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Analyzera

by com.analyzera
OverviewDiscussionsAdmin

Historical scenarios

describe_historical_scenarios
Read-onlyIdempotent

How today's holdings would have fared in named past market episodes, as figures.

Replays the portfolio's current factor exposures through each episode's market moves:
the financial crisis, Covid, the 2022 rate rises and others since 2007, recoveries
included. With no selection every episode runs, worst first; ``tags`` picks episodes
by theme, ``scenarios`` by id, and either match counts. ``detail='full'`` adds each
episode's story, the main drivers and the currency effect. To compare, replay a
benchmark or another portfolio the same way.

A replay of the past, not a forecast: today's holdings, not what was held then, and
market-wide moves only. A one-sector what-if such as "technology down a fifth,
nothing else moving" is not an episode here; the sector weights from
``describe_portfolio_weights`` answer it.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "describe_historical_scenarios"
  }
}

Input Schema

NameRequiredDescriptionDefault
tagsNo
detailNosummary
holdingsNoIn place of portfolio_id: asset ids and weights, measured as of the latest date and not stored. describe_holdings also takes names, ISINs and amounts.
scenariosNo
portfolio_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds substantive semantics beyond them: results are a replay of today's holdings rather than historical holdings, market-wide moves only with no idiosyncratic/sector effects, recoveries included alongside crashes, and worst-first ordering. This is well beyond the annotation bar.

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?

Front-loaded with the core statement, then selection mechanics, then scope caveats. Three paragraphs is longer than average, but nearly every sentence carries routing or scoping value; only minor tightening is possible.

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?

With no output schema and five all-optional parameters, the description does well to explain result semantics ('as figures', worst-first, full-detail additions). It doesn't clarify whether portfolio_id or holdings is required to get meaningful output, nor the unit/currency of the figures — a small but real gap for a zero-required-parameter 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 description coverage is only 20% (just the holdings field), so the description must compensate, and it largely does: tags selects by theme, scenarios by id, 'either match counts' clarifies they compose, and detail='full' is explained as adding story, drivers and currency effect. The portfolio_id parameter is left unstated, which is the notable gap.

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 and resource ('replays the portfolio's current factor exposures through each episode's market moves') and immediately scopes the output as 'figures'. It is clearly distinguishable from siblings like preview_scenario and describe_portfolio_weights, both of which are implicitly or explicitly routed elsewhere.

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?

Explicitly defines default behavior ('with no selection every episode runs, worst first'), how each selector works (tags by theme, scenarios by id), and — crucially — names what this tool is NOT and which sibling answers that case ('a one-sector what-if ... the sector weights from describe_portfolio_weights answer it'). Alternative-comparison guidance is also given.

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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