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dca_backtest

Read-onlyIdempotent

Dollar-cost-averaging backtest using installed monthly closes, not live quotes. Asset ticker, start/end months YYYY-MM, initial deposit in USD, monthly in USD per purchase, frequency in months (1, 3 or 12), buying fee in percent, optional benchmark and total/price return basis. Initial deposit and first recurring purchase occur at the selected start month-end. Omitted dates use calculator defaults clamped to coverage. Returns data metadata and the free calc-dca PNG. Returns results, conditions, assumptions, an editable link, share page and PNG. No account access or order execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoEnd month, YYYY-MM. Must be within the installed data snapshot.2026-09
assetNoAsset ticker. Available historical start dates vary by asset.QQQ
startNoStart month, YYYY-MM.2016-10
buyFeeNoBuying fee as a percentage, e.g. 0.1 means 0.1%.
initialNoInitial deposit in dollars.
monthlyNoContribution in dollars per chosen frequency (DCA), or per month (compound interest).
benchmarkNoOptional benchmark asset.none
frequencyNoContribution interval in months: 1, 3 or 12.
returnBasisNototal reinvests distributions; price excludes distributions.total
include_imageNoAlso inline a PNG no wider than 1000 pixels. Image URL is always returned.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
metricsYes
resultsYes
edit_urlYes
image_urlYes
share_urlYes
calculatorYes
conditionsYes
disclaimerYes
assumptionsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, so safety is covered. The description adds real value beyond that: first purchase occurs at the selected start month-end, omitted dates use calculator defaults clamped to coverage, and no account access or order execution. It stops short of describing rate limits or full return shape, but the added behavioral detail is substantial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Reasonably front-loaded, but the sentence 'Returns data metadata and the free calc-dca PNG' is immediately followed by a near-duplicate 'Returns results, conditions, assumptions, an editable link, share page and PNG,' which wastes space and blurs the return-value statement.

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 an output schema present and annotations covering the safety profile, the description needs only the sourcing caveat, timing rule, and default-handling note, all of which are present. It is largely complete for a no-required-parameter read-only backtest tool.

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

Parameters3/5

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

Schema description coverage is 100%, so all 10 parameters are fully documented in the schema, establishing a baseline of 3. The description largely restates those fields (ticker, start/end YYYY-MM, deposit USD, monthly USD, frequency, fee, benchmark, return basis) without adding format or constraint detail beyond what the schema and pattern/enum already supply.

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?

Opens with a specific verb+resource ('Dollar-cost-averaging backtest') and immediately scopes it ('using installed monthly closes, not live quotes'). An agent can distinguish it from siblings like compound_interest or historical_return without opening any schema.

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

Usage Guidelines3/5

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

Implies the use case through the 'installed monthly closes, not live quotes' constraint and the DCA framing, but never states when to choose this over compound_interest or historical_return. No explicit alternatives or exclusion conditions are named.

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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