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Hesper Atlas Evidence

Rolling walk-forward evidence

get_walk_forward_evidence
Read-onlyIdempotent

Use when a user asks for stricter time-ordered evidence, weak years, benchmark underperformance, or a check against look-ahead. Returns the committed cache-frozen annual walk-forward artifact. Each year's candidate is selected using only earlier observations and then traded for the next calendar year with next-open execution and costs. Returns the content-derived evidence id, input hash manifest, aggregate results, and paged per-symbol/year rows. Weak periods and underperformance versus buy-and-hold are retained. Set artifact to 'pooled_selection' for the class-pooled selection audit (per-name and pooled rule selection both converge to the untuned class engine) or 'model_portfolio' for the monthly walk-forward of the shipped model-portfolio construction (12-1 momentum top 10/20 among engine-long quality stocks, every risk profile, benchmarks, monthly picks, broad-universe check). Historical validation, not live performance or advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoRows to return, default 25, max 100.
offsetNoRows to skip, default 0.
symbolNoTicker symbol, e.g. NVDA. Case-insensitive.
artifactNoWhich artifact. Default per_name.
include_inputsNoInclude all committed input-file hashes; default false for compact responses.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofNo
sourceNo
caveatsNo
is_liveNo
citationNo
data_typeNo
disclaimerYes
provenanceNo
source_urlNo
last_updatedNo
calculation_versionNo
methodology_versionNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / artifact
      Added value: +{
      +  "description": "Which artifact. Default per_name.",
      +  "enum": [
      +    "per_name",
      +    "pooled_selection",
      +    "model_portfolio"
      +  ],
      +  "type": "string"
      +}
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds substantial behavioral context: the artifact is cache-frozen, each year's candidate is selected on earlier observations only, trading uses next-open execution with costs, weak periods and underperformance are retained, and the output is clearly a historical validation artifact, not live performance or advice. This goes far beyond the annotation baseline.

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?

The description is long but information-dense; every sentence earns its place. It front-loads the primary use case, then lists return contents, then parameter-specific guidance, and closes with a caveat. It is slightly verbose, and the artifact branch could be more compact, but overall the structure supports quick scanning.

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 tool's complexity, five parameters, and an output schema, the description leaves little missing. It explains what the tool returns, how to choose between artifact variants, the safety profile (via annotations), and the limitation of being historical validation. No further contextual detail seems necessary for correct invocation.

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

Parameters5/5

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

Schema description coverage is 100%, so the schema already documents each parameter. The description adds valuable semantic depth, especially for 'artifact' (explaining what pooled_selection and model_portfolio audits do) and for 'include_inputs' (mentioning compact responses). This meaningfully enhances an agent's ability to choose parameter values correctly.

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 a very specific trigger ('Use when a user asks for stricter time-ordered evidence, weak years, benchmark underperformance, or a check against look-ahead') and clearly identifies the resource (cache-frozen annual walk-forward artifact). It distinguishes this tool from likely siblings by the precise domain of walk-forward evidence and by naming internal artifacts, so an agent can route correctly.

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 explicitly states when to use it ('Use when...') and provides detailed guidance on choosing the 'artifact' parameter with specific scenarios for 'pooled_selection' and 'model_portfolio'. It does not explicitly say when not to use this tool versus named sibling tools like get_heldout_evidence or get_track_record, but the use cases are so clearly scoped that the ambiguity is low.

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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