Install Jentic One Beta

Connect the Laddro Career API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Laddro Career API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fladdro.com%2Fladdro" | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fladdro.com%2Fladdro" | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Laddro Career API.

Tailor a resume to a job by positionName plus a jobDescription or jobUrl, returning a PDF and the new resume id in X-Resume-Id

Generate a cover letter for a position from a stored resume or an uploaded file, or save one from your own letterContent

Parse an uploaded PDF resume into a stored, editable resume with a chosen template

Re-render a resume or cover letter with a different templateId, locale, font, fontSize, spacing, margin, or colorId

GET STARTED

Start building with Laddro Career API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Laddro Career API for, with concrete tasks.

★ Job-Specific Resume Tailoring

A job-search agent applies to each role with a resume rewritten for that posting. It sends the positionName with either the pasted jobDescription or a jobUrl that Laddro fetches and extracts, along with a stored resumeId or an uploaded PDF, and receives a tailored PDF. Setting includeCoverLetter returns a ZIP with both documents, and response headers carry the ids of the saved resume and cover letter.

Call POST /v1/tailor with resumeId from GET /v1/resumes, positionName 'Senior Data Engineer', jobUrl 'https://example.com/jobs/123', and includeCoverLetter 'true', then save the returned ZIP

Cover Letter Generation

When only a letter is needed, the agent calls the generate operation with the positionName and a job description or URL, using a stored resume or an uploaded file as the source. A letter written elsewhere can be saved instead with fullName and letterContent, then rendered with a template so it matches the resume's styling.

Call POST /v1/cover-letters/generate with resumeId, positionName 'Product Manager', and jobDescription text, then PUT /v1/cover-letters/{coverLetterId}/render with a templateId from GET /v1/templates

Resume Import and Restyling

A candidate's existing PDF becomes a structured resume through the parse operation, after which the agent can re-render it with any template, locale, font, and color, or export it to a PDF. The templates, fonts, and languages lists need no API key, so the agent can show options before spending credits.

Call POST /v1/resumes/parse with the PDF file and a templateId, then POST /v1/export with the returned resumeId and a font from GET /v1/fonts

AI Agent Job Applications via Jentic

An AI job-search agent calls Laddro through Jentic without holding the laddro_live_ key. It searches by intent, receives the tailoring or cover-letter operation with its input schema, and executes with the x-api-key injected at run time, so producing a tailored resume is one step in a larger application workflow.

Search Jentic for 'tailor a resume to a job', load the POST /v1/tailor schema, and execute it with a resumeId, positionName, and jobUrl

Key Endpoints

15 endpoints — the laddro career api rewrites a resume to match a specific job posting and returns the tailored version as a pdf, optionally with a matching cover letter in a zip.

METHOD

PATH

DESCRIPTION

POST

/v1/tailor

Tailor resume for a job

POST

/v1/cover-letters/generate

Generate cover letter

POST

/v1/resumes/parse

Create resume from PDF

POST

/v1/export

Export resume as PDF

GET

/v1/resumes

List resumes

PUT

/v1/resumes/{resumeId}/render

Render resume with new template settings

GET

/v1/templates

List resume templates

POST

/v1/tailor

Tailor resume for a job

POST

/v1/cover-letters/generate

Generate cover letter

POST

/v1/resumes/parse

Create resume from PDF

POST

/v1/export

Export resume as PDF

GET

/v1/resumes

List resumes

PUT

/v1/resumes/{resumeId}/render

Render resume with new template settings

GET

/v1/templates

List resume templates

Why Jentic?

What agents get from Jentic-routed access to this vendor.

Setup

Wiring Laddro by hand means sending a laddro_live_ key in x-api-key, choosing between JSON and multipart uploads, handling PDF or ZIP binary responses, and watching for 402 when credits run out. Through Jentic you install once, import Laddro from the API Directory, store the key once, and your agent calls it.

