mkt_experience

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

With no annotations at all, the description carries the full disclosure burden, and it does add genuinely useful context: returned entries carry provenance and sample sizes, and single-source entries should be treated as hypotheses rather than norms. It still omits whether reads are safe/side-effect free, whether one-article entries are included in results, and what happens for a platform with no records.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single tight sentence with a colon structure that front-loads the resource and then the caveat; nothing is wasted. It is efficient, though arguably too terse given the missing usage and parameter detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter lookup with no annotations and no output schema, the description covers the nature and trust level of the data but leaves an agent guessing about the platform value format, result shape, and when to prefer sibling tools. Adequate minimum viability, not complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the description never mentions the `platform` parameter – no accepted value format, no list of supported platforms, no case or slug conventions. The section on sample sizes does not compensate for this gap, so the only required input is entirely undocumented.

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

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

The description names the resource – a library of real-world platform practices with sources and sample sizes – which is more specific than a bare label. But it never states an operation verb (look up, list, query), so an agent must infer from the single `platform` parameter that this retrieves experience entries for a platform.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance and no routing against close siblings such as mkt_search, mkt_memory or mkt_playbooks, all of which sound like plausible places to look for platform know-how. The '≥2 articles = pattern, 1 article = hypothesis' rule governs how to read results, not when to call this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.