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DeepStack: Independent analysis of the AI economy

The case against a thesis

deepstack_counter
Read-onlyIdempotent

Use when the user states a thesis or a claim about the AI economy and wants the strongest case against it. Returns the side the claim sits on (or the question, when no side label fits), the strongest case against that side from a voice outside it, dated and linked, more opposing voices, and the dated tests that settle it; below the relevance floor, that DeepStack has no formed side on the claim.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYesThe thesis as the user states it, for example “AI demand is real”.
limitNoHow many further opposing voices to return after the strongest case.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds substantive behavior beyond them: the shape of the return (side label, strongest opposing case, further voices, settling tests) and the important edge-case behavior below the relevance floor, where it reports no formed side. It stops short of describing sourcing or freshness mechanics.

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?

A single dense sentence, front-loaded with the usage trigger and followed by the return contents; nothing is redundant. It is slightly over-packed, listing several return elements in one clause chain, which costs a little readability.

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 no output schema, the description carries the return-value burden and does so fairly well, enumerating side, opposing case, further voices, and settling tests, plus the relevance-floor fallback. Minor omissions remain around what 'dated and linked' sources are and how many results precede the limit-controlled extras.

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 both parameters are already documented, including the limit default and 1-5 range. The description adds no syntax, formatting, or example-level detail for either parameter, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action (return the strongest case against a stated thesis) and a specific resource (claims about the AI economy), and the title reinforces it. It does not, however, distinguish itself from siblings like deepstack_sides or deepstack_docket, so an agent must infer the boundary from the names alone.

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?

It gives an explicit triggering condition: the user states a thesis or claim about the AI economy and wants the strongest case against it. There is no when-not guidance and no alternative sibling is named for adjacent cases (e.g., when the user wants a side rather than a counter-argument), so routing is clear but not fully disambiguated.

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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