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Agent Platform Rules

Get a platform's rules for AI agents / automated accounts

get_platform_rules
Read-only

Before signing up, posting or commenting on a platform as an AI agent or automated account, call this to see whether automation and AI-generated text are allowed, what must be disclosed (e.g. labels), verbatim quotes from the rules, source URLs, and when they were last verified. Not legal advice; open the sources before acting.

Input Schema

NameRequiredDescriptionDefault
platformYesOne of: hackernews, reddit, bluesky, mastodon.social, github, x (aliases like "hn", "twitter", "bsky" work).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description still adds value by disclosing the output's nature (verbatim quotes, source URLs, last-verified timestamps) and a staleness caveat telling the agent to open sources before acting. It does not state rate limits or freshness guarantees, so it stops short of a 5.

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?

Two sentences, front-loaded with the trigger condition and followed by concrete payload contents. Every clause carries information; nothing is repeated from the schema or annotations.

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?

For a single-parameter read tool with no output schema, the description fully pre-specifies what comes back and what to do with it. The only gap an agent might want, behavior for an unknown platform, lies in error handling that the schema's value list already constrains.

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?

Schema coverage is 100% and the enum-style value list plus aliases are documented in the schema itself, so the baseline is 3. The description earns a nudge above baseline by tying the parameter to the scenario (a platform you are about to act on), though it does not mention that unsupported platforms will not resolve.

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 (get/fetch) and resource (a platform's automation/AI rules) and enumerates what the response contains: allowance status, disclosure requirements, verbatim quotes, source URLs, verification dates. Clearly distinguishable from the sibling list_platforms, which merely enumerates platforms.

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?

Gives an explicit pre-condition trigger: call this before signing up, posting, or commenting as an AI agent. The 'Not legal advice; open the sources before acting' line adds a follow-up obligation, which is genuine usage guidance rather than filler.

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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