Jev Decision MCP
Run a local stdio MCP server that uses TypeSafe's Jev model to make typed decisions from supplied context through one tool, jev_decide.
Exposes a single MCP tool,
jev_decide, for decision-making; it does not execute actions.Supports three question types:
choice(pick a label from 1–255 options with probabilities and confidence),score(rate on 2–10 ordered levels, returning a 0-based score, legend, probabilities, and confidence), andnoul(probability that a condition holds, from 0 to 1).Accepts a
statecontaining relevant text/JSON and named independentquestions, batching them in one call while preserving question IDs.Returns
model,answers, and tokenusageas both MCP structured content and JSON text.Lets callers override the model per call and configure defaults via environment variables such as
TYPESAFE_API_KEY,TYPESAFE_DEFAULT_MODEL, andJEV_TIMEOUT_MS.Works as a local stdio MCP server and can be run via
npx, installed globally, or built from source.Leaves thresholds, escalation, and action execution to the caller; it only returns judgments and usage.
Jev Decision MCP
A local stdio MCP server exposing one tool, jev_decide, for typed decisions through the official TypeSafe API. Written in TypeScript with the official MCP and TypeSafe SDKs.
Independent community project; not affiliated with TypeSafe. Licensed under MIT.
Question | Use it for | Result |
| Choose a label or candidate | Choice, probabilities, confidence |
| Rate a single dimension on 2–10 ordered levels | 0-based score, probabilities, legend, confidence |
| Judge whether a condition holds | Probability of yes, from 0 to 1 |
Batch independent questions over the same context in one call. Answers retain their question IDs. The MCP returns judgments and token usage; the caller owns thresholds, escalation, and action execution.
Requirements
Node.js 22 or newer, with npm available on your
PATH.An MCP client that supports local stdio servers.
An API key from TypeSafe Console. Use an official TypeSafe key; OpenRouter and third-party gateway keys are not supported.
Related MCP server: Codex Gemini MCP
Install from npm
The npm package includes the built server. npx downloads it on first use and runs it without cloning or building:
npx -y jev-decision-mcp@0.1.3Add the following to your MCP client's configuration, replacing YOUR_TYPESAFE_API_KEY with your key. For clients that use mcpServers JSON:
{
"mcpServers": {
"jev": {
"command": "npx",
"args": ["-y", "jev-decision-mcp@0.1.3"],
"env": {
"TYPESAFE_API_KEY": "YOUR_TYPESAFE_API_KEY"
}
}
}
}Alternatively, set TYPESAFE_API_KEY in the environment that launches your client and omit env if the client forwards that variable. Restart or reload your client's MCP servers after saving the configuration; the tool appears as jev_decide.
If the host cannot resolve npx, use its platform-specific launcher or absolute path
as documented by that host. GUI applications may have a different PATH from
your terminal.
The server communicates over stdio; starting it in a terminal waits for an MCP
client rather than opening a web page. The npm installation does not read a
.env from the caller's working directory.
To install the executable globally instead:
npm install -g jev-decision-mcp@0.1.3Then use "command": "jev-decision-mcp" with no arguments in your MCP client, and supply the same API key.
Install via Glama
Open Jev Decision MCP on Glama to view its README, tool schema, and source. For local installation, use the npm configuration above, or follow the listing's GitHub Repository link to download or clone the source and build it below. Glama lists this as a local stdio server; the listing URL is not an MCP connection endpoint.
Connect to Codex
For the npm package, merge this into your Codex config.toml:
[mcp_servers.jev]
command = "npx"
args = ["-y", "jev-decision-mcp@0.1.3"]
env_vars = ["TYPESAFE_API_KEY"]
tool_timeout_sec = 45Set TYPESAFE_API_KEY in the environment that launches Codex; env_vars forwards
its existing value to the server. Keep the host tool timeout above
JEV_TIMEOUT_MS / 1000. See official Codex MCP configuration.
Build from source
Clone the repository, install its dependencies, and compile the server:
git clone https://github.com/amidabuddha/jev-decision-mcp.git
cd jev-decision-mcp
npm ci
npm run buildYou can also download the repository with Code → Download ZIP on GitHub, extract it, and run npm ci and npm run build in the extracted directory.
Copy .env.example to .env in that directory and set TYPESAFE_API_KEY, or supply the key through your MCP client's environment. The source build reads this .env regardless of its working directory; existing environment variables take precedence. Restart the server after changing it. .env is excluded from Git.
Configure your MCP client to run the built entry point:
{
"mcpServers": {
"jev": {
"command": "node",
"args": ["/absolute/path/to/jev-decision-mcp/dist/index.js"]
}
}
}Replace the placeholder with your checkout's absolute path. Windows paths can use forward slashes, for example C:/projects/jev-decision-mcp/dist/index.js. If node is not on the client's PATH, use the absolute path to your Node executable.
For Codex, use the same command and args in the TOML example above. Keep env_vars only if you supply the key through Codex's environment instead of .env.
To verify your build, run npm run check (type checking and tests with mocked API responses; no TypeSafe calls). Use npm run dev for local development and rebuild with npm run build after source changes before restarting your MCP client.
Configuration
Set these variables in your MCP client's server environment, or in .env for a source build:
Variable | Default | Purpose |
| Required for calls | TypeSafe API credential |
|
| Default model; each call can override it |
|
| Total deadline including retries; 1–120000 ms |
Keep your client's tool timeout above JEV_TIMEOUT_MS / 1000 seconds. jev-latest follows TypeSafe model updates; set a specific supported model version for repeatable evaluations.
