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JevGuard MCP Server

by seb4ez

jevguard_evaluate

Evaluates input state against question definitions using a deterministic pipeline that includes state pruning, closed-world escape injection, certainty calibration, and caching.

Instructions

Executes the deterministic JevGuard evaluation pipeline including state pruning, closed-world escape injection, certainty calibration, and 0-token caching.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoTarget model identifier (default: jev-latest).jev-latest
stateYesInput state payload to evaluate against criteria.
api_keyNoOptional TypeSafe AI API key (defaults to TYPESAFE_API_KEY environment variable).
timeoutNoHTTP request timeout in seconds (default: 30.0).
endpointNoUpstream API endpoint (default: https://api.typesafe.ai/v1/systemone).https://api.typesafe.ai/v1/systemone
questionsYesDictionary or list of question definitions (noul, score, choice).
bypass_cacheNoBypass deterministic cache lookup.
mock_answersNoOptional raw answers dictionary for testing or offline execution.
auto_inject_escapesNoAutomatically inject neutral escape alternatives into categorical choices.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are absent, so description must disclose behavior. It lists key pipeline steps (state pruning, escape injection, calibration, caching) giving a good sense of what happens. However, it doesn't mention that the tool may be deterministic and cache results (beyond '0-token caching'), nor does it note any potential side effects or side effects of bypassing cache. But the explicit pipeline components add transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence but packs a lot of specific terminology. It is front-loaded with the main action and lists key pipeline components efficiently. It is appropriately concise, though it might benefit from a brief example or more plain-language explanation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 9 parameters and a nested object (questions), the description provides a high-level overview but does not clarify the exact input format for 'questions' beyond the schema. It doesn't explain the return value or error handling. Since output schema is absent, the description should hint at what the result looks like, but it doesn't. Still, the components listed give a good sense of the pipeline.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so parameters are well-documented in schema. The description doesn't add much beyond the schema. However, it clarifies that 'questions' can be a dictionary or list and mentions 'escape injection' and 'caching' which relate to parameters like auto_inject_escapes and bypass_cache. But the schema already describes those.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool executes a 'deterministic JevGuard evaluation pipeline' with specific components (state pruning, closed-world escape injection, certainty calibration, 0-token caching). This gives a clear verb and resource, though it could be more specific about what 'evaluate' means (e.g., evaluating an AI system's state against criteria). It is distinguishable from siblings but not fully explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is the main evaluation entry point, but does not explicitly state when to use it vs. siblings (e.g., calibrate, prune_state, cache_fingerprint). It doesn't mention any prerequisites or exclusions. The sibling names suggest related utilities, but no direct routing is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.