canonical: https://jentic.com/apis/codequiry.com/codequiry

# Codequiry API

The Codequiry API checks source code for similarity and plagiarism and flags AI-generated submissions. The typical flow creates a check, uploads one or more ZIP submissions, starts the check, polls its status, and then reads an overview and detailed results, including the matched web files behind a flagged section. A quick check runs that whole sequence in a single call. Results can be read through the viewer, exported as CSV or a full export, and an AI detection operation reports whether a submission looks machine-written.

## For AI agents

Create a code similarity check, upload ZIP submissions, start it, and read overview and detailed plagiarism results plus AI-generation detection. A quick check runs the whole flow in one call.

## Scope

Does not grade assignments, run unit tests, or scan for security vulnerabilities. Use for source code similarity and AI-generation detection only.

## Capabilities

- Create a similarity check and upload one or more ZIP code submissions
- Start a check and poll its status until it completes
- Read overview and detailed similarity results, including matched web files
- Detect AI-generated code through the AI detection results
- Run an all-in-one quick check in a single call
- Export results as CSV or a full export and open the results viewer
- List your checks, supported languages, and detection engines

## Use cases

### Agent-Run Plagiarism Checks Through Jentic

An AI agent reviewing code submissions can run Codequiry through Jentic instead of wiring the multi-step flow by hand. The agent creates a check, uploads the ZIP submissions, starts the check, polls until it finishes, and reads the overview and detailed results. Because checks are addressed by id, the agent's reach is easy to bound to the checks it created.

Example prompt: Create a check, upload a ZIP submission, start the check, poll until it completes, and read the results overview

### Batch Student Submission Checking

For academic integrity, a workflow uploads many student code submissions in a batch and checks them for similarity against each other and web sources. The overview highlights the highest-similarity pairs, and the detailed results and viewer show the matching sections side by side.

Example prompt: Batch-upload a set of student submissions to a check, start it, and read the overview to find the highest-similarity pairs

### AI-Generated Code Detection

To screen for machine-written code, a check includes an AI detection step that reports whether a submission looks AI-generated. A reviewer can run this alongside the similarity pass to flag submissions that warrant a closer look.

Example prompt: Fetch the AI detection results for a completed check and flag any submission scored as likely AI-generated

### Originality Gate for Code Contributions

A contribution pipeline can run a quick check on an incoming code submission and gate the next step on the similarity outcome. The single quick-check call uploads, runs, and returns a result so the pipeline does not have to orchestrate each step itself.

Example prompt: Run a quick check on an incoming submission and branch the pipeline on whether the similarity exceeds a threshold

## Key endpoints

| Method | Path | Description |
| --- | --- | --- |
| POST | `/check/create` | Create a check |
| POST | `/check/upload` | Upload a submission ZIP |
| POST | `/check/start` | Start a check |
| GET | `/checks/{checkId}/status` | Get check status |
| POST | `/check/overview` | Get results overview |
| POST | `/check/results` | Get detailed results |
| GET | `/ai-results` | Get AI detection results |
| POST | `/check/quick` | Run an all-in-one quick check |

## Key resources

- **Checks** — Create, list, get, update, and delete similarity checks and start them running
- **Submissions** — Upload single or batch ZIP submissions and delete a submission from a check
- **Results** — Overview, detailed results, matched web files, CSV and full exports, and the results viewer
- **AI Detection** — Report whether submissions in a check look AI-generated
- **Account and Metadata** — Validate the API key, read account and quota, and list supported languages and detection engines

## AI readiness

This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.

- **Score:** 65 / 100
- **Maturity:** AI-Aware
- **Dimensions:**
  - Foundational Compliance: 70 / 100
  - Developer Experience & Jentic Compatibility: 79 / 100
  - AI-Readiness & Agent Experience: 49 / 100
  - Agent Usability: 94 / 100
  - Security: 50 / 100
  - AI Discoverability: 72 / 100
- **View full report:** https://jentic.com/apis/codequiry.com/codequiry/scorecard
- **How the score is calculated:** https://docs.jentic.com/reference/api-readiness-framework/overview/
- **More about the dimensions:** https://docs.jentic.com/reference/api-readiness-framework/specification/#dimensional-model-overview

### Score it yourself

Every API in the directory is allowlisted, so you can re-score it with no key required.

- **Score your own API:** https://jentic.com/scorecard.md
- **Scoring CLI agent skill:** https://github.com/jentic/jentic-api-scorecard/blob/main/skills/jentic-api-scorecard/SKILL.md

```sh
npx @jentic/api-scorecard-cli score <openapi-url>
```

## Why Jentic

- **Setup:** Wiring the Codequiry API by hand means passing your key in the apikey request header and following its multi-step flow: create a check, upload ZIP submissions, start it, then poll status before reading results. Through Jentic you install once, import Codequiry from the API Directory, store the key once, and your agent calls it.
- **Permission scoping:** Codequiry puts the check id in the URL path for its REST operations (the /checks/ prefix), so a rule can pin your agent to the checks it needs and the operations you choose. You choose the operations it may call, so destructive ones like deleting a check or a submission are not included unless you add them.
- **Credential handling:** Your Codequiry 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.
- **Discovery method:** Agents search Jentic by intent such as check code for plagiarism, and Jentic returns the matching Codequiry operation with its input schema so the agent calls the right endpoint without reading the reference docs.

## Related APIs

- **Code Climate** — Automated code analysis that checks code quality rather than plagiarism or AI generation.
- **SonarQube** — Static analysis for bugs, code smells, and security issues alongside originality checks.
- **GitLab** — Source control and CI where submissions live and checks can be triggered on merge requests.

## FAQ

### What authentication does the Codequiry API use?

The Codequiry API authenticates with an API key sent in the apikey request header, per its OpenAPI spec; a validate operation lets you confirm the key works. Through Jentic the key is stored encrypted by your own Jentic One instance and injected at call time, so it never reaches the agent's context.

### Can I check code for plagiarism with the Codequiry API?

Yes. The typical flow is to create a check, upload one or more ZIP submissions, start the check, poll its status, and then read the overview and detailed results, including the matched web files behind a flagged section. A quick check runs that whole flow in a single call.

### Can the Codequiry API detect AI-generated code?

Yes. Alongside similarity detection, an AI detection results operation reports whether a submission looks AI-generated. You run it against a completed check to get the AI results for the submissions in it.

### How do I run a plagiarism check with the Codequiry API through Jentic?

Install Jentic One, then import Codequiry from the API Directory and store your key once. Your agent searches by intent such as check code for plagiarism, and Jentic returns the create-check and upload operations with their input schemas so the agent uploads the submissions, starts the check, and reads the results. To run it on your own infrastructure, install Jentic One from its GitHub repo.

### Is there a Codequiry API MCP server?

You don't need an MCP server to give your agent the Codequiry API. Jentic connects it directly from the API Directory: import it, store your key once, and your agent calls the check, upload, and results operations on demand. Operations are discovered when the agent needs them, so they do not fill its context ahead of time.

### Can I limit what my agent is allowed to do with the Codequiry API?

Yes. Write a rule that allows only the operations your agent needs, such as creating checks, uploading submissions, and reading results, so it can run plagiarism checks and nothing else. Deleting a check or a submission stays out of that set unless you add them, and every call the agent makes is logged.