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lodetomasi

Portfolio Copilot

by lodetomasi

log_decision

Append a suggested investment decision to a local ledger, recording the alternative considered for later measurement of decision quality.

Instructions

Append a suggested decision to the local decision ledger (data/private, git-ignored). Records what was decided and the shadow alternative so it can be measured later.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
priceNo
scoreNo
themeNopersonal_edge grouping fallback used only when category is not set
actionYesBUY|BUY_SMALL|HOLD|WATCH|REDUCE|SELL|NO_BUY
reasonYes
symbolYes
cap_eurNoPer-position EUR cap this amount was checked against, for decision_quality's amount_within_cap criterion
sourcesNo
categoryNoFree-form grouping used by personal_edge, e.g. a sector or strategy
red_teamNo
amount_eurNo
candidatesNoThe full ranking shown at decision time (e.g. capital_auction's 'candidates_for_ledger'): [{'symbol','kind','utility','price','price_symbol'}, ...]. Stored so portfolio.opportunity can later measure regret against every option that was on the table, not just the single recorded 'alternative'.
confidenceNo
alternativeNowhat you would buy instead
decision_kindNo'bucket' for a bucket/index fill (no red team, no alternative, no per-stock cap by design -- decision_quality scores it on the criteria that actually apply); omit for an ordinary single-stock decision
thesis_statusNoSTABLE|STRENGTHENING|WEAKENING|BROKEN|UNVERIFIABLE if check_thesis was run at decision time; feeds decision_quality's thesis_status criterion
alternative_priceNo
Behavior3/5

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

No annotations are present, so the description carries the transparency burden. It usefully discloses the local, git-ignored ledger location and the append-only persistence model, but it does not explain side effects, return values, idempotency, or failure modes for a write operation.

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?

Two compact sentences with no filler; the verb is front-loaded and both sentences contribute useful context. It is concise to a fault given the parameter complexity, but as pure structure it is efficient.

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

Completeness2/5

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

With 17 parameters, no output schema, and no annotations, an agent has little guidance on expected behavior, return value, or how the many optional fields fit together. The description only covers the basic append purpose and leaves the rest to the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 47%, so the description should compensate, but it mentions only the generic concepts 'what was decided' and 'shadow alternative' without detailing any of the 17 parameters. It adds almost no meaning beyond the schema.

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?

The description states a specific action ('Append') and a specific resource ('local decision ledger') plus the purpose of recording decisions for later measurement. It is unambiguous about being a write-only log, but it does not explicitly name or contrast sibling tools like review_decisions or decision_quality, so it stops short of full differentiation.

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?

There is no statement about when to choose this tool over alternatives or any exclusions. The description implies it is for recording a decision, but it does not mention that review_decisions is for reading or that decision_quality is for scoring, leaving selection entirely 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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