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jev-mcp

by jkudish
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JavaScript
Remote

Classify items against a shared label set

jev_classify

Assign items to one class from a shared catalog in one batched request, returning confidence, margin, and auto-review flags for uncertain cases.

Instructions

Assign each item to one class from a shared catalog with TypeSafe Jev, in one batched request: the class catalog is sent once and every item becomes an independent Choice question. Returns per item: the chosen class, the full distribution, confidence, winner-to-runner-up margin, and an auto-versus-review decision. Auto requires both a high top probability (default 0.85) and a clear margin (default 0.50); everything else is flagged for review. Include a manual_review class in the catalog if you want an explicit escape hatch; the tool never invents one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYesItems to classify. Text is truncated at 2000 characters; send bounded excerpts, not whole documents.
classesYesShared class catalog. Strong descriptions carry the decision: a precise definition, what belongs, what does not, precedence over overlapping classes, and a short example.
contextNoShared context available to every item's judgment: policies, catalogs, anything stable.
purposeNoWhat this classification is for; shared across all items.
auto_acceptNoMinimum top probability for auto. Default 0.85.
minimum_marginNoMinimum winner-to-runner-up gap for auto. Default 0.5.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.10.1
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. Changed1 schema field changedv0.9.0
    • changedInput schema / properties / context / anyOf
      Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "additionalProperties": {},
-    "type": "object"
-  }
-]New value: +[
+  {
+    "type": "string"
+  },
+  {
+    "additionalProperties": {},
+    "propertyNames": {
+      "type": "string"
+    },
+    "type": "object"
+  }
+]
  3. Addedv0.4.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does well: it discloses the return fields (chosen class, distribution, confidence, margin, auto/review), the two default thresholds, and that the tool never fabricates a manual_review class. It omits cost/latency characteristics of a batched model call and any concurrency/pagination behavior, which keeps it short of a 5.

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

Conciseness5/5

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

Four sentences, front-loaded with the core purpose and batching model, then returns, then the decision rule, then the escape-hatch caveat. No sentence is filler and each adds a distinct fact.

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

Completeness4/5

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

No output schema exists, so the description correctly enumerates the returned fields, and with no annotations it covers the safety/decision behavior. It does not mention item-count or class-count ceilings (64/250) or the 2000-character truncation, but those are documented in the schema, leaving only minor gaps.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds real meaning beyond the schema by explaining how auto_accept and minimum_margin combine (both a high top probability AND a clear margin are required) and by framing the semantics of the classes catalog as shared, per-item Choice questions.

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

Purpose5/5

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

States a specific verb and resource ('assign each item to one class from a shared catalog') plus the mechanism ('one batched request', 'class catalog is sent once'). This is clearly distinguishable from siblings like jev_rerank, jev_verify, or jev_decide, which do different operations on the same inputs.

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?

Usage context is implied by the auto/manual-review mechanics and the note about including a manual_review class, which is genuinely actionable guidance. However, there is no explicit when-to-use-this-vs-a-sibling statement (e.g. versus jev_verify or jev_decide), so the agent must infer routing.

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

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