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

With no annotations, the description carries the full burden, and it does disclose one useful trait: it returns the FULL page content rather than a snippet. It does not address page size or truncation, whether an invalid path errors or returns empty, or any cost/latency characteristics of fetching whole documents.

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?

One sentence, front-loaded with the verb and resource, with zero redundant wording. Nothing to trim and nothing buried.

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 one-parameter read tool with an output schema that covers return values, the description is nearly sufficient; the remaining gap is the missing explicit hand-off from search_documentation to this tool. The schema compensates for path semantics, so completeness is high.

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 single `path` parameter is documented with concrete examples and its provenance (returned in search results), so the schema already does the work. The description adds nothing about path format or validation beyond that, which is the correct baseline for a fully described single parameter.

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?

"Read the full content of a specific Spark documentation page" gives a specific verb (read) and resource (documentation page) and scopes it to a single page, which implicitly separates it from search_documentation. It stops short of naming the sibling outright, so an agent must infer the contrast.

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 phrase "a specific page" plus the schema note that paths are returned in search results suggests this is the follow-up call after a search. There is no explicit statement of when to use this versus search_documentation, nor any precondition about obtaining a path first.

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 supplied, the description carries the full behavioral burden and discloses almost nothing: no ranking behavior, result shape, pagination, or scope limits. The only behavioral signal is 'keyword query', which is thin for a tool with no annotation coverage.

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 no filler, so it is efficient and easy to scan. It is arguably too terse for the tool's role, but that gap is captured under other dimensions rather than as verbosity.

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 is simple: three fully documented parameters and an output schema, so return values need no explanation here. The remaining gap is routing guidance against read_documentation and any note on search behavior, which the description omits.

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 query, limit, and section parameters are already fully documented, including stemming behavior and common section values. The description adds no parameter meaning beyond that, which meets the baseline for fully covered schemas.

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 gives a specific verb and resource ('Search Apache Spark documentation by keyword query'), so an agent knows exactly what the tool does. It does not differentiate from the sibling read_documentation, leaving the search-vs-read choice to the name alone.

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?

There is no statement of when to use this tool versus read_documentation, nor any prerequisite or exclusion. The only hint is the phrase 'keyword query', which implies lookup-by-term usage but is never made explicit.

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