DeltaSignal ATLAS-7

DeltaSignal alpha opportunities audit

deltasignal_alpha_opportunities_audit

Read-onlyIdempotent

Use this read-only diagnostic tool to explain why the alpha-opportunity board includes, excludes, or demotes rows. It returns issuer-type, identity, quality-gate, and raw-alpha-versus-board-rank summaries from the same scoring universe used by deltasignal_alpha_opportunities. Parameters: limit is 1-100 for bounded samples; source_date replays a known YYYY-MM-DD slice; issuer_type narrows the audit to operating_company, etf_trust, fund_vehicle, foreign_issuer, unresolved_identifier, or all; include_rows=true attaches full publishable audit rows and should be used only for explicit debugging. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, and does not change board scoring, payments, wallets, files, or account state. Use it after deltasignal_alpha_opportunities or deltasignal_alpha_sweep when the user asks why a high raw alpha row is missing, why ETF/trust/fund rows are excluded by default, why a row was demoted, or whether a screen is safe to summarize.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Maximum sample rows to return in each audit sample. Use 10-25 for agent summaries and up to 100 for broad debugging.
`issuer_type`	No	Optional issuer-type audit filter: operating_company, etf_trust, fund_vehicle, foreign_issuer, unresolved_identifier, or all.
`source_date`	No	Optional DeltaSignal source slice date in YYYY-MM-DD format.
`include_rows`	No	Set true only when the user explicitly asks for the full publishable audit rows.

Output Schema

TableJSON Schema

Name	Required	Description
`data`	Yes	Alpha-opportunity board audit explaining issuer-type filters, identity quality, quality gates, and raw-alpha versus board-rank demotions.
`provenance`	Yes	Traceability information for the MCP tool response.
`mcp_summary`	Yes	Concise high-signal summary of the tool response. Maximum 140 characters.
`usage_metadata`	No	Performance and estimated cost metadata for this MCP tool call.
`suggested_follow_ups`	Yes	Concrete next MCP calls an agent can run to continue the workflow.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

AddedMay 14, 2026

TDQS

A5/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds concrete behavioral detail: 'performs one HTTPS read, has no destructive side effects, and does not change board scoring, payments, wallets, files, or account state.' This exceeds the annotation baseline with actionable context.

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 well-structured with logical sections (purpose, parameters, behavior, usage scenarios). Every sentence adds value; there is no fluff or redundancy. It front-loads the core purpose and then supports with necessary detail.

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?

Despite having an output schema and annotations, the description provides a complete picture: what to expect in results, parameter semantics, safety guarantees, and specific use cases. It fully equips an agent to invoke the tool correctly.

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 coverage is 100%, but the description enriches each parameter with behavioral context: limit as bounded samples, source_date as replay, issuer_type as narrowing audit, and include_rows as full rows for explicit debugging. This adds meaning beyond the schema definitions.

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 specific verb+resource: 'explain why the alpha-opportunity board includes, excludes, or demotes rows.' It clearly distinguishes this diagnostic from sibling tools like deltasignal_alpha_opportunities and deltasignal_alpha_sweep, and it enumerates the returned summary types.

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?

Explicit when-to-use guidance is provided with concrete trigger questions ('why a high raw alpha row is missing, why ETF/trust/fund rows are excluded by default, why a row was demoted, or whether a screen is safe to summarize'). It also references sibling tools as alternatives to use after.

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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