Viral Agent

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 adds real value by previewing the payload contents (description, audience, setup difficulty, cost model, links, ranking history), which matters because there is no output schema, but it says nothing about failure behavior for a bad slug.

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 sentence that front-loads the scope ('one agent') and then lists the returned facets. No filler, no repetition of the title or name.

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?

With one required parameter, no output schema, and annotations covering safety, the description carries the remaining burden well by enumerating what the response contains. Missing only edge-case behavior (unknown slug, name ambiguity) to be fully 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 100% and the slug property already documents its own provenance ('from another tool result, or its name'). The description adds no further semantics about slug format, case sensitivity, or name-vs-slug resolution, so the baseline 3 applies.

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?

States clearly that this retrieves full details for a single agent, and the enumerated field list (what it does, cost model, links, ranking history) makes the resource concrete. It does not explicitly contrast with search_agents or the list-style siblings, but the singular 'one agent' scope makes the distinction reasonably obvious.

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?

Usage is only implied: an agent can infer this is the drill-down call for a single known agent while get_rising/get_trending/search_agents produce lists. There is no explicit statement of when to prefer this tool, nor any mention of what to do if the slug is unknown or the agent doesn't exist.

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

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

Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=false), so the bar is lower. The description adds genuinely useful behavior: the three-day comparison window and daily refresh cadence. It omits ordering guarantees, default page size, and what fields each entry carries.

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?

Two short sentences with the ranking definition front-loaded and the freshness note second. Nothing is padding and nothing repeats the title verbatim.

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?

With no output schema, the description should hint at the shape of a result entry or point to get_agent for detail; it does neither. It adequately explains the metric and refresh rate for a one-optional-param list tool, but leaves default sizing and downstream drill-down unaddressed.

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 description coverage is 0% for the single 'limit' parameter, so the description carries the burden and does not compensate at all — no default value, no mention of the 1-50 range, no note on what happens when omitted.

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?

States a specific metric and resource: AI agents ranked by star/attention growth over three days versus prior pace. The ranking rule is concrete enough that an agent knows what it gets back. It stops short of differentiating from the sibling get_trending, which sounds like an overlapping concept.

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?

No guidance on when to choose this over get_trending or search_agents, and no mention of the default result count. The phrase 'right now' implies a recency-driven use case, but that is inference rather than instruction.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered; the description goes beyond them by disclosing the ranking formula and the fact that filtering is purely optional (zero required params). It does not mention result volume, ordering stability, or pagination, but for a read-only leaderboard those are minor.

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?

Two sentences, ranking and scope front-loaded before the filter list, with no filler. The filter enumeration runs slightly long, but each example genuinely aids slug selection.

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 zero-required-param, read-only list tool with full schema coverage, no output schema, and safety annotations, the description covers what the ranking means and how to narrow it. The only meaningful gap is the lack of differentiation from get_rising, which belongs to usage guidance rather than completeness.

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 100%, so baseline is 3, and the description earns a bump by explaining what 'audience' and 'task' actually accept (marketers, content-creators, video-clips, inbox, research) and by decoding max_setup_level as '1 = works in a browser, 5 = requires programming', which the schema only states numerically. It omits limit and include_builder_tools, but both are self-documenting in the schema.

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?

States a specific resource and scope ('This week's Viral Agent leaderboard of AI agents gaining momentum') and even the ranking inputs (star growth scaled by size, acceleration, HN attention), so the agent knows exactly what it gets. However, it never distinguishes itself from the near-synonymous sibling get_rising, which an agent could easily confuse with 'trending'.

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?

The description lists optional filters but gives no guidance on when to reach for this tool versus get_rising or search_agents. With a sibling literally named 'get_rising' producing a similarly growth-oriented list, the absence of any disambiguation is a real selection hazard.

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

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 usefully clarifies that matching is semantic as well as lexical ("by the problem they solve"), but says nothing about result ordering, whether results are capped, or what a result contains.

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 front-loaded sentence with the capability stated first and concrete examples appended. No filler, and the examples earn their place by demonstrating the query style.

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 read-only search tool with no output schema, the description is adequate on intent but thin on return behavior: it never says what comes back (agent names, IDs, descriptions) or that an ID from here feeds into get_agent. The unmentioned limit parameter also leaves the result-set size unexplained.

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 only 50%: query is documented, limit is not described at all beyond min/max bounds. The description partly compensates by giving example queries ("answer customer emails", "turn videos into clips"), which teaches the expected phrasing, but it never mentions the limit parameter or a sensible default value for it.

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?

States a clear verb ("Find") plus the resource ("AI agents and agent tools") and the two matching modes (name or problem solved). The contrast with get_agent/get_rising/get_trending is implicitly clear from "search," but the description never names those siblings or says how results differ from a direct get.

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?

Usage is implied by the examples: use this when you know the job to be done but not the agent. There is no explicit "when not to use" or routing guidance such as "use get_agent when you already have an ID," which is the natural alternative among the siblings.

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