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workflow_log

Log project tasks, decisions, and insights to preserve context across AI conversations. Entries are scoped to the current git project and synced to CLAUDE.md.

Instructions

Log a project workflow entry. PROACTIVE: You MUST automatically call this tool (without being asked) when any of these happen: (1) User requests a multi-step task → log type "task" with what needs doing. (2) A design/architecture choice is made between alternatives → log type "decision" with rationale. (3) You discover something non-obvious and project-specific (e.g. undocumented behavior, workaround needed) → log type "insight". Core question: "Would this be useful in a FUTURE conversation about this project?" If yes, log it. If only relevant now, skip it. WHEN TO LOG each type: type "task" — User requests a multi-step task (not a one-liner fix). Log when starting (what needs doing) and when partially complete (what is done, what remains). Do NOT log trivial single-step changes. type "decision" — A choice was made between alternatives (e.g. Redis vs local cache, library A vs B). Include the rationale: WHY this approach was chosen. Do NOT log obvious defaults with no alternatives considered. type "insight" — Discovered something PROJECT-SPECIFIC that is not obvious from docs or code (e.g. an API has an undocumented rate limit, a specific query is slow, a workaround was needed). Do NOT log general programming knowledge that applies to all projects. If an entry with the same title and type exists, it will be updated (upsert). Data is scoped to the current git project and auto-synced to the project CLAUDE.md.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoOptional tags for categorization
typeYesEntry type: "task" for work items, "decision" for architecture/design choices, "insight" for discovered knowledge or pitfalls
titleYesShort title for this entry, used as upsert key
contentYesDetailed description

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it discloses upsert-on-same-title-and-type semantics, git-project data scoping, and auto-sync to CLAUDE.md. These are non-obvious side effects an agent must know before invoking.

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 PROACTIVE mandate is front-loaded, followed by a clean core-question test and a type-by-type breakdown. Length is justified by the need to drive unsolicited invocation, and no sentence is redundant with the schema besides minor restatement.

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?

For a 4-param mutation tool with no output schema and no annotations, the description covers purpose, triggers, exclusions, parameter meaning, upsert behavior, and storage/sync scope. Nothing an agent needs to call it correctly is missing.

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, but the description goes further by explaining what each enum value means in practice and when each applies, plus confirming title acts as the upsert key. Some of this is redundant with the schema, keeping it just under a 5.

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?

States a specific verb+resource ('Log a project workflow entry') and immediately scopes it with the three entry types, so an agent can tell it apart from workflow_list and workflow_done (read/complete siblings) without opening the schema.

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

Usage Guidelines5/5

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

Explicit when-to-call triggers, per-type logging criteria, and negative cases ('Do NOT log trivial single-step changes', 'Do NOT log obvious defaults', 'Do NOT log general programming knowledge'). It even supplies a decision heuristic ('Would this be useful in a FUTURE conversation?'), leaving nothing 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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