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daf-jev

daf-jev

by docxology
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

jev_ask

Submit named questions about a single state in one API call to get typed answers—noul, choice, or score—along with usage and request ID details.

Instructions

Ask named questions about one state in a single API call.

questions maps question id to either a CLI ask-spec string (noul:<instructions>, choice:<instructions>:k=desc,..., score:<instructions>:l1,l2,...) or a native {type, instructions, criteria} dict. Returns {model, answers: {id: answer dict with type}, usage, request_id}; request_id is omitted when the API did not return one. provider selects the registered API provider (default jev).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "state": "<state>",
      "questions": "<questions>"
    },
    "name": "jev_ask"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNo
stateYes
providerNojev
questionsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.2

TDQS

B3.2/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full behavioral burden. It does well on the output side, spelling out the return payload (model, answers, usage, request_id) and the fact that request_id is omitted when absent, plus the provider default. It says nothing about cost, permissions, model defaults, or whether the call mutates state.

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 purpose sentence is front-loaded and the remaining lines are dense but informational, with no filler. The heavy backtick formatting makes it slightly harder to read than plain prose, but nothing is redundant.

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?

For a four-parameter tool with nested objects, no output schema, and zero schema description coverage, the definition covers the trickiest parameter and the return shape well. It falls short on two of four parameters and on any indication of when this tool is the right choice among its many siblings.

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 0%, so the description must compensate. It does a genuinely useful job on `questions`, enumerating the ask-spec string forms and the native dict form, and it notes `provider`'s default. However, `state` (which accepts string, array, or object) and `model` are left entirely unexplained.

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 first sentence gives a concrete verb and resource: asking named questions against one state in a single API call. That is specific enough to distinguish it as a batched question-answering tool, but it never names a sibling such as jev_evaluate or jev_reask_plan to clarify boundaries.

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

Usage Guidelines2/5

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

There is no explicit when-to-use or when-not-to-use guidance. The phrase 'in a single API call' hints that batching is the point, but nothing tells the agent when to choose this over jev_evaluate or jev_reask_plan, or what preconditions exist.

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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