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ninetails-io

gnucash-mcp

list_jobs

Retrieve GnuCash jobs with filters for customer/vendor, active-only, and pagination options. Get compact rows or full JSON details.

Instructions

List jobs, optionally filtered.

Leads with a Showing X-Y of Z jobs line; page with offset, or pass limit=0 for the count only.

Args: owner_type: Filter by "customer" or "vendor". Omit for all. owner_id: Filter by specific customer or vendor ID (requires owner_type). active_only: If True (default), exclude inactive jobs. verbose: If True, return full JSON dicts; otherwise compact tab-separated rows. limit: Page size (default 50, max 250). 0 = count only. offset: 0-indexed first row to return (default 0).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
offsetNo
verboseNo
owner_idNo
owner_typeNo
active_onlyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries full burden. It documents several behavioral details: leads with a count line, compact rows vs full JSON dicts based on verbose, default active_only=True behavior, and pagination semantics. This adds significant value beyond the schema.

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 well-organized with an intro line followed by a bulleted Args section covering all 6 parameters. Every sentence earns its place—no fluff or redundant information. The structure makes key behaviors (count line, pagination) front-and-center.

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

Completeness4/5

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

There is an output schema present, so return values need less explanation. The description covers the key behaviors: count line, pagination, filters, output formats. It's adequate for an optional-filter list tool, though it could note how verbose=False rows differ exactly (tsv structure) without relying on the output schema.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must fully compensate. It does: every parameter is explained with its values and defaults (owner_type filter options, owner_id prerequisite, active_only default, verbose output format, limit page size/max/count semantics, offset indexing). This is comprehensive.

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 'List jobs, optionally filtered' using a specific verb+resource. It doesn't explicitly distinguish from sibling tools like get_job or get_job_report, but the plural 'list' naming and description make the list-vs-get distinction obvious from context.

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?

The description provides clear guidance on how to use the tool: page with offset, pass limit=0 for count-only, filter by owner_type/owner_id, active_only behavior. It doesn't explicitly state when NOT to use this tool or offer alternatives, but the usage patterns are clearly documented.

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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