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api-evangelist

by com.apievangelist
OverviewDiscussionsAdmin

entity_demand

Read-only

PRO — THE INVERSION. Every other insights tool runs company -> stack; this one runs stack -> companies: which companies show demand for a given service, tool or standard, broken down by industry. This is the prospecting query — "who should I be selling this to" — and it exists in no report, bundle or page. Returns BOTH the authoritative company count and the companies it can actually name, with the basis difference explained.

Input Schema

NameRequiredDescriptionDefault
kindYesEntity kind: "service", "tool" or "standard".
slugYesEntity slug or display name — either resolves ("spark" and "Apache Spark" both work).
limitNoHow many named companies to return, 1-500.
contextNoOptional: why you are asking. One sentence — the task you are trying to complete, or what you expect to get back. Never included in the answer and never used to rank; it is read only when a result turns out to be wrong, which is when knowing the intent is what makes the report actionable.
industryNoRestrict the named companies to one industry.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / kind / description
      Added value: +"Entity kind: \"service\", \"tool\" or \"standard\"."
    • addedInput schema / properties / limit / description
      Added value: +"How many named companies to return, 1-500."
  2. Changed1 schema field changed
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Optional: why you are asking. One sentence — the task you are trying to complete, or what you expect to get back. Never included in the answer and never used to rank; it is read only when a result turns out to be wrong, which is when knowing the intent is what makes the report actionable.",
+  "type": "string"
+}
  3. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already establish readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds genuine behavioral value beyond that: it discloses that the return carries two distinct numbers (authoritative count vs. named companies) and that the 'basis difference' is explained, which is the kind of output quirk an agent needs and no schema provides.

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?

Front-loaded with the core inversion idea and information-dense. The all-caps 'PRO — THE INVERSION' and the promotional 'exists in no report, bundle or page' are marketing filler, but most sentences earn their place.

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?

For a read-only query with no output schema, the description covers purpose, the direction of the query, and the two-count return shape, which is most of what an agent needs. It stops short of describing result format or what 'basis difference' means concretely.

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 the schema already documents kind, slug, limit, context and industry. The description adds only the 'broken down by industry' framing that maps to the industry param, and nothing about slug resolution or limit semantics beyond the schema. Baseline 3 is correct.

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?

States a specific verb+resource (which companies show demand for a given service, tool or standard) and explicitly frames the direction of the query as the inversion of the other insights tools. An agent can tell it apart from insights_adoption or company_dimensions from the description 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?

Gives clear context for when to reach for it ('the prospecting query — who should I be selling this to') and contrasts it with 'every other insights tool'. It never names a specific sibling to prefer or an exclusion, so routing is inferred rather than explicit.

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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