Skip to main content
Glama
christophejlegros-lgtm

ASTRA Unified Research Lab MCP Server

sensor_olfactory

Simulate olfactory sensor responses by encoding chemical compounds into latent receptor patterns. Adjust compounds, concentrations, and temporal phase to model bio-hybrid neuromorphic olfaction.

Instructions

Koniku Kore Olfactory Encoding (Chemoreceptor → Latent)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesOlfactory sensor parameters
Behavior2/5

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

With no annotations provided, the description must carry the full behavioral disclosure burden. It only hints at a chemoreceptor-to-latent encoding flow but does not mention simulation behavior, output format, side effects, or whether this invokes hardware or software processing.

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 and free of fluff, but it is under-specified rather than concise. It presents a title-like fragment with no structured functional explanation, so brevity does not add value.

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?

The tool has a rich nested parameter schema, no output schema, and no annotations. The description fails to explain what the output is, how the parameters affect behavior, or what the latent encoding represents. It is fundamentally incomplete for an AI agent to select and 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 description coverage is 100% for the single 'input' parameter, and nested properties are fully defined with names, types, defaults, and constraints. The description adds no additional parameter semantics, but since the schema already does the heavy lifting, a baseline score of 3 is appropriate.

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 identifies the resource as olfactory encoding ('Chemoreceptor → Latent') and vaguely implies a transformation, but lacks a clear verb such as 'generates' or 'encodes'. It is not a tautology, but it does not state the tool's function in an actionable way.

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 guidance on when to use this tool versus the many sibling sensor tools (e.g., sensor_visual, sensor_audio, sensor_fuse). The description provides no context, prerequisites, or alternative tool references.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/christophejlegros-lgtm/ASTRA-Unified-ResearchLab-MCP-v2.4'

If you have feedback or need assistance with the MCP directory API, please join our Discord server