jev-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| JEV_MODEL | No | Model id. Defaults to 'jev-latest'. | jev-latest |
| JEV_API_KEY | No | Alternative to TYPESAFE_API_KEY. | |
| JEV_TIMEOUT_MS | No | Per-attempt timeout. Defaults to 15000. | 15000 |
| TYPESAFE_API_KEY | No | Your TypeSafe API key (required unless JEV_API_KEY is provided). | |
| JEV_MAX_QUESTIONS | No | Questions per jev_ask. Defaults to 64. | 64 |
| TYPESAFE_LOG_LEVEL | No | SDK verbosity. Safe at any level; all output goes to stderr. | |
| JEV_MAX_STATE_CHARS | No | Largest state accepted. Defaults to 200000. | 200000 |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| jev_classifyA | Pick exactly one option from a set you define. Returns the chosen option, the probability of every option, a confidence value, and a recommended action gated on confidence. Use when the answer is one of a fixed set. The options must be supplied by you: Jev selects among them and cannot invent a new one. Up to 255 options. |
| jev_scoreA | Rate the state along an ordered scale you define. Returns a probability-weighted score that can land between levels, the distribution, confidence, and a recommended action. Use for degree or severity, not for picking a category. |
| jev_checkA | Ask a yes/no question. Returns the probability that the answer is yes, from 0 to 1, plus a verdict. There is no separate confidence: a value near 0.5 means yes and no are close to equally likely, not that the answer is 'medium'. Use one check per label when several labels may apply at once. |
| jev_askA | Ask several independent questions about the same state in ONE request. Jev prefills the state once and scores every question in a single forward pass, so extra questions add almost no latency. Prefer this over repeated single-question calls: on a document-dominated workload it is dramatically cheaper and faster with no change in answers. Questions cannot see each other's answers, so state any speculative premise explicitly and let your own logic decide which answers apply. |
| jev_modelsA | List the models this API key can use, with their release dates. Use it to confirm the key works and to find a model id for JEV_MODEL before assuming one exists. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose: listing models, picking one from a set, scoring on a scale, yes/no checking, and batching multiple independent questions. The descriptions clarify boundaries (e.g., jev_check is specifically for yes/no, while jev_classify handles arbitrary options). No overlap that would cause an agent to misselect.
All tools share the consistent 'jev_' prefix, but the second part is mostly verbs (classify, ask, score, check) except for 'models' which is a noun. This is a minor deviation from a strict verb pattern, but still predictable and readable.
Five tools is well-scoped for a classification/scoring API. Each tool earns its place and covers distinct operations without redundancy. This is within the ideal 3-15 range and appropriate for the server's purpose.
The surface covers the core operations: model discovery, single-label classification, ordered scoring, binary checks, and batched multi-question inference. There are no obvious gaps for the apparent domain; agents can perform all necessary workflows without workarounds.