AgentScout

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

Adds useful behavior beyond the readOnly/idempotent annotations: results are ranked by relevance with GitHub stars as a tiebreak, and tools lacking clear local-running evidence are excluded from local filters. It does not cover authentication or rate limits, but those are outside the main behavior for this read-only recommendation tool.

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 dense sentences with the core purpose, ranking logic, filters, and the most nuanced filter guidance front-loaded. Parenthetical examples earn their place by illustrating plain-language task input and local-running use cases.

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?

Complete enough for a read-only recommendation tool with no output schema: the description covers what the tool does, how results are ordered, and what each major filter means. A minor gap is that it does not describe the shape of the returned recommendations beyond ranking.

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?

The description adds meaning beyond the schema for the runs_locally filter, explaining offline versus self-hosted_or_offline and the exclusion behavior, and clarifies that the task is plain words. It only names pricing and open_source without adding semantics, and does not mention the limit parameter, so coverage is partial.

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: it suggests AgentScout tools for a plain-language task, with relevance ranking and GitHub stars as tiebreak. It is clear, but it does not explicitly distinguish itself from the sibling search_tools, leaving the agent to infer that this is a recommendation engine rather than a raw keyword 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?

Gives explicit guidance for the runs_locally filter, including when data must stay local, and mentions optional pricing and open-source filters. It does not explain when to use this tool versus search_tools or get_tool, so the agent must infer the boundary between recommendation and lookup.

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 and closed-world, so the safety profile is covered. The description adds value by naming the payload contents (key features, platforms, GitHub repo and stars, data_locality with evidence), which is the main thing an agent needs beyond the annotations. It omits behavior for missing/invalid slugs.

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 leads with the action and packs the return contents afterward with no filler. Nothing is redundant with the title or schema.

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 usefully enumerates the returned fields, which is the critical missing piece. The only gap is undefined behavior for an unknown slug and whether the lookup can fail, which a lookup tool of this simplicity arguably still warrants.

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 pattern/example already document format, so the schema does most of the work. The description still adds meaning by stating the slug should come from search_tools results, clarifying provenance beyond the schema's bare format rules.

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 ('Get the full AgentScout listing for one tool') and enumerates what the listing contains. The phrase 'from search_tools results' implicitly separates it from the sibling search/list tools, which return multiple or summary items rather than one full record.

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?

Clearly establishes the workflow context: use this after search_tools, passing the slug it returned. It does not state exclusions or what to do when the slug is unknown, so it falls short of a full when/when-not treatment.

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, openWorldHint=false, and idempotentHint, so safety and side-effect profile are covered. The description adds output detail (tool count, page URL) but does not disclose pagination, ordering, or other behavioral traits beyond those 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?

A single sentence, front-loaded with the core action and resource, with no wasted words. It is appropriately sized for a simple no-parameter list tool.

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?

Given the empty schema, rich annotations, and no output schema, the description supplies the essential return fields (categories, tool count, page URL). It could be more complete by noting whether the result is a flat list or offering ordering, but the missing detail is minor.

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?

The tool takes zero parameters, so the baseline score is 4. The description correctly does not discuss nonexistent parameters.

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 states a specific verb (List) and resource (AgentScout categories) and adds what each entry contains (tool count and page URL). It is clear but does not explicitly contrast with sibling tools like search_tools or get_tool, leaving some differentiation to the tool name.

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 is given about when to use this tool versus alternatives such as search_tools or best_for_task. The description implies a simple listing use case but offers no exclusions or decision context.

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 and closed-world, so the safety profile needs no restating. The description adds real value beyond them: the 25-result cap, that pricing is 'as the maker states it' (no normalization), and that each hit carries a listing URL and a plain data-locality note.

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 tightly written sentences, with the core action and filter scope front-loaded before return-shape details. Minor redundancy between the filter list mentioning 'data-locality (runs_locally)' and the later data_locality sentence, but nothing extraneous.

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 carries the burden of describing results, and it does: cap of 25, pricing caveat, listing URL, and the four data_locality states. Adequate for an agent to act on results; only slight gaps around ranking/relevance behavior.

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 all six parameters are already documented, including the runs_locally enum semantics and the limit default. The description only re-lists the filter names and restates data_locality values, adding little beyond what the schema says. 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 and resource ('Search the AgentScout directory of AI agent tools and MCP servers') with keyword-plus-filter scope. This implicitly separates it from get_tool (single lookup) and list_categories (taxonomy), though it never names those siblings explicitly.

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 verb 'search' and the note that the query is 'optional if a filter is set', but there is no explicit when-to-use versus best_for_task or get_tool. It never tells the agent when NOT to use this tool or which sibling to prefer.

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