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EDNA — On-Device Human Sensing for AI

edna.get_human_proof

Current human proof { confidence, classification } for this session.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "edna.get_human_proof"
  }
}

Input Schema

NameRequiredDescriptionDefault
sessionNoTest session id [a-zA-Z0-9_-]{1,64} — sensor bridge and AI client must use the SAME id. Omit to use the X-EDNA-Session header or the "default" session.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.6/5.0
Behavior2/5

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 not state that this is a read-only operation, what happens when no proof exists yet for the session, or whether the value is computed on demand or cached. It adds only the implicit notion of 'current' state, leaving safety profile and empty-state behavior undescribed.

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

Conciseness3/5

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

Extremely short with zero filler, but it is a sentence fragment rather than a structured statement, and the terseness crosses into under-specification instead of disciplined brevity.

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?

The tool is simple (zero required params) and the description does list the two returned fields, which partly compensates for the absent output schema. However, it does not explain what confidence and classification mean, their ranges or allowed values, or how to interpret a missing proof, which is the main interpretive gap an agent would face.

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% and the single session parameter is documented in full, including the header fallback and the 'default' session, so baseline 3 applies. The description adds no meaning beyond the schema for that parameter.

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

Purpose3/5

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

The fragment names the resource (human proof) and the scope ('for this session') and even names the two returned fields, so an agent can guess it reads the current proof state for a session. But there is no verb and no explicit differentiation from siblings like edna.submit_proof or edna.get_human_context; the read-vs-write distinction is inferred from the name rather than stated.

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?

No when-to-use guidance is given: it does not say whether to call this before submitting proof, after submitting, or how it relates to edna.get_human_context or edna.get_verification_info. The agent must infer call timing entirely from the tool name.

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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