should-i-use

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds value by explaining the output structure: alternatives are curated and include a one-line 'prefer it when' and a verdict if indexed, giving the agent concrete expectations about what will be returned.

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?

The description is a single, dense sentence that states the core function and key output details without unnecessary words. It is front-loaded with 'Curated alternatives' and efficiently packs useful information.

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 one-parameter tool with an output schema (as indicated), the description provides sufficient context. It covers the main output elements (prefer-it-when, verdict) and relies on schema and annotations for the rest, making it complete for an agent to select and invoke correctly.

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 input schema fully describes the only parameter 'library' with its format (npm or PyPI). The tool description adds no extra parameter details, but since schema coverage is 100%, the baseline of 3 applies without requiring compensation.

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 specifies the tool provides curated alternatives for a library, including a one-line 'prefer it when' and a verdict if the alternative is also indexed. This clearly distinguishes it from siblings like pick_library or should_i_use, which focus on selection or evaluation.

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 the use case (finding alternatives to a library) but does not explicitly state when to use it versus sibling tools or when not to use it. There is no mention of alternative tools like pick_library for choosing or audit_dependencies for security checks, so guidance is inferred rather than direct.

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 read-only and non-destructive behavior. The description adds useful behavioral context: it accepts manifest contents, filters to only notable dependencies, and is intended as a pre-work audit. No contradiction with annotations, and the added traits go beyond what annotations express.

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?

Four concise, front-loaded sentences. The first states the core purpose, the second specifies input format, the third clarifies output scope, and the fourth provides usage timing. No fluff.

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 output schema and annotations already cover return values and safety, the description is complete: it explains what it does, what to pass, what to expect, and when to run it. No significant gaps for a 2-parameter tool.

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%, so the schema fully documents 'manifest' and 'ecosystem'. The description repeats the accepted manifest formats but does not add new parameter semantics beyond the schema, so baseline of 3 is appropriate.

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 clearly states 'Audit a project's dependencies' with specific criteria (unmaintained packages, unstable APIs, documented reasons to switch). It differentiates from siblings by focusing on dependency health and returning only decision-worthy items, not full reports.

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 context: 'Run this before working in an unfamiliar codebase.' It implies a focused use case and sets expectations for output. However, it does not explicitly name alternative tools or when not to use it, though the adjacent sibling names suggest related advisory tools.

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, and destructiveHint=false, so the safety profile is covered. The description adds behavioral context by specifying the output format ('one line each') and that sources are 'official.' It does not disclose potential failure modes or edge cases, but given the strong annotation coverage, this is acceptable.

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?

The description is two sentences long and front-loaded with the tool's core purpose. Every word earns its place, and there is no fluff or repetition of schema information. It is highly concise and structured for quick understanding.

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 lookup tool with an output schema present, the description covers the essential aspects: what it returns (docs URL and GitHub repo), the format (one line each), and when to use it. It lacks details about error handling for unknown libraries, but the output schema likely documents return structure. Overall, it is sufficiently complete for an AI agent to select and invoke the tool correctly.

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 'library' and 'topic' having descriptions in the schema. The tool description does not add extra parameter-level details beyond the schema, so the baseline score of 3 is appropriate. It does re-emphasize 'library' implicitly, but no additional semantics are provided.

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 clearly states the tool returns 'Official docs URL and GitHub repo for a library,' using a specific verb (returns) and resource (library docs/repo). This distinguishes it from sibling tools like pick_library or should_i_use, which focus on selection or evaluation rather than providing links.

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 includes an explicit usage condition: 'Use when the clamped answers are not enough.' This provides clear context for when to invoke the tool, though it does not explicitly name alternative tools or state when not to use it. The sibling list is available for context, so the guidance is adequate but not exhaustive.

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?

The description adds behavioral details beyond the annotations: it returns exactly 1-2 snippets, includes gotchas, and ensures snippets are correct for the indexed version. It also notes token efficiency. These are useful, non-redundant disclosures.

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?

The description is two sentences long, front-loads the primary purpose, and avoids redundant information. Every sentence contributes value.

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 tool's simplicity, schema coverage, output schema, and annotations, the description covers all essential aspects: what it does, what it returns, and a key constraint on version correctness. It is complete for an agent to select and invoke the tool.

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 input schema already describes both parameters (library and task) with 100% coverage. The description does not add additional parameter semantics beyond aligning with the schema, so baseline 3 is appropriate.

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 clearly states the tool returns the 1-2 best code snippets for a task with a library, including gotchas, which differentiates it from sibling tools like docs_link or 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?

The description gives context about the tool's benefit (cheaper than a docs dump) but does not explicitly mention when to use it versus the sibling tools. It implies usage for quick code examples but lacks explicit exclusions or alternative tool names.

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 provide readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description adds behavioral details beyond this: it returns 1-3 libraries, includes one-line reasoning, and provides an 'honest warning about the top pick.' This is useful context about the tool's conduct and output style.

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?

The description is exactly two sentences: the first is dense with purpose and output details, the second is a clear usage directive. No redundant words or repetition of schema/annotation information. It is front-loaded and earns every word.

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 tool with only 2 simple parameters, a rich annotation set, and an existing output schema, the description covers all necessary aspects: what it does, output format, and when to use it. There is no missing critical context like permissions, side effects, or return behavior (covered by schema).

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% (both 'task' and 'ecosystem' have descriptions), so the baseline is 3. The description does not add new parameter semantics beyond the schema, but it reinforces that 'task' is a coding task and that the output recommendations depend on it. It doesn't introduce confusion or omit details.

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 uses a specific verb ('recommend') and resource ('should-i-use index'), and clearly states the output (1-3 libraries with reasoning and warning). It distinguishes itself from siblings like should_i_use (which likely evaluates a specific library) and audit_dependencies (which audits existing dependencies).

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 phrase 'Use before installing anything' provides clear timing/context. It doesn't explicitly name alternatives or exclusions, but the context implies this tool is for pre-installation selection. Sibling names are present but not referenced; a more explicit comparison would push this to 5.

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 indicate read-only and non-destructive behavior. The description adds meaningful context beyond annotations by detailing the output composition: '4-axis scores with reasons, when to skip it, and maintenance signals (last push, weekly downloads).' This gives the agent a clear expectation of the tool's response shape and evaluative tone, without contradicting 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?

The description is a single, well-structured sentence that leads with the core purpose ('Honest verdict on a specific library') and concisely lists the key outputs. Every word earns its place, with no redundancy or filler.

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 tool's simplicity (one parameter) and the presence of an output schema, the description provides a sufficient high-level summary of what the tool returns. It mentions the main components (scores, reasons, skip advice, maintenance signals) without needing to explain return format in detail, as the output schema likely handles that. It is complete for the tool's context, though it could mention that the verdict is based on these axes.

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 input schema already provides 100% coverage with a clear description of the 'library' parameter as 'Library name as published on npm or PyPI'. The description does not add any additional semantics beyond calling it 'specific library'. Since schema coverage is high, the baseline is 3; no extra value is added.

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 clearly states the tool's purpose with a specific verb and resource: it provides an 'honest verdict on a specific library' with detailed components (4-axis scores, reasons, skip advice, maintenance signals). This distinguishes it from sibling tools like 'pick_library' or 'alternatives' which likely involve comparisons or selections.

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 usage when evaluating a specific library, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. Sibling tools like 'alternatives' or 'pick_library' might be used for different tasks, but no guidance is given. The phrase 'on a specific library' hints at scope but lacks explicit direction.

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