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accelo_get_activity_time_allocated

Retrieve aggregate time allocated across activities, returning summed billable/nonbillable seconds and total charged. Apply filters like staff or date ranges to scope the set.

Instructions

Get aggregate time allocated across a set of activities.

Returns the summed billable/nonbillable seconds and total charged for the activities matched by the filters. This is an AGGREGATION over a collection (endpoint GET /activities/allocations), NOT a single activity's allocation — pass the same filters accepted by accelo_list_activities to scope the set (e.g. {"staff": 14, "date_logged_after": 1690000000}).

Args: filters: Same filter dict as accelo_list_activities (staff, against_type, against_id, date_logged_before/after, etc.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filtersNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description bears the full burden. It discloses the aggregation nature, the return content (summed billable/nonbillable seconds and total charged), and the underlying endpoint, which is strong. It doesn't mention auth requirements, rate limits, or edge case behavior like empty result sets.

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 front-loaded with the core purpose, then behavior, then parameter details. It's slightly verbose but each sentence adds value. The parenthetical endpoint reference and example filter dict are useful, not filler.

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 single-param aggregation tool with no annotations and no output schema, the description covers purpose, scope, return value, filter semantics, and the endpoint. It's nearly complete, though it could mention what happens with no filters or how results are paginated/shaped.

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 coverage is 0% and the schema only defines a free-form object. The description compensates fully by explaining what filters means, that it accepts the same dict as accelo_list_activities, and lists example keys (staff, against_type, against_id, date_logged_before/after). This adds substantial meaning beyond the bare schema.

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 verb and resource ('Get aggregate time allocated across a set of activities'), and explicitly distinguishes itself from alternatives by saying it's an AGGREGATION over a collection, NOT a single activity's allocation, naming the endpoint GET /activities/allocations. This lets an agent differentiate it from accelo_get_activity and accelo_count_activities without opening schemas.

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?

It clearly states the context ('pass the same filters accepted by accelo_list_activities to scope the set') and names the sibling for filter semantics. It lacks explicit when-not-to-use guidance (e.g., vs. count_activities or list_activities) but the collection-vs-single distinction effectively narrows usage.

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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