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DeltaSignal ATLAS-7

by io.github.aitrailblazer

DeltaSignal ATLAS-7 audit status

deltasignal_atlas7_audit_status
Read-onlyIdempotent

Use this read-only tool to check whether the Azure-native ATLAS-7 full-universe regression audit is healthy. It reads the latest audit summary artifact from Azure Blob and reports last successful run time, issuer count, operation count, failure counts, historical route status, composite route status, and artifact prefix. Parameters: none. Behavior: read-only and idempotent; it has no destructive side effects, does not run the audit, mutate data, or access raw issuer evidence. Use this before trusting historical ATLAS-7 surfaces in an agent workflow or when an operator asks whether the nightly 215-issuer audit is current.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesLatest Azure ATLAS-7 regression audit health summary.
provenanceYesTraceability information for the MCP tool response.
mcp_summaryYesConcise high-signal summary of the tool response. Maximum 140 characters.
usage_metadataNoPerformance and estimated cost metadata for this MCP tool call.
suggested_follow_upsYesConcrete next MCP calls an agent can run to continue the workflow.

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (read-only, idempotent, non-destructive), the description discloses the underlying data source (Azure Blob), the exact fields it reports, and explicitly states it does not access raw issuer evidence or run the audit. This adds valuable behavioral context the annotations do not convey.

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?

Three sentences, each earning its place: purpose, data source/outputs, behavior and usage. Front-loaded with the primary action, and no filler or repetition of the tool name.

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 no parameters, strong annotations, and an output schema, the description fully covers the tool's role, safety profile, and usage context. It adds distinguishing context (Azure-native, full-universe, nightly 215-issuer) that makes it complete for an agent to select correctly among a large sibling set.

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?

The tool has zero parameters, so schema coverage is 100% and description simply states 'Parameters: none.' The baseline for 0 params is 4, and there is nothing more to explain. The description appropriately notes this without redundancy.

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 uses a specific verb ('check whether... is healthy') and pinpoints the exact resource: the Azure-native ATLAS-7 full-universe regression audit. It distinguishes itself from the many sibling audit tools by naming ATLAS-7, 'full-universe regression', and explicitly noting it only reports status, not runs the audit.

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 states when to use: 'before trusting historical ATLAS-7 surfaces in an agent workflow' and 'when an operator asks whether the nightly 215-issuer audit is current.' It also clarifies what it does NOT do (run the audit, mutate data) which helps rule out misuse.

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

C2.8/5.0
Disambiguation2/5

The tool set has extensive overlap: natural-language variants duplicate raw JSON tools (e.g., deltasignal_covenant_stress vs deltasignal_covenant_stress_natural), composite workflow tools duplicate low-level screens (deltasignal_alpha_sweep vs deltasignal_alpha_opportunities), and the atlas7_* and deltasignal_* prefixes repeat the same concepts (e.g., atlas7_covenant_stress vs deltasignal_covenant_stress). An agent would likely struggle to pick the right tool for a given intent.

Naming Consistency2/5

Three distinct prefixes (atlas7_, deltasignal_, strategix_) are used, and duplicate tool names appear across prefixes (e.g., atlas7_readiness vs deltasignal_readiness). Verb patterns are inconsistent, mixing nouns and verbs (atlas7_archive_search, deltasignal_thesis_create, deltasignal_morning_brief_natural), and there is no clear rule for raw, natural, composite, or resolver variants.

Tool Count1/5

With 81 tools, the server is massively over-scoped. Many are near-duplicates or overlapping composites; the core domain likely needs 20-30 tools at most. The count is far beyond what an agent can reasonably navigate, and it indicates a failure to consolidate or version the surface.

Completeness3/5

The set covers a broad range of functionality: screening, stress, alpha, peer comparison, history, tripcode resolution, synthetic ETF state, and thesis monitoring. However, there are lifecycle gaps (e.g., thesis_create has no corresponding update/delete, and the atlas7 vs deltasignal split leaves no unified surface). The duplication suggests comprehensive coverage in some areas and awkward holes in others.

Resources