> For the complete documentation index, see [llms.txt](https://agpt.co/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://agpt.co/docs/integrations/block-integrations/score.md). # Typesafe Score Score evidence on a concrete, ordered scale with TypeSafe Jev. ## Jev Score ### What it is Score evidence with Jev using an explicit ordered scale. Levels define the scale: describe concrete evidence for each tier so a very good case does not reach the top. For hiring, distinguish terrible, bad, okay, excellent, brilliant, and one-in-a-thousand by what qualifies. ### How it works Provide a question and at least two level descriptions, ordered from lowest to highest. Jev returns a potentially fractional score, the level legend, probabilities, and confidence. The block emits these values directly and computes only `max_score` as `len(levels) - 1`. The level text defines what earns each tier. For example, distinguish delivering a tested feature, independently operating production services, and leading measurable improvements across teams; reserve the top tier for evidence beyond ordinary excellent performance. Each call exposes its actual bodies, usage, timing, and truncation status. A failed call emits an error without a score. ### Inputs | Input | Description | Type | Required | | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | -------- | | state | Text or JSON context shared by every question in this stateless call. JSON is compactly serialized; oversized context is truncated with a note. | State | No | | question | Plain-language question to score. | str | Yes | | levels | Ordered level descriptions, lowest first. Levels define the scale: describe concrete evidence for each tier so a very good case does not reach the top. For hiring, distinguish terrible, bad, okay, excellent, brilliant, and one-in-a-thousand by what qualifies. | List\[str] | Yes | ### Outputs | Output | Description | Type | | ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- | ----------------- | | error | API failure details; emitted only when a call fails. | str | | request | Verbatim JSON request body sent to Jev. | str | | response | Verbatim UTF-8 response body from Jev, or null if no response arrived. Invalid UTF-8 is preserved as a lossless Base64 data URL with an error. | str | | latency\_ms | Wall-clock API call duration in ms. | float | | input\_tokens | Input tokens reported by the API, or null if unavailable on failure. | int | | output\_tokens | Output tokens reported by the API, or null if unavailable on failure. | int | | request\_id | TypeSafe request ID response header. | str | | truncated | Whether any input text was truncated. | bool | | truncation\_note | Truncation details, or an empty string. | str | | score | Score returned by Jev on the 0..max\_score scale. | float | | legend | Level numbers mapped to their descriptions. | Dict\[str, str] | | probabilities | Probability for each score level. | Dict\[str, float] | | confidence | Confidence returned by Jev. | float | | max\_score | Highest possible score: number of levels minus one. | int | ### Possible use case **Role-Fit Review**: Rate documented experience against concrete hiring tiers before a human reviews the result. **Incident Impact**: Score an incident using levels tied to the number of users or workflows affected. **Specification Completeness**: Evaluate whether a document provides the evidence required by each completeness tier. *** --- # Agent Instructions This documentation is published with GitBook. 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