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

With no annotations, the description carries the burden and clearly indicates a read-only retrieval operation via 'Return' and 'statistics'. It also discloses the output format as formatted markdown, which is a useful behavioral detail.

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 short and front-loaded with the main action, followed by a practical use case and return format. Every sentence adds value without unnecessary repetition.

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, zero-parameter tool with an output schema, this description provides complete guidance: what it returns, why to use it, and the output format. No critical information is missing for correct invocation.

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 has zero parameters, so no parameter documentation is needed; the baseline for zero-parameter tools applies. The description adds nothing about parameters, but none are required.

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 ('Return') and resource ('current cache statistics for the LiveDocs MCP server'). It is clearly distinguishable from sibling tools, which handle docs, search, code examples, and libraries, not cache statistics.

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?

Explicitly provides use cases: understanding cache hit rates and diagnosing repeated query latency. It does not mention exclusions or alternatives, but the sibling tools make the choice unambiguous.

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?

With no annotations, the description carries the burden of behavioral disclosure. It explains that the tool hunts for code-rich pages and extracts/format fenced code blocks, which is useful. However, it does not disclose whether results are verified, whether network/rate-limit behavior applies, or any caveats about 'working' examples.

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 with no filler. The core purpose is front-loaded, and the second sentence earns its place by differentiating from a sibling 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?

The schema covers all parameters, an output schema exists, and the description covers purpose, behavior, and the main alternative. Given this is a read-oriented retrieval tool, the description is largely complete. Minor gaps like rate limits or validation expectations prevent a 5.

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 all four parameters. The description adds context about the tool's overall behavior but does not add meaning to individual parameters beyond what the schema already provides. 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?

Description uses a specific verb and resource: 'Fetch working code examples for a specific library feature from official docs.' It clearly distinguishes itself from the sibling get_docs() by emphasizing code-rich pages and fenced code block extraction.

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 'Unlike get_docs()' sentence explicitly tells the agent when to prefer this tool over a key alternative. It implies use when code examples are needed rather than general documentation. It does not describe conditions for other siblings like search_docs_multi, but the primary routing decision is clear.

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?

With no annotations provided, the description carries the full burden for behavioral disclosure. It states the tool fetches live content directly from official websites, which signals a read-only network operation and distinguishes it from cached knowledge. However, it does not disclose potential failure modes, rate limits, or that results may vary by version (though the schema covers versioning).

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 first sentence is strong and informative, but the following paragraph includes promotional phrasing ('Solves the core AI-engineer problem') and repeats the 'latest content' idea. The description remains compact, yet some redundancy could be trimmed without losing meaning.

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?

The tool has a rich input schema and an output schema, so return format is handled structurally. The description covers the core purpose and use context, and the schema handles parameters and prerequisites like the list_libraries reference. Only minor operational detail such as error behavior is missing, but overall it is sufficient for correct invocation.

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 documents all 4 parameters with descriptive help (100% coverage), so the description adds little parameter-level detail. It mentions 'API signatures, parameters, and examples' generically but does not go beyond what the schema already provides. Baseline of 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?

The description opens with a specific verb-object pair: 'Fetch real-time, up-to-date documentation from the official docs site of a library.' It clearly identifies the resource (documentation) and source (official docs site), and explains the benefit (latest API signatures, parameters, examples). Though it doesn't explicitly contrast siblings, the function is unambiguous and distinct from the sibling names.

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 provides a concrete trigger for use: it solves the problem of LLMs having outdated training data by fetching live content. This clearly implies using the tool when current documentation is needed. It doesn't mention alternatives or exclusions, but the context is explicit enough for an agent to know when to invoke it.

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?

No annotations are provided, so the description carries the burden of behavioral disclosure. It fully describes the enumerative, non-mutating intent ('List all') and adds the concrete scope of '45+' libraries, but does not explicitly confirm absence of side effects or mention any permission or error behavior.

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 concise sentence that leads with the action and resource, then states the optional filter. There is no redundant or filler content, and every phrase earns its place.

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 list tool with one optional parameter, full schema coverage, and an output schema, the description is sufficient. It tells the agent exactly what the tool returns access to and how to narrow results, without needing extra detail.

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 the only parameter with 100% coverage ('Optional category filter string'), so the description adds marginal semantic value by repeating the filtering idea. A baseline of 3 is appropriate because the schema fully handles parameter documentation.

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'), a resource ('documentation libraries'), and a scope ('all 45+', 'optionally filtered by category'). This distinguishes it clearly from sibling tools like get_docs and search_docs_multi, which target retrieval of docs rather than enumeration of libraries.

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 clearly conveys the primary usage context: enumerating the available libraries and optionally narrowing by category. It does not explicitly name alternatives or exclusion criteria, but the purpose is unambiguous enough for an agent to know when to call it.

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?

No annotations are provided, so the description carries the behavioral burden. It does convey a key behavior: the same query is searched 'simultaneously' across multiple libraries, and it implies a read-only search operation. However, it does not disclose the underlying Serper API dependency, potential rate limits, or what happens when a library key is invalid. These are meaningful gaps given there are no 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 compact and front-loaded with the core purpose. The core sentence is immediately followed by a clear 'Ideal for...' usage signal and three illustrative examples. Every sentence earns its place, with no filler or redundancy.

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?

The description, combined with a fully documented schema and an output schema, covers purpose, usage, and query framing well. It is nearly complete for a multi-library search tool. The main missing contextual piece is explicit routing guidance to get_docs for single-library searches or to list_libraries when library keys are uncertain, but these are minor gaps.

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 input schema already documents query, libraries, and serper_api_key well. The description adds helpful real-world examples of query phrasing and reinforces the 'same query' semantics, but it does not materially extend parameter meaning beyond what the schema already provides. 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 opens with a specific verb and resource: 'Search the same query across multiple libraries simultaneously.' It clearly identifies what the tool does and immediately distinguishes it from single-library alternatives like get_docs by emphasizing multi-library search. The example queries reinforce the purpose and add concrete framing.

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 explicitly states when to use it: 'Ideal for comparing how different frameworks handle the same concept.' It provides three concrete example query patterns, which strongly guide an agent on intended usage. However, it does not explicitly say when not to use it or point to get_docs for single-library searches, so it stops short of a full 5.

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