Permission scoping

Laddro puts the resume id in the URL path (/v1/resumes/{resumeId}/...), so a rule can pin your agent to one resume for reads and re-renders. Tailoring, generation, and export carry their targets in the request body, so for those you choose which operations it may call, and credit-spending ones are not included unless you add them.

Credential isolation

Your Laddro API key is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'tailor a resume to a job' or 'export a resume as PDF', and Jentic returns the matching Laddro operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using Laddro Career API through Jentic.

What authentication does the Laddro Career API use?

Per its OpenAPI spec, the Laddro Career API uses an API key, starting with laddro_live_, sent in the x-api-key header. Keys come from console.laddro.com. The templates, template detail, fonts, and languages endpoints are public and need no key. Through Jentic the key is stored encrypted by your own Jentic One instance and injected at execution time.

Can I tailor a resume from a job URL with the Laddro Career API?

Yes. POST /v1/tailor takes positionName (required) and either jobDescription or jobUrl, which Laddro fetches and extracts. Use a stored resumeId, upload a PDF or DOCX file, or send neither to use your default resume. Set includeCoverLetter to true to receive a ZIP with the tailored resume and a cover letter.

What are the rate limits for the Laddro Career API?

The OpenAPI spec describes credit-based usage rather than a fixed request rate: each action consumes credits from a monthly quota that depends on your plan, then from prepaid credits. When credits run out the API returns 402. See https://console.laddro.com for your current quota.

How do I generate a cover letter through Jentic with the Laddro Career API?

Search Jentic for 'generate a cover letter for a job' and it returns the cover-letter generation operation with its input schema. Execute POST /v1/cover-letters/generate with positionName and a jobDescription or jobUrl, plus a resumeId. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Is there a Laddro MCP server?

You don't need an MCP server to give your agent the Laddro Career API. Jentic connects it directly from the API Directory: import it, store your credential once, and your agent calls it. Operations are discovered by intent on demand, so no extra server's tool definitions sit in the agent's context.

Can I limit what my agent is allowed to do with the Laddro Career API?

Yes. Give a browsing agent only the template, font, language, and resume read operations, so it can show options without spending credits. If it should work on one document, allow the render operation on a single resumeId and nothing else, and every call is logged by your own instance.

Laddro Career API

AI ReadinessFoundational (D)44/100

See full scorecard

Official vendor OpenAPI document · agent-readyAI/MLLanguage ModelsapiKey15 EndpointsREST

For Agents

Tailor a stored or uploaded resume to a job description or job URL, generate a cover letter, and render or export the result as a styled PDF.

Use for: Tailor my resume for this senior data engineer posting, Generate a cover letter for the product manager job at this URL, Turn my PDF resume into an editable Laddro resume, Export my resume as a PDF with the modern template

Not supported: Does not handle job applications to employers, applicant tracking, or job search listings. Use for tailoring, generating, and rendering resumes and cover letters only.

The Laddro Career API rewrites a resume to match a specific job posting and returns the tailored version as a PDF, optionally with a matching cover letter in a ZIP. It parses an uploaded PDF into a stored resume, generates or saves cover letters, re-renders resumes and letters with a chosen template, font, spacing, and color, and exports any stored resume as a PDF. AI endpoints can stream progress as Server-Sent Events, and calls draw on a monthly credit quota.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: A-Signals: 4

97%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

100%

Specification Validity

Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: A-Signals: 4

48%

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

55%

Response Coverage

Percentage of operations with complete response definitions (success, client error, server error).

100%

Tooling Readiness

Health of API ingestion, bundling, and resolution within Jentic pipelines.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: FSignals: 4

41%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

38%

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: A+Signals: 1

94%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: C-Signals: 1

50%

Authentication Strength

Average quality of security schemes based on authentication method strength (weakest link for OAuth2).

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: ASignals: 1

80%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Laddro Career API to your agent

Capabilities

Start building with Laddro Career API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Laddro Career API

Jentic AI Readiness Score

Laddro Career API

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness