jev-delegate
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENROUTER_API_KEY | Yes | Your OpenRouter API key. Each installation uses its own key. Required to run the server. | |
| JEV_DELEGATE_TELEMETRY | No | Set to 'off' to disable telemetry entirely. Any other value or unset enables telemetry. | on |
| JEV_DELEGATE_TELEMETRY_PATH | No | Path to the local metadata-only telemetry file. Defaults to ~/.codex/state/jev-delegate/usage.jsonl unless set. | ~/.codex/state/jev-delegate/usage.jsonl |
| JEV_DELEGATE_WORKSPACE_ROOT | No | Set to the project directory if your MCP client does not expose workspace roots. Not needed for Claude Code, which exposes the project directory automatically. |
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 |
|---|---|
| search_and_rankA | Run read-only ripgrep inside the current workspace, then use Jev to return only relevant matches. Use automatically when raw search would exceed 20 candidates or about 4K tokens. English judgments only. |
| classify_itemsA | Assign exactly one caller-supplied label to each item using Jev typed decisions. Adds other and insufficient_context labels. Use for English bulk classification, not prose generation. |
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 2 tools
The two tools are completely distinct: one focuses on searching and ranking relevant matches from raw search results, while the other assigns labels to items. There is no overlap in purpose, and an agent can easily select the correct tool based on the task.
Both tools use an imperative verb phrase, but 'search_and_rank' is a compound verb while 'classify_items' follows a verb_noun pattern. The slight inconsistency is minor and does not hinder readability, but a more uniform pattern (e.g., 'search_and_rank_items') would be ideal.
With only two tools, the server feels thin for a delegation service. However, the narrow scope of search ranking and classification justifies a small count, making it borderline rather than excessive. Each tool serves a clear purpose, so the count is acceptable but leaves little room for additional functionality.
The tool surface covers the core functions implied by the server name: search-and-rank and classification. Minor gaps exist (e.g., no summarization or generation tool), but the stated purpose is well-addressed, and an agent can complete common tasks without hitting dead ends.