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DeepStack: Independent analysis of the AI economy

A company or a person

deepstack_entity
Read-onlyIdempotent

Use when the user asks what the voices argued about a company, or what a person argued, in The Buildout; by slug, ticker or name. Returns where it appears, the figures the stories reported, each argument attributed, dated and linked, and for a listed company the delayed market snapshot with its source. Never a score or a ranking.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoNarrow the lookup to companies, to people or to the organizations that contribute argument (listed with the people).
nameNoThe name, when the slug is not known.
slugNoThe entity slug, as in https://deepstack.ltd/companies/<slug>/ or /people/<slug>/.
tickerNoA listed company’s ticker, for example NVDA.

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 declare readOnly, idempotent and non-destructive behavior, so the description's added value is the shape of the answer: arguments are attributed, dated and linked, and the market snapshot is explicitly delayed and sourced. The explicit negative ('Never a score or a ranking') usefully bounds expectations, though permissions and rate limits are unaddressed.

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?

Two sentences, front-loaded with the usage trigger before the return contract. The middle clause listing returned artifacts is somewhat dense but each item (dates, attribution, links, market source) carries real information.

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?

There is no output schema, so the description properly carries the return burden and does so at a useful level of detail, including the sourced market snapshot. It stops short of stating limits, pagination, or fallback behavior when no entity matches, which leaves a modest gap.

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%, so all four parameters (kind, name, slug, ticker) are already documented, including the enum and slug URL format. The description only restates that slug/ticker/name are accepted identifiers, adding no syntax or precedence guidance (e.g., which identifier wins if several are supplied).

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?

States a specific verb and resource: look up a company or person in The Buildout by slug, ticker or name, and it enumerates what it returns (appearances, reported figures, attributed arguments, delayed market snapshot). The closing 'Never a score or a ranking' implicitly distinguishes it from comparative siblings, but no sibling is named outright.

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?

Gives an explicit trigger ('Use when the user asks what the voices argued about a company, or what a person argued') plus the accepted identifier forms. There are no named alternatives or when-not conditions, so routing between deepstack_entity and siblings like deepstack_results still requires inference.

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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