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Resume Data Extraction

extract_resume_data
Read-onlyIdempotent

Extracts and converts resumes, CVs, and candidate documents into structured, searchable profiles with contact details, skills, experience, education, certifications, and taxonomy-enriched data for ATS, HCM, and AI recruiting workflows. When used on a careers page or application form, the same extraction call auto-fills every application field in under 10 seconds — documented to increase candidate conversion by up to 194%. Supports 40+ languages with English-normalized output for global intake, and runs in batch mode to process legacy databases or migration backlogs overnight at scale. Also supports resume reprocessing — re-running previously extracted resumes through the latest extraction logic and taxonomy version to bring older records up to current data quality, without requiring a new document from the candidate. Distinct from bulk import (first-time extraction of a new batch) and from talent data refresh (re-enrichment from a newer submitted resume).

Input Schema

NameRequiredDescriptionDefault
filenameNoOptional original file name (default 'inline_resume.txt'). A '.txt' extension is appended automatically if missing.
resume_textNoComplete, verbatim plain-text of the resume/CV — every line exactly as in the original. Do NOT summarise, paraphrase, or base64-encode; pass raw text.

Output Schema

NameRequiredDescriptionDefault
dataYesTool-specific result payload. The exact fields depend on which tool produced this result.
metaYesRequest metadata: unique trace ID and server-side processing latency.
successNoAlways ``true`` for a successful result. Check this field first to distinguish success from error envelopes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint, so the safety profile is covered. What the description adds is largely unverifiable marketing ('up to 194%' conversion, 'under 10 seconds') and capability claims that the signature cannot back up — 'runs in batch mode' and 'resume reprocessing... without requiring a new document' are impossible with a zero-required-param call that only accepts a filename and inline text. These overstatements actively mislead about what the call does rather than clarifying it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is front-loaded correctly, but the remainder is padded with promotional copy — conversion-rate statistics, speed claims, language counts — that does not help an agent decide or invoke. The genuinely useful sentence (the bulk import / talent refresh distinction) is buried at the end, and two full sentences are spent on capabilities the tool does not expose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be explained, and the input schema is fully documented. But the description spends its length on modes (URL intake, batch, reprocessing) that this tool's parameters cannot perform, while omitting the one thing the agent needs to disambiguate: that this tool requires the resume as inline text, unlike its extract_resume_data_from_url sibling. For a tool in a 30+ sibling set with a near-identical name, that omission is a real gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and both parameters are well documented in the schema itself (filename default and .txt append behavior; resume_text must be verbatim, not summarized or base64-encoded), so the baseline of 3 applies even though the description says nothing about either parameter. The description neither helps nor badly harms parameter understanding, but its silence on the inline-text requirement while touting URL/batch scenarios works mildly against correct invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence names a specific verb and resource ('Extracts and converts resumes, CVs, and candidate documents into structured, searchable profiles') and enumerates the output fields, so the agent knows exactly what the tool produces. It also contrasts with two related workflows (bulk import, talent data refresh), but those are not actual siblings — the real ambiguous neighbor, extract_resume_data_from_url, is never mentioned, so differentiation from the tool the agent is most likely to confuse it with is missing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It describes several usage scenarios (careers page/application form auto-fill, batch migration, reprocessing old records), which implies when to reach for it. However, none of those scenarios are expressible with this tool's two parameters (filename, resume_text) — there is no URL, batch, or reprocess flag — and the description never states the actual selection rule: use this for inline resume text, use extract_resume_data_from_url when you only have a link. Usage is implied but not actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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