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accelo_list_object_budgets

List object budgets to track time and money spent against issues, milestones, jobs, and contract periods. Filter by ID, against type/ID, or order results.

Instructions

List object budgets from Accelo.

Object budgets track time and money spent against issues, milestones, jobs, and contract periods. Key fields include billable, nonbillable, logged, charged, service_price, material_price, and is_billable. Each budget is linked via against_type/against_id (e.g. against_type='milestone', against_id=15).

Args: filters: Filter dict. Keys: id, against_id, against_type, order_by_asc/desc (id) fields: Additional fields, e.g. "service_price_subtotal,material_cost" page: Page number (0-indexed) limit: Results per page (max 100)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
limitNo
fieldsNo
filtersNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden, and it does disclose pagination semantics (page is 0-indexed, limit max 100) plus the against_type/against_id linkage model. It says nothing about authorization requirements, rate limits, or the shape of the response, so coverage is partial.

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?

Front-loads the purpose, then the domain context, then a clean Args block. Every element is relevant and the paragraph-to-list structure aids scanning, with only mild redundancy between the field list and the filters example.

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?

For a read-only list tool with no annotations and no output schema, the description covers purpose, the linked-entity model, updatable field names, and every parameter. The one remaining gap is that it never describes the response envelope (pagination shape, total count) that an agent would need to page correctly.

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

Parameters4/5

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

Schema description coverage is 0%, yet the Args section documents all four parameters: filter keys (id, against_id, against_type, order_by_asc/desc), a concrete fields example, and page/limit semantics including the max of 100. This meaningfully compensates for the bare schema, though it omits defaults and the accepted values for against_type.

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?

States a specific verb and resource ('List object budgets') and adds a substantive explanation of what an object budget is and what it links to (issues, milestones, jobs, contract periods). This clearly separates it from accelo_get_object_budget and accelo_count_object_budgets, though the description never names those siblings explicitly.

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?

No when-to-use guidance is given. The description explains the resource but offers nothing about when to choose this list endpoint over accelo_get_object_budget, accelo_count_object_budgets, or the related material/service budget tools, leaving selection to inference.

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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