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Markdown and editors soft-wrap on their own, so the manual ~75-char line breaks across the docs added nothing but noise. Join wrapped prose, list items, and paragraphs into single lines across 67 docs — README, CLAUDE.md, deployment, usage, the RAG knowledge base, and the agent role prompts. Whitespace-only: code fences, tables, and blockquote alerts are byte-identical and the change is token-verified (no content altered). Applied with a deterministic reflow tool (committed separately). Also lands two doc edits that were awaiting commit: the measured under-load resource numbers in usage.md and the pr_reviewer additions to the org-structure RAG doc.
3.5 KiB
3.5 KiB
Journal Tools
There is no roboco_journal_* tool. Journaling is a single content tool on the roboco-do MCP server: note. The scope argument selects the entry kind; structured fields are filled per scope.
note(
text: str, # always: one-paragraph summary
scope: str = "note", # note | decision | reflect | learning | struggle
task_id: str | None = None, # auto-filled from your active task if omitted
title: str | None = None,
# decision-scope fields:
context: str = "",
options=None, # list of {name, pros, cons} (a single dict is ok)
chosen: str = "",
rationale: str = "",
consequences=None, # list of strings (a single string is ok)
# reflect-scope fields:
what_done: str = "",
what_learned: str = "",
what_struggled: str = "",
next_steps=None, # list of strings (a single string is ok)
)
text is always required. Missing narrative fields default to a visible placeholder rather than being rejected — the note is always recorded.
Scopes
| Scope | Use For | Structured fields |
|---|---|---|
note |
General entry | (just text) |
decision |
Decision log | context, options, chosen, rationale, consequences |
reflect |
Task reflection | what_done, what_learned, what_struggled, next_steps |
learning |
Learning capture | (just text) |
struggle |
Problem / blocker | (just text) |
General Entry
note(
text="SCAN is better than KEYS for large datasets",
scope="learning",
title="Redis SCAN vs KEYS",
task_id=task_id,
)
Decision Log
note(
text="Chose Redis for session storage over PostgreSQL.",
scope="decision",
title="Session storage choice",
context="Need fast session lookups",
options=[
{"name": "PostgreSQL", "pros": "durable", "cons": "slower reads"},
{"name": "Redis", "pros": "sub-ms reads", "cons": "ephemeral"},
],
chosen="Redis",
rationale="Sub-ms reads, ephemeral data",
consequences=["Session loss on Redis restart is acceptable"],
)
Learning
note(
text="asyncio.gather for parallel calls — reduced latency 50%",
scope="learning",
title="Parallel async calls",
)
Struggle (Problem / Blocker)
note(
text=(
"Tests failing intermittently — tried timeout increase and retry "
"logic; root cause was a race condition in setup."
),
scope="struggle",
task_id=task_id,
)
Reflection
Use a reflect-scope note before submitting to QA — it gives QA the "why" behind the diff.
note(
text="Implemented rate limiting with a Redis-backed sliding window.",
scope="reflect",
task_id=task_id,
what_done="Implemented rate limiting",
what_learned="Lua scripts give atomicity for the counter increment",
what_struggled="Testing concurrency deterministically",
next_steps=["Add a load test for the 100-req boundary"],
)
Reading Journals
Journals are written by note and surface through the knowledge base — there is no separate journal-read tool. Search past notes (yours and your team's, where permitted) via the roboco-optimal MCP server:
# Semantic search over indexed notes/decisions/learnings
roboco_kb_search(query="rate limiting", index_types=["journals", "decisions"])
# Conversational lookup with follow-up context
roboco_ask_mentor(question="What did we decide about session storage?")