ASTRA Unified Research Lab MCP Server

sensor_olfactory

Simulates olfactory chemoreceptor responses to compounds, generating latent encodings for neuromorphic sensing.

Instructions

Koniku Kore Olfactory Encoding (Chemoreceptor → Latent)

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": {
      "input": "<input>"
    },
    "name": "sensor_olfactory"
  }
}

Input Schema

Name	Required	Description	Default
`input`	Yes	Olfactory sensor parameters

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.0.0Aug 17, 2026

TDQS

C2.1/5.0

Behavior1/5

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

There are no annotations, so the description carries the full burden. It only states a conceptual pipeline ('Chemoreceptor → Latent'), with no mention of side effects, return value, simulation state changes, or requirements such as a running platform. This is too opaque to predict tool behavior.

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

Conciseness2/5

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

The description is extremely short, which is good for speed, but it is under-specified rather than concise. It omits core behavioral information that a few extra sentences could safely supply, so brevity costs clarity.

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

Completeness1/5

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

For a tool with a nested input object, no output schema, and zero annotations, the one-phrase description leaves almost everything unknown: input semantics, output shape, and operational side effects. It is inadequate for an agent to decide whether and how to invoke this tool correctly.

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 coverage is 100% and all subparameters have explicit types, defaults, enums, and ranges, so the baseline is 3. The description adds only the general chemoreceptor→latent framing and does not explain how individual parameters affect the encoding, but compensation is not required given full schema coverage.

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 description names a specific capability (olfactory encoding via Koniku Kore, chemoreceptor → latent) and therefore distinguishes itself from the visual/audio/fuse/process/status siblings. However, it is a noun-phrase title rather than a verb+resource sentence, so it does not explicitly state what action the tool performs or what it returns.

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 sentence explains when to use this tool versus sibling sensor tools. It implies olfactory use through the word 'olfactory', but offers no exclusions, prerequisites, or alternative routing. The agent must infer usage from the name alone.

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

Deploy Server