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battery-erp

battery-erp

by icohangar-ops
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

record_bin_confirmation

Log a human-verified on-hand quantity for a part in battery ERP, correcting inventory records after validating the confirmation token.

Instructions

Record a human-confirmed on-hand quantity. Requires auth_token matching BATTERY_ERP_CONFIRM_TOKEN.

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": {
      "part_number": "<part_number>",
      "actual_quantity": "<actual_quantity>",
      "auth_token": "<auth_token>"
    },
    "name": "record_bin_confirmation"
  }
}

Input Schema

NameRequiredDescriptionDefault
actorNomcp-operator
notesNo
auth_tokenYes
request_idNo
part_numberYes
actual_quantityYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations present, the description must carry the full burden of behavioral disclosure. It reveals an auth requirement (auth_token matching BATTERY_ERP_CONFIRM_TOKEN) but does not disclose side effects — whether it updates inventory, overrides existing quantities, or has any other impact. The word 'record' implies a write, but the consequences are undefined.

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 extremely concise and front-loaded: purpose first, then auth requirement. Both sentences earn their place with no filler. However, it is so sparse that it borders on under-specification, but that is more a completeness issue than a conciseness flaw.

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?

For a write operation with no annotations and no output schema, this description is incomplete. It does not explain what happens upon success/failure, whether part_number must exist, the expected format of actual_quantity, or how this confirmation integrates with the inventory workflow. Agents lack essential information to call this tool reliably.

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

Parameters2/5

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 param meaning. It only explains auth_token (must match the environment token) and implicitly links actual_quantity to 'on-hand quantity', but part_number, actor, notes, and request_id are completely unexplained. This is insufficient given the number of parameters.

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

Purpose5/5

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

The description states a specific action ('Record') and a specific resource ('a human-confirmed on-hand quantity'), which clearly distinguishes this from sibling read/lookup tools and from create_bin_check_request. The verb+object combination is unambiguous and informative.

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 provides no guidance on when to use this tool versus alternatives. It does not mention typical trigger conditions (e.g., after a manual count) or contrast with create_bin_check_request. Agents must infer usage from the name and purpose alone.

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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