Dev & Engineering model-context-protocolagent-client-protocolcomputer-usetyped-decisionsconfidence-gatingopenai-compatible-apistdiotypescript

Jevbridge Decision Sidecar

Add typed decisions, confidence gates, and computer-use action selection to an existing LLM.

FollowAgents review · FARS-2.1
Use with care
62/ 100 5-point scale 3.1 / 5
1 2 3 4 5 6
Per-dimension scores and reasoning
1Trust14 / 29 · 2.4/5

CI explicitly grants read-only contents access, the package claims no runtime npm dependencies, and remote access is described as limited to user-configured TypeSafe or LLM services. However, no implementation files are supplied, so actual runtime privilege minimization cannot be confirmed and least_privilege receives only thin credit. The README describes confirm, escalate, abort, and destructive-action gating, but supplies neither gate thresholds nor implementation evidence, so user_confirmation is partial. SECURITY.md clearly says supplied state leaves the machine for configured services and that the heuristic backend stays local; the environment table also identifies provider selection, although provider retention, logging, and secondary use are not covered. Sensitive-data guidance warns against publishing credentials or customer source and provides private vulnerability reporting, but omits redaction, minimization, key rotation, and leak-response procedures. Zero runtime dependencies and a fixed temporary typechecker version reduce supply-chain exposure, while movable major-version Action tags and the absence of a lockfile, audit process, or update policy prevent full dependency-security credit. External effects are presented as decisions and gates rather than direct execution, with confirmation advised before destructive calls, but the actual boundary is not verifiable from these files. No transactional recovery, undo, or compensating-action design is documented, so rollback is absent. The license, acknowledgments, project links, and temporary publishing account are disclosed, but the repository account, intended organization, and copyright entity differ, and publisher identity is unverified, preventing full attribution credit.

2Reliability8 / 14 · 2.9/5

README, package.json, SECURITY.md, and CI are broadly consistent about Node 22, stdio protocols, environment variables, and the lack of runtime dependencies. The discrepancy between the current account and intended organization is disclosed, supporting good but not exhaustive self-consistency. Node 22 is the sole required platform, remote backends are optional, and a local heuristic fallback exists when keys are absent, so dependency availability is handled thoroughly. No exception types, exit codes, JSON-RPC errors, timeout or retry behavior, or user-facing diagnostic text is shown, so failure_messages scores zero; this deduction concerns missing static failure-message evidence, not absent execution results.

3Adaptability16 / 18 · 4.4/5

The material clearly covers support routing, destructive-command gating, context retention, GUI next-action selection, and MCP and ACP host scenarios. Capability boundaries are unusually explicit: Jev does not generate text, Jevbridge is a decision sidecar, GUI choices use a closed action set, and the heuristic is expressly unsuitable as a safety signal. The backend:auto selection order and environment triggers are documented, but exact thresholds and conflict handling for execute, confirm, escalate, and abort are omitted, limiting trigger precision. Configuration examples span Node, CLI, library use, Claude Desktop, Codex, Cursor, OpenCode, and Zed, with an offline trial path, providing strong environment-fit evidence.

4Convention13 / 18 · 3.6/5

The README has a useful contents structure, distinct sections, capability tables, and protocol explanations; prerequisites and installation steps are directly usable. Names are mostly consistent across the CLI, package, and tools, but the package is unpublished and the repository remains under gamesonrblx while targeting tacticocc, creating transitional naming and distribution instability. Examples cover library, CLI, MCP, ACP, and computer use, but there is no dedicated FAQ or systematic troubleshooting guide. The heuristic bias and unfinished session resume, upstream proxy, publication, and transfer work are disclosed, though security and protocol-compatibility limits are not consolidated. MIT metadata matches a complete LICENSE, justifying full license credit. Version 0.1.0 exists, but there is no changelog, release history, or compatibility policy. Maintenance paths include Tactico contact details, Issues, and Security Advisories, yet no verified maintainer, response commitment, or explicit update owner is established, so maintenance responsibility remains thin.