The server starts without a key so a client can discover the tool, but decision calls then return MISSING_API_KEY. State and questions are sent to the official TypeSafe API and may incur API charges. TYPESAFE_BASE_URL does not override the API endpoint. The server does not persist inputs or decisions, and upstream error bodies are not exposed.
Call the tool
Call jev_decide with the JSON in examples/decision.json. It combines a team choice, a refund yes/no judgment, and an urgency score. Smaller example:
{
"state": { "request": "Please refund the duplicate charge." },
"questions": {
"route": {
"type": "choice",
"instructions": "Which team should handle `request`?",
"criteria": {
"billing": "Charges, invoices, refunds",
"technical": "Broken software or integrations",
"other": "Neither billing nor technical"
}
}
}
}The response contains model, answers, and usage, both as MCP structured content and JSON text. Jev may round probabilities to two decimal places, so their sum can differ slightly from 1. The server allows the corresponding rounding margin (up to 0.005 per option) and preserves the returned values without normalization. Choice supports 1–255 named options. Instructions and descriptions may be strings, JSON objects, or arrays; choice descriptions may also be null. Noul accepts optional criteria.true and criteria.false descriptions.
Supply relevant facts, source text, and policies explicitly: Jev cannot see the caller's conversation or local files. Write complete judgments in instructions; IDs are only response keys. Include a no-match option when appropriate. Questions in one batch cannot see one another's answers. A noul near 0.5 is uncertainty about yes/no, not medium intensity. Confidence does not authorize actions or guarantee correctness; evaluate thresholds on representative data.
Troubleshooting
If the tool does not appear after an update, restart the server and open a fresh client session. The configured server is named jev; its tool is jev_decide.
Service/configuration failures are MCP tool errors (isError: true), never invented decisions. Codes include MISSING_API_KEY, INVALID_INPUT, INVALID_RESPONSE, API_ERROR, CONNECTION_ERROR, TIMEOUT, and CANCELLED. Invalid arguments may also be rejected directly by the MCP SDK.
References
Available Tools
1 tooljev_decideAsk Jev for typed decisionsARead-only
Evaluate supplied context using TypeSafe Jev. Batch independent, narrow questions in one call: choice selects a provided label; score returns a position on 2–10 ordered levels (0-based); noul returns probability of yes, not intensity. Supply relevant evidence and full instructions; question IDs are not sent to the model. Include an other/none choice when appropriate. Questions cannot use each other's answers. Returns raw judgments, probabilities, confidence for choice/score, and token usage. Use caller-defined policies for uncertainty. This sends the supplied state and questions to TypeSafe and may incur API charges. It does not execute selected actions.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Optional TypeSafe model override; defaults to the configured model or jev-latest. | |
| state | Yes | Relevant source text or JSON context; include evidence, definitions and policies needed for the questions. | |
| questions | Yes | Named independent questions over the same state. IDs are not model instructions; write each full judgment in instructions. |
Output Schema
| Name | Required | Description |
|---|---|---|
| model | Yes | |
| usage | Yes | |
| answers | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Even though annotations already declare readOnlyHint=true and destructiveHint=false, the description adds valuable behavioral context: question IDs are not sent to the model, the call sends supplied state to TypeSafe, may incur API charges, and does not execute selected actions. It also discloses what the returns contain (raw judgments, probabilities, confidence, token usage). No contradiction with annotations exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense but every sentence earns its place: type semantics, batching guidance, parameter intent, behavioral caveats, returns, and cost/non-execution notes are all present. It is front-loaded with the core purpose and then progressively adds usage and safety detail. There is no filler or tautology.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complex nested schema and the presence of an output schema, the description covers everything an agent needs: how to define each question type, what to put in state, which constraints apply (independence, other/none choice), what the call returns, and that it does not execute actions. The absence of sibling tools removes the need for differentiation, and return-value details are covered by the output schema. Nothing critical is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is already strong, but the description adds meaning beyond the schema. It explains that state should contain evidence, definitions, and policies; that questions are named and independent; and that noul returns probability of yes rather than intensity. It also clarifies that 'score returns a position on 2–10 ordered levels (0-based)', which the schema alone does not convey. This materially helps an agent construct valid inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb-plus-resource ('Evaluate supplied context using TypeSafe Jev') and then enumerates the three decision types (choice, score, noul) with their distinct semantics. It clearly differentiates the tool's function from any generic decision helper, and the title aligns with the behavior. No ambiguity remains about what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit usage guidance: batch independent, narrow questions in one call, include evidence and full instructions, add an other/none choice when appropriate, and use caller-defined policies for uncertainty. It also states an exclusion—questions cannot use each other's answers—which tells an agent when not to combine or chain questions. This is actionable and specific.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v0.1.0- First observed
jev_decide
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion between tools. The tool's internal modes (choice, score, noul) are clearly delineated within the description, so an agent can unambiguously select this tool for any decision-related task.
The single tool name 'jev_decide' follows a clear prefix-verb pattern (server name + action). Since there is only one tool, there is no inconsistency to evaluate; the naming is predictable and matches the server's purpose.
One tool is slightly thin by general guidelines, but this server is highly specialized for decision-making. The tool consolidates multiple decision types into a single callable interface, making a larger tool count unnecessary. The count is reasonable for the narrow scope.
The tool comprehensively covers the decision-making domain: choice selection, scoring on ordered levels, and probability of a yes outcome. It also provides confidence metrics, token usage, and handles batching of independent questions. There are no obvious missing operations for a decision-support tool, as it explicitly does not execute actions.
Maintenance
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