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lightbulb-mcp

lightbulb-mcp

by RPasquale
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

solver_assign_resources

Destructive

Assign resources via the solver domain agent, routing through the dispatcher under your JWT, tenant, and company scope.

Instructions

Run the solver domain agent action assign_resources.

Routes through the platform's domain-agent dispatcher under your JWT, tenant, and company scope.

Args: message: Free-text objective for the action. inputs: Optional JSON string of structured inputs for the action.

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": "solver_assign_resources"
  }
}

Input Schema

NameRequiredDescriptionDefault
inputsNo{}
messageNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.8/5.0
Behavior3/5

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

Annotations already cover readOnly/destructive hints, so the description doesn't need to repeat them. It does add useful behavioral context by stating the action routes through the domain-agent dispatcher under JWT, tenant, and company scope, but it says nothing about side effects or open-world behavior beyond what annotations imply.

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

Conciseness4/5

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

The description is short and front-loaded, with the routing/scope sentence and an Args block adding value without padding. The first line is somewhat redundant with the tool name, but overall the structure is clean and efficient.

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?

The output schema and annotations cover returns and safety, but the description still omits the core domain semantics: what assign_resources does, when to call it, and how to structure inputs. For a tool in a large family of solver/domain actions, this is a significant completeness gap.

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?

With 0% schema description coverage, the description is the only documentation for the two parameters. It correctly labels message as a free-text objective and inputs as an optional JSON string, but it doesn't specify what keys or values the structured inputs expect, so an agent still has to guess for non-trivial calls.

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 action ('solver domain agent action assign_resources') and adds routing context, so it's not a pure restatement of the name. However, it never explains what 'assign_resources' actually accomplishes, leaving an agent to infer the tool's purpose from the name alone.

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 provided. The description does not reference solver_chat, solver_solve_optimization, dispatch_domain_agent, or any other alternative, and gives no conditions for choosing this tool over its many siblings.

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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