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lightbulb-mcp

lightbulb-mcp

by RPasquale
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

software_delivery_context

Collects IT/Ops, workspace, deployment, and approval context before editing code. Use in Claude Code, Codex, or Cursor to ensure informed changes.

Instructions

Get the Lightbulb software-delivery context packet before editing code.

Use this from Claude Code, Codex, or Cursor before making a repo change. It gathers IT/Ops, coding workspace, project-management, deployment, CloudOps, connector, memory, and approval context under the authenticated user's RBAC scope.

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": "software_delivery_context"
  }
}

Input Schema

NameRequiredDescriptionDefault
repositoryNo
project_keyNo
include_liveNo
workspace_idNo
include_memoryNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It indicates a read operation ('Get') and mentions gathering context under RBAC scope, but does not explicitly state it is read-only, disclose side effects, or cover auth details beyond RBAC. It is adequate but minimal.

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

Conciseness5/5

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

The description is two sentences: first states purpose, second specifies usage scenarios and gathers what context. It is front-loaded, efficient, and contains no extraneous information.

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

Completeness3/5

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

Given the tool's complexity (5 parameters, output schema present), the description covers the general purpose and scope but lacks parameter guidance. The presence of an output schema partially compensates for missing return value details. It is minimally complete.

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?

The input schema has 5 parameters with 0% description coverage, and the tool description does not explain any parameter. Meaning and usage must be inferred entirely from names and defaults, which is insufficient for an agent to use the tool correctly.

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

Purpose4/5

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

The description clearly states the tool retrieves a 'Lightbulb software-delivery context packet' for use before editing code, using a specific verb ('Get') and resource. It lists the types of context gathered but does not differentiate from sibling tools like 'coding_get_context' or 'software_delivery_loop'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a usage context ('before making a repo change' from specific IDEs) but gives no guidance on when to prefer this tool over alternatives, nor any explicit exclusions. The timing is clear but incomplete.

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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