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

# jev_search

# `fastmcp.experimental.transforms.jev_search`

Tool search ranked by TypeSafe's Jev.

Jev is a System One model: it does not generate text. A request carries a
`state` and a map of typed questions, every question is judged against the
same state in parallel, and each answer is a probability distribution over
options the caller defined. That makes it a natural ranker for a tool
catalog: the query is the state, the tool names are the options, and the
probabilities are the ranking.

The transform follows the shape of TypeSafe's skill-suggestion cookbook
([https://docs.typesafe.ai/cookbooks/skill\_suggestion](https://docs.typesafe.ai/cookbooks/skill_suggestion)): a cheap wide pass over
the whole catalog on one-line summaries, then a close read of a shortlist
with each tool's full description and parameters. The close read asks two
kinds of question. A Choice decides *which* candidate fits best and orders
the results. One Noul per candidate decides *whether* it does what the query
asks at all, so a query nothing serves comes back empty instead of returning
the least-wrong tool.

## Classes

### `SystemOneClient` <sup><a href="https://github.com/PrefectHQ/fastmcp/blob/main/fastmcp_slim/fastmcp/experimental/transforms/jev_search.py#L54" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

The slice of `typesafe_sdk.AsyncTypeSafeClient` the transform uses.

**Methods:**

#### `system_one` <sup><a href="https://github.com/PrefectHQ/fastmcp/blob/main/fastmcp_slim/fastmcp/experimental/transforms/jev_search.py#L57" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}}
system_one(self, state: Any, questions: Any) -> Any
```

### `JevSearchTransform` <sup><a href="https://github.com/PrefectHQ/fastmcp/blob/main/fastmcp_slim/fastmcp/experimental/transforms/jev_search.py#L87" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

Search transform that ranks tools with TypeSafe's Jev.

Experimental: the ranking parameters may change. Requires the `jev`
extra (`pip install "fastmcp[jev]"`) and a TypeSafe API key, read from
`TYPESAFE_API_KEY` unless `api_key` or `client` is given. A missing
key is an error at construction, not at the first search.

Tool descriptions are model input. A description written to argue for
its own selection can move the ranking; the transform only ranks tools
the caller could already list, so that exposure is bounded by what the
catalog holds.

**Args:**

* `model`: The TypeSafe model name. `jev-latest` follows releases;
  pin a versioned id once you have tuned `fit_threshold`.
* `api_key`: TypeSafe API key. Defaults to `TYPESAFE_API_KEY`.
* `client`: A ready `AsyncTypeSafeClient` (or anything with an async
  `system_one`) to use instead of building one.
* `timeout`: Seconds per API attempt when the transform builds its own
  client. A search is one to a few requests.
* `shortlist`: How many candidates each wide-pass request carries
  forward. With `close_read=True`, this must be at most half of
  `chunk_size` so every wide pass reduces the candidate set by a
  meaningful amount. The close read sees at most
  `min(3 * shortlist, 255)` candidates.
* `fit_threshold`: A candidate whose "does this tool do what the request
  asks" probability falls below this is dropped from the results.
  Tune it against queries from your own users.
* `close_read`: Whether to re-read the shortlist with full descriptions
  and parameters in a second request. `False` asks the fit
  question of every tool in the wide pass instead, on summaries
  only, so a search is a single round trip.
* `chunk_size`: Tools per wide-pass request. Catalogs above this size
  are ranked in concurrent chunks. At most 255, the Choice limit.
* `summary_chars`: Characters of description per tool in the wide pass.
* `detail_chars`: Characters of rendered description and parameters per
  tool in the close read.