jev-mcp
Jev-MCP is a Model Context Protocol server that provides fast, no-text-generation typed judgments over supplied state using TypeSafe Jev models.
Run typed questions with
jev_ask: mix Choice, Score, and Noul (yes/no) questions over arbitrary state.Classify with
jev_classify: pick one label from a closed list, optionally with an order ensemble for disagreement detection.Score with
jev_score: place state on an ordered rubric/levels.Check with
jev_check: evaluate independent yes/no propositions.Match with
jev_match: find the best candidate for a query with an explicit abstention signal (exists).Screen with
jev_screen: filter untrusted or instruction-like text (as a filter, not a security boundary).Inspect with
jev_health: list available models and measure round-trip latency.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@jev-mcp classify these support tickets as bug, billing, or account access"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
jev-mcp
Jev provides typed judgments over a closed answer set in about 130 ms, with no text generation, through TypeSafe.
Install
Set TYPESAFE_API_KEY in the environment used by your MCP client, then run:
uvx --from git+https://github.com/blakestone-x/jev-mcp@v0.2.1 jev-mcpClaude Code:
claude mcp add --scope user jev -e TYPESAFE_API_KEY=... -- uvx --from git+https://github.com/blakestone-x/jev-mcp@v0.2.1 jev-mcpCodex (~/.codex/config.toml):
[mcp_servers.jev]
command = "uvx"
args = ["--from", "git+https://github.com/blakestone-x/jev-mcp@v0.2.1", "jev-mcp"]
env = { TYPESAFE_API_KEY = "..." }Cursor or another MCP client:
{
"mcpServers": {
"jev": {
"command": "uvx",
"args": ["--from", "git+https://github.com/blakestone-x/jev-mcp@v0.2.1", "jev-mcp"],
"env": { "TYPESAFE_API_KEY": "..." }
}
}
}Replace ... with the key value. Passing it through the client environment keeps it out of tool arguments and request state.
The scripts/register.sh helper requires Bash 4+; do not run it with sh. Apply mode reads
TYPESAFE_API_KEY from the environment and refuses to change client configuration when it is unset. --apply writes
the plaintext key into ~/.codex/config.toml and the Claude Code user config. The key is briefly visible in the process
list while claude mcp add runs because that command receives it as an argument. On Windows, use
scripts/register.ps1; it restricts the resulting configuration files to the current user and keeps a .bak copy
before rewriting an existing Codex configuration.
The install examples pin the public v0.2.1 tag. Omitting the tag tracks the repository's default branch instead.
Related MCP server: jev-mcp-python
Worked example
This call classifies one state containing five support-ticket texts against one label map. It returns one label for the combined state; send one request per ticket when you need an individual label for each ticket:
{
"state": [
"The password reset link expired before I could use it.",
"I do not recognize the charge on my statement.",
"My order arrived two days late.",
"The export button shows a blank screen.",
"Please add a dark theme."
],
"labels": {
"account_access": "Login, password, or sign-in problems.",
"billing": "Charges, invoices, or payment questions.",
"delivery": "Late, missing, or damaged deliveries.",
"bug": "Unexpected product behavior.",
"feedback": "Suggestions or general comments."
},
"question": "Which category best fits the combined state of these five support tickets?",
"add_other": true
}The response is an ordinary MCP structured result with a stable envelope:
{
"ok": true,
"model": "jev-latest",
"answers": {
"choice": "account_access",
"confidence": 0.91,
"probabilities": {
"account_access": 0.91,
"billing": 0.03,
"delivery": 0.02,
"bug": 0.02,
"feedback": 0.01,
"other": 0.01
},
"band": "act"
},
"usage": {
"input_tokens": 420,
"output_tokens": 0,
"est_cost_usd": 0.00001764
},
"latency_ms": 130.0
}Tools
tool | use it for | not for |
| Your own Choice, Score, and Noul question map | Text generation or numeric extraction |
| Selecting one label from a closed list | Open-ended writing or ranking without labels |
| Placing state on an ordered rubric | Returning a number extracted from text |
| Independent yes/no propositions | Treating a probability as proof |
| Matching a query to candidates with abstention | Assuming the closest candidate is genuine |
| Filtering instruction-like or untrusted text | A security boundary or authorization decision |
| Listing served models and measuring round-trip latency | Evaluating application content |
Design principles
The key stays in the environment and never in the agent's context.
All policy thresholds live in one file:
jev_mcp/questions.py.Confidence is distribution concentration, not correctness.
Screening is a filter, not a security boundary.
Gate consequential decisions on the Score.
Order-ensemble turns disagreement into a cheap review signal.
Matching includes abstention through the
existsNoul.
Profiles and recipes
examples/triage.questions.json and examples/route.questions.json are generic, complete jev_ask request examples. They contain a state object and a questions map in the wire shape accepted by jev_ask; replace state with your own data and send the whole object as the tool arguments. Jev intentionally does not load profiles for you.
import json
with open("examples/triage.questions.json", encoding="utf-8") as file:
request = json.load(file)
result = await session.call_tool("jev_ask", request)See RECIPES.md for comparison, reference-in-state, descriptions, batching, calibration, and order-ensemble patterns.
