EffectHub

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds one genuine behavioral detail — comparison_url only appears when a researched head-to-head page exists — but says nothing about parameter validation failure modes or rate limits, so it goes modestly beyond the annotations rather than richly.

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 tight sentences: the first front-loads what is compared and what the output contains, the second covers the conditional comparison_url. No filler or restated title content.

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 simple read-only two-parameter tool with full schema coverage and an output schema (so return values need not be explained), the description is essentially complete, and it helpfully details output content anyway. The only gap is the absent when-to-use guidance relative to list_alternatives.

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 100%, with both parameters documented as slugs including pattern and length constraints, so the schema carries the semantics. The description adds nothing about the slug format or whether order of a/b matters, so 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 a specific verb+resource (compare two EffectHub tool cards) and enumerates the compared dimensions (pricing, scores, platforms, API, liveness) plus the plain-sentence diff. This is clearly distinguishable from the single-tool sibling get_tool and the listing siblings search_tools/list_alternatives.

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: the agent can infer this is for head-to-head comparison of exactly two known tools. There is no explicit when-to-use, when-not-to-use, or routing statement against the similar-sounding sibling list_alternatives, so the agent must infer the boundary itself.

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, idempotentHint=true, openWorldHint=false, and destructiveHint=false, so the safety profile is covered. The description still adds useful behavioral context beyond that: it discloses liveness data ('whether it is still alive and when that was last checked') and the presence of a canonical card URL, which an agent would not infer from the annotations.

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, front-loaded with the payload contents and followed by the input-sourcing note; nothing is padded. The field enumeration partially duplicates what the output schema already declares, a minor redundancy rather than real bloat.

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 single-parameter, read-only lookup with an output schema, the description is complete enough: it covers the purpose, the input's provenance, and the card's key fields. Explaining return contents is not strictly required given the output schema, but it does no harm.

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 schema already gives a format example ('cursor'), so the baseline would be 3. The description goes further by showing how to derive the slug from an effecthub.net URL, which adds practical meaning about the value's origin and shape.

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 verb and resource (retrieve one EffectHub tool card) and enumerates the card's contents, so an agent knows exactly what comes back. It also implicitly separates itself from siblings by naming search_tools as the upstream feed for the slug and framing this as the single-tool lookup versus compare/list/search.

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?

It tells the agent where the required slug comes from (search_tools output, or the tail of an effecthub.net/ai/{slug} URL), which gives clear usage context. It stops short of explicit when-not guidance or a direct contrast with compare_tools and list_alternatives, so it doesn't reach the top of the scale.

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 readOnly/idempotent/non-destructive, so the bar is lower. The description adds genuine behavioral detail beyond them: result ordering, the 'researched' vs 'category' basis labels, and the exclusion of discontinued tools — useful for interpreting results.

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?

Three tight sentences, front-loaded with the core definition, then ordering, then exclusion. No filler and nothing repeated from the schema or annotations.

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 an output schema present, return values need not be explained, and the description still clarifies the ordering and the meaning of the basis values. What is missing is only the sibling-routing guidance an agent would need to choose this tool deliberately.

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 100% and both parameters (slug, limit) are documented in the schema, so the description does not need to compensate. It adds no syntax or constraint detail beyond the schema, which is the expected baseline.

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?

Opens with a specific verb+resource: tools that do the same job as the given tool. It is clearly distinct from get_tool or compare_tools in intent, though it never names those siblings to make the boundary explicit.

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 explains what comes back and in what order, which implies the use case (discover replacements/peers), but it never states when to prefer this over search_tools or compare_tools. Usage is inferable rather than stated.

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 readOnly/idempotent/non-destructive, so the safety bar is low. The description still adds real behavioral context: only published, live tools are returned, and each result carries pricing, Effect score, and a card link. No rate limits, pagination, or empty-result behavior is disclosed.

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?

Three sentences, front-loaded with the core search capability, then filters, then return payload. No redundant restatement of the name or title and nothing padded.

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 an output schema present, the description needn't detail return fields, yet it summarizes what comes back. For a 5-parameter search tool this is nearly complete; the only gap is guidance on when this discovery tool should be replaced by its sibling lookup/comparison tools.

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, but the description meaningfully separates the free-text query (product name, job, or plain-word category) from the strict slug parameters and pricing enum. That contrast helps an agent choose the right field rather than dumping everything into query.

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 verb (Search) and resource (EffectHub catalog of AI tools) and enumerates the three accepted query shapes with parenthetical examples. An agent can immediately tell this is the discovery/browse tool and distinguish it from get_tool or compare_tools.

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 implies when the tool applies (finding tools by name, job, or category) and mentions optional narrowing filters, but never names a sibling or states when to prefer get_tool, compare_tools, or list_alternatives instead. Usage is inferable but not routed.

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