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lightbulb-mcp

lightbulb-mcp

by RPasquale
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

record_project_policy_assignment

Log policy assignment propensities before outcomes exist. Confirm after verifying action alternatives, probabilities, and decision-time source to create a receipt that never authorizes execution.

Instructions

Log exact policy propensities before an outcome exists.

Set confirm_record=true only after checking the action alternatives, behavior/candidate probabilities, and decision-time source. This receipt never authorizes execution, learning, skill updates, or policy promotion.

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": {
      "project_id": "<project_id>",
      "metric_id": "<metric_id>",
      "direction": "<direction>",
      "chosen_action_id": "<chosen_action_id>",
      "actions_json": "<actions_json>",
      "behavior_policy_json": "<behavior_policy_json>",
      "candidate_policies_json": "<candidate_policies_json>"
    },
    "name": "record_project_policy_assignment"
  }
}

Input Schema

NameRequiredDescriptionDefault
noteNo
unitNo
directionYes
metric_idYes
decided_atNo
links_jsonNo{}
project_idYes
actions_jsonYes
assignment_idNo
confirm_recordNo
source_event_idNo
chosen_action_idYes
evidence_refs_jsonNo[]
behavior_policy_jsonYes
candidate_policies_jsonYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.1

TDQS

A3.9/5.0
Behavior4/5

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

The description adds meaningful behavioral context beyond the annotations by clarifying that the receipt never authorizes downstream actions and is only a pre-outcome log. However, it doesn't explain what happens when confirm_record is false (e.g., preview vs no-op), which is a minor transparency gap.

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?

Three sentences, front-loaded with the primary purpose, followed by a usage condition and a caveat. There is no filler, and the backticked parameter plus short sentence breaks make it scannable.

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?

For a 15-parameter tool with zero schema descriptions, the description covers the core concept and the confirm flag but leaves the JSON payload formats and several required parameters unexplained. The presence of an output schema helps with return values, but an agent would still need domain knowledge to populate all required fields 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?

The description maps to key parameters: 'action alternatives' suggests actions_json, 'behavior/candidate probabilities' suggests behavior_policy_json and candidate_policies_json, and 'decision-time source' relates to source_event_id/decided_at. With 0% schema description coverage, this is helpful, but 15 parameters exist and most, including project_id, metric_id, direction, chosen_action_id, evidence_refs_json, and links_json, receive no semantic explanation.

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 opens with 'Log exact policy propensities before an outcome exists,' which names a specific verb, resource, and timing. It clearly distinguishes this as a pre-outcome logging action, though it doesn't explicitly name sibling tools for differentiation.

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

Usage Guidelines4/5

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

It gives a concrete precondition: set confirm_record=true only after checking action alternatives, behavior/candidate probabilities, and decision-time source. It also states a when-not boundary ('never authorizes execution, learning, skill updates, or policy promotion'), though it doesn't point to alternative tools.

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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