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Glama

company_bank

Manage company bank records by inspecting or advancing their lifecycle, with proof verification and external effects.

Instructions

Inspect or advance bank through its scoped lifecycle; supplied proofs are verified and provider effects remain external.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nowNo
engineNo
operationNolist
entity_refNo
project_idYes
bundle_jsonYes
payload_jsonNo{}

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Addedv0.1.1

TDQS

C2.1/5.0
Behavior2/5

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

With no annotations, the description carries the full burden. It discloses two behavioral traits — 'supplied proofs are verified' and 'provider effects remain external' — but both are cryptically worded and hard to act on. It never states what 'advance' does to state, whether effects are reversible, what permissions are needed, or what happens on verification failure.

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

Conciseness3/5

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

The description is short and free of filler, but its brevity comes at the cost of clarity — 'scoped lifecycle' and 'provider effects remain external' are dense jargon packed into one sentence. It is concise in word count but not in meaning; key operational details were traded away for compactness.

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?

For a tool with 7 parameters, 0% schema coverage, no annotations, and a huge family of similar company_* siblings, this one-sentence description is severely incomplete. The output schema covers return values, but an agent cannot determine valid operations, what proofs to supply, what a 'bank' lifecycle is, or when to prefer this tool over company_reconcile_bank.

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 all 7 parameters, and it names none of them. 'Supplied proofs' vaguely hints that some payload carries proofs, but it is not tied to bundle_json, payload_json, or entity_ref. The set of legal values for 'operation' is never given, and project_id/now/engine remain entirely unexplained.

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 verb pair ('inspect or advance') and a resource ('bank') with a lifecycle concept, which is more than a tautology. However, 'bank' and 'scoped lifecycle' are undefined jargon, and the schema's 'operation' parameter (default 'list') doesn't align cleanly with the described inspect/advance verbs. Among ~600 siblings including company_reconcile_bank and dozens of other company_* tools, nothing distinguishes this tool's scope.

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?

There is no guidance whatsoever on when to use this tool versus any alternative. Given the enormous sibling set of company_* lifecycle tools (company_launch, company_bring_up, company_reconcile_bank, company_closure), the absence of any selection criteria or exclusions leaves the agent to guess.

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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