5Effectiveness7 / 13 · 2.7/5

Typed noul, choice, and score answers, gate actions, and named protocol tools make the intended outputs reasonably consumable, and examples show branching usage. Full response and error schemas and compatibility guarantees are absent, preventing maximum output-usability credit. A typed fast-decision sidecar has a plausible and clearly articulated marginal role, especially for routing and closed-set GUI actions, but no benchmark, comparison, or outcome data substantiates superiority. The files only suggest reducing frontier-model token use and provide an offline fallback; they do not quantify latency, API expense, extra calls, accuracy tradeoffs, or operational cost, so cost_benefit is weakly supported.

6Verifiability4 / 8 · 2.5/5

Basic metadata is traceable across package.json, LICENSE, SECURITY.md, and CI, but the core capability, gating, protocol-compatibility, and testing claims lack the referenced implementation and test files, leaving claim traceability low. Node requirements, licensing, repository location, dependency shape, and outbound-data behavior receive useful cross-file corroboration. The README distinguishes completed roadmap items, future work, heuristic limitations, and the planned repository transfer, but broad language such as “any LLM” exceeds the specific adapters evidenced, so fact/inference separation is good rather than complete.

Evidence confidence: Low Reviewed Sep 20, 2026 Reviewed revision 84fcdc30e69a
Safety controls not found in source: rollback or recovery path
Before you use it
  • Treat state sent to a remote backend as having left the machine; do not include credentials, customer source, or data the selected provider is not authorized to process.
  • Do not treat heuristic scores as safety approval. The README explicitly warns that question-text overlap can produce high-risk scores for harmless input.
  • Hosts should enforce independent confirmation for paid, destructive, or submission actions; the supplied material describes gating intent but does not prove its implementation or default thresholds.
  • Before adopting the MCP or ACP integration, inspect the missing error, timeout, retry, JSON-RPC failure, and abnormal-exit behavior.
  • The package is not yet published and the repository is intended to move. Pin the reviewed revision and verify provenance separately until organizational identity, update ownership, and distribution are stable.
Review evidence [1][2][3][4][5]
See the full review method →

What does this agent do, and when should you use it?

Jevbridge is a zero-runtime-dependency Node.js 22 TypeScript library with MCP and ACP stdio adapters. It turns unstructured state into typed probabilistic noul, choice, and score answers while Codex, Claude, Grok, OpenCode, or an OpenAI-compatible model continues generating plans and explanations. A shared question format can run against native TypeSafe Jev, an LLM adapter, or a local heuristic backend. Its confidence gate returns execute, confirm, escalate, or abort decisions for tool calls and potentially destructive interface actions. It can be self-hosted as a library, CLI, MCP server, or ACP sidecar, although upstream ACP-agent proxying, session resume, and the planned npm package are not yet complete.

evaluate reads one state plus a set of questions created with noul, choice, and score, then sends them to the jev, llm, heuristic, or auto backend. auto prefers TypeSafe Jev when TYPESAFE_API_KEY is present, otherwise uses a configured LLM adapter, and finally falls back to a local keyword scorer when no key is available. gate inspects the answers and returns execute, confirm, escalate, or abort according to confidence and destructiveness. The MCP stdio server exposes jev_decide, jev_gate, jev_computer_use, and jev_recipe, along with recipe resources; the ACP stdio adapter uses Content-Length framing for initialize, session/new, and session/prompt. For computer use, computerUseQuestions, observationState, and readAction select from the closed set click, type, scroll, wait, screenshot, done, and abort while evaluating targets, safety, destructiveness, and goal progress.

  1. An engineering team using Codex, Claude, or another generator needs support routing, intent checks, and urgency assessments returned as values that application code can branch on.
  2. An MCP workflow developer wants confirmation, escalation, or cancellation before tools delete data, spend money, or submit a form.
  3. A GUI automation team needs to select a next step from a fixed action set while checking destructiveness separately from action confidence.
  4. A developer using Zed, JetBrains, or another ACP host wants a stdio decision sidecar that classifies prompts and emits gated updates.
  5. A CI or test maintainer needs an offline heuristic backend for smoke tests without provisioning external API credentials.
  6. A support-platform developer wants to assess refund intent, owning team, and urgency from the same customer request.

