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list_activities

Retrieve work history for a coding session to view conversation logs and actions taken by the AI assistant, with pagination support for managing large datasets.

Instructions

List activities (work history) for a session.

Activities represent individual work units within a session, showing the conversation and actions taken by Jules.

Args: session_name: Resource name of the session (e.g., "sessions/abc123") page_size: Number of activities to return (1-100, default 50) page_token: Token for pagination from previous response

Returns: Dictionary with activities list and optional nextPageToken

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_nameYes
page_sizeNo
page_tokenNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses that the tool returns paginated results (via page_token) and default behavior (page_size default 50), which are useful behavioral traits. However, it doesn't cover other aspects like rate limits, authentication needs, error conditions, or whether it's read-only (implied but not explicit).

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 well-structured with a clear purpose statement, parameter explanations, and return value note. It uses bullet-like sections (Args, Returns) for readability. Some minor verbosity exists (e.g., 'Activities represent...' could be tighter), but overall it's efficient and front-loaded.

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?

Given 3 parameters with 0% schema coverage and an output schema present, the description does a good job explaining parameter semantics and the return structure. It covers the core functionality adequately for a list operation, though it could benefit from more behavioral context (e.g., error cases) since annotations are absent.

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%, so the description must compensate. It adds meaningful context for all three parameters: session_name is clarified as a 'Resource name' with an example format, page_size specifies range (1-100) and default, and page_token explains its purpose for pagination. This goes beyond the bare schema, though it doesn't detail validation rules beyond the range.

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 clearly states the verb ('List') and resource ('activities for a session'), specifying that activities are 'individual work units within a session' showing 'conversation and actions taken by Jules'. This distinguishes it from sibling tools like list_sessions or list_sources by focusing on session-specific work history.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing session), exclusions, or comparisons with sibling tools like get_session or list_sessions, leaving the agent to infer usage context.

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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