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log_activity

Record work activities such as decisions, bugs, and test results to build persistent project context across AI coding sessions.

Instructions

Record an important activity during work. Use for: decisions (architecture/implementation choices), bugs (issues discovered), todos (unfinished work), commands (significant commands run), test_result (test outcomes), error (errors encountered). Record outcomes, not reasoning. Be specific: 'Use PKCE for OAuth' not 'Made a decision'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryYesType of activity
metadataNoOptional additional data (e.g., {root_cause, fix} for bugs)
descriptionYesDescription of the activity
workspace_pathNoWorkspace path to resolve session from

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.2.0

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It clearly indicates the tool records an activity and gives guidance on content quality, but it does not disclose whether the entry is persisted, how it relates to the workspace/session, or what response is expected.

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 compact and front-loaded with the core purpose. Every sentence earns its place: what it does, which categories to use, and how to phrase the content. No redundancy or 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 4-parameter tool with no output schema, the description covers the important category semantics and content expectations. Metadata and workspace_path are adequately described in the schema. A small gap is the lack of any statement about return values or session behavior, but this is not critical for a simple logging tool.

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 coverage is 100%, so the baseline is 3. The description adds meaningful value by expanding the category enum into concrete use cases and giving a good versus poor example for the description parameter, which goes beyond the schema's generic labels.

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 clear verb+resource: 'Record an important activity during work' and enumerates the activity categories it supports. The category list makes its purpose specific, though it does not explicitly distinguish itself from similar siblings like append_event or log_file_change.

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?

Explicitly tells when to use it with a list of activity types such as decisions, bugs, todos, commands, test_result, and error. It also gives content guidance like 'Record outcomes, not reasoning,' but it does not name alternatives or state when not to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.