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ASTRA Unified Research Lab MCP Server

ASTRA Unified Research Lab MCP Server

by christophejlegros-lgtm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

set_parameter

Modify a state parameter in the neuromorphic simulation pipeline by specifying its path and a new numeric value, enabling dynamic adjustment of simulation configurations.

Instructions

Modify State Parameter

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": {
      "path": "<path>",
      "value": "<value>"
    },
    "name": "set_parameter"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes
valueYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.0.0

TDQS

D1.8/5.0
Behavior2/5

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

With no annotations, the description must carry the full burden of disclosing side effects and expectations. "Modify" conveys mutation, but there is no information about whether changes persist, affect a running simulation, require reset, or return any confirmation.

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 phrase is short, but under-specification is not conciseness. A single vague clause omits essential context while adding no distinctive content, so the structure does not earn its place.

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?

Given no annotations, no output schema, and no parameter documentation, this description is far from complete. An agent cannot know which state parameter paths are valid, what value changes do, or what the result of the call is.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

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 for the undocumented path and value fields. It does not: it only repeats the generic modify/parameter idea and gives no hint about path format, value range, allowed values, or units.

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?

"Modify State Parameter" essentially restates the tool name set_parameter rather than adding specific meaning. It identifies a verb and resource but does not clarify what kind of state parameter is affected, leaving the tool indistinguishable from other state-related tools like simulation_control or snn_step.

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?

The description offers no guidance on when to use this tool versus siblings such as simulation_control, snn_step, or wm_train_step. There are no conditions, exclusions, or alternative tools mentioned, so an agent has no basis for routing to this tool.

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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