No other tool was measured on this criterion for this input.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com