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EDNA — On-Device Human Sensing for AI

by space.openos
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edna.get_human_context

Time-aware human context: dynamics, tempo trend, pauses, cautious indicators with limits.

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_context"
  }
}

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

C2.3/5.0
Behavior2/5

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

With no annotations, the description carries the full disclosure burden and largely drops it. It never states that the call is a read, what 'limits' actually bounds (confidence? data volume? cautious-indicator reliability?), whether an unavailable sensor degrades the result, or what the response shape looks like.

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?

It is a single short fragment with no padding, so length is fine. But the compression is achieved by stacking undefined jargon rather than by front-loading a clear claim, so brevity costs rather than buys clarity.

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

Completeness2/5

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

No output schema and no annotations mean the description must explain return content, and it does not. For a tool whose entire value is the shape of the returned human-context signals, and sitting in a crowded sibling set, this leaves the agent unable to judge fitness before calling.

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?

Only one optional parameter exists and the schema documents it at 100% coverage, including the session-id regex, the header fallback, and the 'default' behavior. The description adds nothing about session semantics, so the schema does all the work and baseline 3 applies.

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

Purpose2/5

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

The description is a noun-phrase inventory ('dynamics, tempo trend, pauses, cautious indicators with limits') with no verb and no defined terms. An agent cannot tell what this actually returns, nor how it differs from siblings such as edna.get_signal_vector, edna.get_live_brief, or edna.get_writing_process, which all sound like they surface the same kind of derived human-state data.

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 when-to-use statement, no exclusion, and no named alternative. 'Time-aware' hints at a temporal framing but gives no condition that would select this tool over the several other get_* context tools in the same family.

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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