> ## Documentation Index
> Fetch the complete documentation index at: https://developers.techwolf.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Fetching Matches

Once your Employees and Vacancies have been created inside the Skill Engine
API, you can start generating Vacancy/Employee matches.

## Finding Vacancies for an Employee

To find the most suitable Vacancies for a given Employee, submit a `POST`
request to `/employees/<external_id>/matching_vacancies`. When you want to use
the default matching configuration, the request body below is sufficient. In
case you want to add filters & configure the matching score calculation, head
over to [the next page](/tutorials/vacancy-matching/configuring-matches/)!

```json theme={null}
{
  "filters": [],
  "weights": []
}
```

The matches in the response object are sorted by decreasing match score. They
are returned with the following structure:

```json theme={null}
[
  {
    "employee_id": "b003505-eb84-42dc-a79f-5e7b1fe897b7",
    "vacancy_id": "a3903505-eb84-42dc-a79f-5e7b1fe897b7",
    "score": 0.87657
  },
  {
    "employee_id": "b003505-eb84-42dc-a79f-5e7b1fe897b7",
    "vacancy_id": "ae4fcdf2-cda5-11ea-87d0-0242ac130003",
    "score": 0.6544
  }
]
```

You can tune the matching results to your preference via the query
parameters `score_min_threshold` and `limit`.

## Finding Employees for a Vacancy

Finding the most suitable Employees for a given Vacancy is similar to the
approach described above, with the only difference being the
endpoint: `/vacancies/<external_id>/matching_employees`.

## Getting Explanations

With the `matching_employees` and `matching_vacancies` presenting the best
matches for a given entity, we can also zoom in on an individual match,
fetching not just the match score itself, but also the explanations behind it.

By sending a `GET` request
to `/employees/<external_id_1>/vacancies/<external_id_2>/match`, with query
parameter `response_format=explained`, you can retrieve the relevant skills &
relevant experience, which explain the matching score.

You can then use these explanations to enrich your user experience, as well as
to assist users in providing feedback about the match.