What we measured
The measurements below come from a field-service company's production data.
measurement | result |
Clean labels at confidence 0.8 to 1.0 | 95% agreement |
One-line label descriptions | 81.0% agreement; 1,689 tokens/request |
Rich label descriptions | 84.5% agreement; 4,745 tokens/request |
Reversing criteria order | 32 of 200 choices flipped; flipped confidence averaged 0.42 |
Candidate matching | 8 of 11 picks matched when a candidate existed; misses had low |
Amount comparison | 100% on 64 cases where displayed amounts differed |
Past-date comparison | 100% for “is it past”; 98% on a four-level overdue Score |
Limits
The default serialized request budget is 120,000 characters for the state and questions sent to the service. Set
JEV_MCP_MAX_REQUEST_CHARS to use a different positive limit. JEV_MCP_MAX_STATE_CHARS remains accepted as a
compatibility alias for one release. For jev_match, the budget applies to the full candidate input and to each
serialized candidate window.
The service provider has not published rate limits. What we observed on one early-access key: small requests ran at
160 per second with flat latency and no rate-limit responses, while a sustained run of about 40 requests per second
at 1,700 tokens each drew 429s after three minutes. Treat the limit as tokens per minute rather than requests per
second, keep bulk jobs resumable, and back off for seconds on a 429, not milliseconds. This server's retry policy
backs off from 1 second to 20 seconds and honors Retry-After. A Noul carries evidence strength, not confidence. Jev is early access:
validate thresholds and outputs against your own data, and keep consequential actions behind your normal review controls.
jev_match accepts a window from 20 through 254 and up to 2,000 candidates by default. Set
JEV_MCP_MATCH_MAX_CANDIDATES to change the candidate cap. For n candidates, the normal billed request count is
ceil(n / window) + 1: one request per window plus one finalist-selection request. A single-window call may need
only its initial request, and a call with no finalists may omit the extra request. exists is the maximum of the
per-window values, so more windows can inflate it; inspect windows and exists_by_window too. The match deadline is
45 seconds by default and can be changed with JEV_MCP_MATCH_DEADLINE_S.
Set JEV_MCP_MODEL to select the model; it defaults to jev-latest. The timeout contract is: a single tool call can take up to about 30 seconds when the service is degraded; jev_classify(ensemble=true) can take about 60 seconds because it makes two sequential evaluations; jev_match can take up to its deadline. SDK attempts use an 8-second HTTP timeout and a 20-second retry budget.
License
MIT. See LICENSE.
Available Tools
7 toolsjev_askB
Use Jev for typed judgments over supplied state; it is not for text generation or numeric extraction.
Confidence is distribution concentration, not correctness. The default est_cost_usd uses the published early-access input price and may change.
Inputs: state (required), questions (required). Returns: object
jev_checkB
Use Jev for independent yes/no checks over state; it is not for text generation or numeric extraction.
Confidence is distribution concentration; Noul probability is evidence strength. The default est_cost_usd uses the published early-access input price and may change.
Inputs: state (required), propositions (required). Returns: object
jev_classifyA
Use Jev to select one label from a closed list; it is not for text generation or numeric extraction.
Confidence is distribution concentration, not correctness. With order-ensemble enabled, order flips concentrate at low confidence, so disagreement is a second, cheap reason to route to a person. The default est_cost_usd uses the published early-access input price and may change.
Inputs: state (required), labels (required), ensemble, question, +1 more. Returns: object
jev_healthA
Use Jev health to inspect the configured model and the account's available models and release dates.
Health checks report zero estimated cost; the published early-access input price may change for other tools.
Inputs: no named parameters. Returns: object
jev_matchA
Find the best candidate with an explicit abstention signal; it is not text generation.
Candidates are evaluated in windows, and exists is the maximum of the
per-window Noul probabilities. More windows can therefore inflate exists;
inspect exists_by_window and windows before using the result. The
default est_cost_usd uses the published early-access input price and may change.
Inputs: query (required), candidates (required), window, question. Returns: object
jev_scoreB
Use Jev to place state on an ordered situation rubric; it is not for text generation or numeric extraction.
Confidence is distribution concentration, not correctness. The default est_cost_usd uses the published early-access input price and may change.
Inputs: state (required), levels (required), question (required). Returns: object
jev_screenB
Use Jev to screen untrusted text; it is a filter, not a security boundary.
Confidence is distribution concentration, not correctness. The default est_cost_usd uses the published early-access input price and may change.
Inputs: text (required), source. Returns: object
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 7 tools
Each tool maps to a distinct judgment mode: open typed judgment, closed-list classification, ordered scoring, yes/no checking, candidate matching, text screening, and health inspection. The generic jev_ask could broadly overlap with the more specific modes, but the descriptions clarify when each should be used.
All tools share the consistent jev_ prefix and use short lowercase names matching their action or purpose. The naming convention is uniform and predictable across the entire set.
Seven tools is a well-scoped set for a single Jev judgment API. Each tool covers a distinct operation without redundancy, and the count feels neither thin nor bloated.
The tool surface covers the core judgment types Jev appears to support: ask, classify, score, check, match, and screen, plus health for configuration introspection. The repeated exclusions of text generation and numeric extraction indicate those are intentionally out of scope rather than missing.
Maintenance
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Sentiment, toxicity, entity extraction, PII, translation, summary, QA, fraud scoring, safety audit.
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- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to obtain calibrated probabilities, option picks, scale scores, and multi-question judgments for batches of short text, returning numeric results for sorting, filtering, and counting.MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI agents to submit a state and typed questions and receive structured judgments such as yes/no probabilities, named choices, and rubric scores, with no text generation or parsing.MIT