What are this agent's strengths and limitations?

Pros
  • One noul, choice, and score schema works across TypeSafe Jev, several LLM providers, and the offline heuristic backend.
  • Library, CLI, MCP stdio, and ACP stdio interfaces support both in-process integration and a separate decision-sidecar deployment.
  • Destructiveness is gated independently from action confidence, so a highly confident refund or payment click can still require confirmation.
  • The heuristic backend enables offline tests and CI smoke checks without external credentials.
  • Documented configuration covers OpenAI, Anthropic, xAI, OpenCode, Codex, and generic OpenAI-compatible endpoints.
Limitations
  • It requires Node.js 22 or newer, and the source-based commands rely on --experimental-strip-types.
  • The heuristic scorer includes question text in its evidence and can produce inflated scores from word overlap; it is explicitly unsuitable as a safety signal.
  • Native Jev and hosted LLM modes require provider credentials and network access, with quality and cost depending on the selected service.
  • Proxying an upstream ACP agent and intercepting its tool calls, plus session load and resume, remain roadmap items.
  • The planned @tacticocc/jevbridge npm package is not published, so the documented setup currently clones and runs the repository source.
  • The material references both tacticocc/Jevbridge and gamesonrblx/Jevbridge and says an organization transfer is pending, creating a potential repository-location migration.

How do you install or deploy this agent?

Install Node.js 22 or newer and Git, then run:

git clone https://github.com/gamesonrblx/Jevbridge.git
cd Jevbridge
npm install

The project has no runtime npm dependencies. A first offline invocation requires no credentials:

node --experimental-strip-types src/cli.ts eval support-route

For native Jev, export TYPESAFE_API_KEY. For an LLM backend, set JEVBRIDGE_LLM and the applicable XAI_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY, or OPENCODE_API_KEY. JEVBRIDGE_LLM_MODEL and JEVBRIDGE_BASE_URL can override the model and OpenAI-compatible endpoint.

How do you use this agent?

List the built-in recipes:

node --experimental-strip-types src/cli.ts recipes

Run the computer-use recipe:

node --experimental-strip-types src/cli.ts eval computer-use

Start either stdio adapter:

node --experimental-strip-types src/cli.ts mcp
node --experimental-strip-types src/cli.ts acp

The wrapper can run a recipe as well:

node bin/jevbridge.mjs eval destructive-gate

For library use, import evaluate, gate, noul, choice, and score from ./src/index.ts, pass state, questions, and backend to evaluate, then apply gate to result.answers. In an MCP host, configure node with --experimental-strip-types, the absolute path to src/cli.ts, and mcp. Call jev_gate before a destructive tool and use jev_computer_use for closed-set GUI action selection.

How does this agent compare with similar options?

Compared with asking a generative LLM to produce prose and make every operational decision, Jevbridge constrains decisions to noul, choice, and score outputs and applies a separate confidence gate. Native TypeSafe Jev is the preferred backend, while Codex, Claude, Grok, OpenCode, or a generic OpenAI-compatible model can provide the System One role without a TypeSafe key. The heuristic backend works offline but is intended only for tests, not as a substitute for safety-sensitive model decisions.

FAQ

Can it run without a TypeSafe API key?
Yes. auto can use a configured LLM provider, or fall back to the local heuristic backend when no provider key is available.
Is the offline mode suitable for guarding payments or deletions?
No. The heuristic scorer can be distorted by words in the question itself, and the project describes its output as a smoke test rather than a safety signal.
Is MCP mandatory?
No. Jevbridge can also run as a TypeScript library, CLI, or ACP stdio adapter; MCP is one supported delivery mode.
What access does a deployment need?
The basic setup executes Node.js and reads local source and configuration paths. The heuristic mode can stay offline, while TypeSafe Jev and hosted LLM adapters need network access and the corresponding API credentials.
Does it replace Codex or Claude as the generating agent?
No. The generating model remains responsible for plans and explanations; Jevbridge supplies typed decisions and gated actions that software can consume.

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