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Invokary Platform Requirements

List platforms

list_platforms
Read-onlyIdempotent

Lists the platforms in the Platform Requirements dataset (Claude, ChatGPT, Cursor and the Official MCP Registry): each one's directory, vendor, number of requirements, topics and latest check date. Use it to see what the dataset covers and which ids the other tools take.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
citationYes
platformsYes
protocolRequirementCountYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already cover the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the bar is lower. The description adds useful context about what fields each record carries, but does not disclose anything beyond that – no rate limits, no pagination, no auth notes. Adequate given the annotation coverage, not rich.

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 resource and its enumeration, followed by the usage cue. Efficient, though the parenthetical list of platforms is slightly dense, but the extra detail earns its place by making the output shape concrete.

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 zero-parameter, read-only discovery tool with an output schema already present, the description covers everything an agent needs to know to call it: what it returns and why. Since the output schema exists, it needn't document return-value structure, and its framing of 'which ids the other tools take' closes the main gap by connecting this tool to its siblings.

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?

This tool has zero parameters, so the baseline is 4. The description correctly frames the tool as parameter-free discovery, and it compensates by enumerating the exact contents/labels of the returned records, which is helpful given there is nothing to parameterize.

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 (Lists) and resource (platforms) and names the exact dataset enumeration (Claude, ChatGPT, Cursor, Official MCP Registry). It also says what each record contains (directory, vendor, requirement count, topics, latest check date), which tells an agent precisely what this tool returns.

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?

The description gives a clear use case: 'Use it to see what the dataset covers and which ids the other tools take.' That effectively positions this as the discovery/id-lookup entry point relative to siblings like get_platform_requirements and compare_platforms, though it does not name those siblings or state exclusions explicitly.

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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