gl_entries

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

With no annotations provided, the description fully carries the behavioral burden. It exposes that cancelled GL is always excluded, that truncation is explicit via truncated:true, that limit is clamped to 1..5000 with 0 falling back to 500, and that the operation is read-only. This is exceptional disclosure of edge-case behavior.

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

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

The description is dense but every sentence earns its place: core purpose, cancellation behavior, truncation semantics, limit rules, pagination advice, and read-only status. The most important scoping information is front-loaded.

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

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

Given seven parameters, no annotations, and no output schema, the description still gives an agent everything needed to invoke the tool correctly: filters, date-range boundaries, limit behavior, cancellation exclusion, truncation signaling, and pagination guidance. No critical call-blocking detail is missing.

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

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must add meaning beyond the raw parameter names. It does so thoroughly: it explains how account, party, voucher_no, and voucher_type act as optional filters, and it gives detailed semantics for limit including clamping, fallback behavior, and the deliberate contrast with Frappe's convention.

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

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

The description clearly identifies the tool as returning general-ledger rows for a date range with optional filters, so an agent understands what resource is being acted on. It does not explicitly differentiate from accounting-related siblings like financial_statement or ar_ap_summary, so it stops short of the top score.

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

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

The description gives clear operational context: it is a read-only date-range query with optional filters, and it instructs paginating by narrowing the date range when more rows exist. It does not name specific alternative tools or state when-not-to-use, but the usage context is sufficiently clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.