Hesper Atlas Evidence

Ledger statistics

get_ledger_stats

Read-onlyIdempotent

Use when a user asks for drawdowns, weak periods, best-versus-worst results, or whether a headline hides an unfavorable distribution. Returns detailed cuts of the current-rule historical replay: per calendar year, per theme, return distribution buckets, holding periods, the ten best and ten worst closed trades, the open book, and closed+open combined on identical buckets. Use section to pull one cut instead of all of them. Signal data, not investment advice.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`section`	No	Which cut to return. Default 'all'.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`as_of`	No
`source`	No
`caveats`	No
`is_live`	No
`citation`	No
`data_type`	No
`disclaimer`	Yes
`provenance`	No
`source_url`	No
`last_updated`	No
`calculation_version`	No
`methodology_version`	No

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds meaningful behavioral context: it states the output includes per-year/per-theme cuts, extremes, open book, combined closed+open buckets, and explicitly frames the tool as signal data, not investment advice.

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 description is front-loaded with the primary use case, then compresses the eight available cuts into one readable, purposeful sentence. The final pointer to `section` and the 'not investment advice' caveat are both relevant and brief.

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?

Given the output schema exists and the annotations cover side-effect safety, the description supplies exactly the missing decision context: what kinds of questions this tool answers, what cuts are available, and how to narrow output. Nothing an agent needs to select it correctly is absent.

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 coverage is 100% and the single parameter already has a clear enum and default description. The description reinforces using `section` to pull one cut instead of all, but this does not go substantially beyond what the schema already provides.

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?

The description opens with an explicit user-intent trigger ('drawdowns, weak periods, best-versus-worst, ...') and a specific resource ('current-rule historical replay'). The list of distinct cuts makes the tool's scope concrete and separates it from sibling tools such as get_track_record or get_forward_record.

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

Usage Guidelines4/5

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

The description clearly states when to use the tool ('Use when a user asks for drawdowns, weak periods...') and advises using the `section` parameter to narrow the result. It does not explicitly name sibling alternatives or state when not to use it, so it stops short of full exclusion guidance.

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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TDQS

A4/5.0

Disambiguation3/5

The five historical-performance tools (get_track_record, get_walk_forward_evidence, get_heldout_evidence, get_ledger_stats, list_closed_trades) occupy heavily overlapping territory, and an agent could easily grab the wrong evidence artifact. The detailed 'use when' openers help considerably, but the boundaries between replay, walk-forward, and validation are subtle enough that misselection risk remains real.

Naming Consistency4/5

All tools follow a clean snake_case verb_noun pattern, with get_ reserved for single artifacts/reports and list_ for enumerable collections. The convention is slightly loose—get_changes_since and get_ledger_stats are more list-like than get_-like, and the verbs don't always signal collection size—but overall the pattern is predictable and readable.

Tool Count4/5

At 15 tools, the server sits at the upper boundary of a well-scoped surface, and each tool does earn its place in the evidence ecosystem (current signal, lists, portfolio, context, diffs, publications, replay, validation, methodology, provenance). It is slightly heavy and could feel daunting, but nothing is redundant.

Completeness4/5

The surface covers the evidence domain thoroughly: current state, forward publication history, performance replays, two distinct validation artifacts, methodology, and hash-level provenance. Minor gaps exist—there is no tool to enumerate the full covered universe or search across tickers, forcing users to arrive with a symbol in mind—but core workflows have no dead ends.

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