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ScrumDoLLC

scrumdo-mcp

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by ScrumDoLLC

log_activity

Record machine-parseable activity log entries to ScrumDo cards. Specify action, detail, agent, user, and environment for filtering and human-readable updates.

Instructions

Write a structured activity log entry to a card.

Entries are stored as machine-parseable comments and rendered in the ScrumDo UI as human-readable updates. They are filterable via get_activity_log().

Args: card_ref: Card reference, e.g. 'ON-914'. action: Short action label, e.g. 'started', 'deployed', 'reviewed', 'snapshot', 'seeded', 'PR opened'. detail: Longer description or context (markdown). agent: Agent/bot name writing the log (e.g. 'claude-code', 'codex', 'github-actions'). user: Human user involved (username or email). environment: Environment name (stage, integration, branch). milestone: Milestone or release tag, if applicable. task_ref: Related task or sub-item reference. extra: Any additional structured data to attach.

Returns the created comment object.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
card_refYes
actionYes
detailNo
agentNo
userNo
environmentNo
milestoneNo
task_refNo
extraNo
Behavior4/5

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

No annotations provided, so description carries full burden. It clearly states entries are stored as machine-parseable comments, rendered in UI, and filterable. It also documents side effects by specifying it returns the created comment object. However, it does not mention required permissions or other behavioral traits.

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 header line and parameter list, but it includes some redundancy (e.g., 'Args:' could be omitted). Still, it earns its sentences and is front-loaded with the core purpose.

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

Completeness5/5

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

Given 9 parameters, 2 required, no output schema, and no annotations, the description is comprehensive. It explains what the tool does, how data is stored, and specifies return value. It also provides example values for the 'action' parameter, making it actionable.

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% (no parameter descriptions in schema). The description compensates fully by providing clear, detailed explanations and examples for each parameter, especially 'action' and 'detail', adding significant meaning beyond the 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 uses specific verbs ('Write') and resources ('structured activity log entry to a card') and distinguishes from siblings like 'add_comment' by noting that entries are machine-parseable and filterable via 'get_activity_log'.

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

Usage Guidelines3/5

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

The description implies use for activity logging with examples for 'action' parameter but does not explicitly state when to use this tool over alternatives like 'add_comment' or when not to use it.

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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