SiteGPT Docs

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

Even though annotations already declare readOnlyHint, idempotentHint, and destructiveHint, the description adds substantial non-obvious behavior: the filesystem is virtualized/in-memory, not a real machine, no network writes or process control, each call is stateless, output is truncated to 30KB, and paths must be converted to URLs in responses. This goes well beyond what the annotations convey and contains no contradictions.

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 long but information-dense, front-loading the critical sandbox/staleness constraints before command examples and output handling. Every section serves a purpose: the distinction from a real shell, the workflow, the stateless warning, concrete examples, truncation guidance, and URL conversion.

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 has one parameter, no output schema, and moderate operational complexity, the description covers all necessary invocation contexts: what the filesystem contains, which commands work, how to search, how to read pages, how to batch reads, output limits, and how to reference results in responses. Nothing essential is missing.

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?

Although the schema covers the single `command` parameter 100%, the description dramatically enhances semantic understanding by listing supported commands, providing illustrative examples for tree, rg, head, cat, and jq, and explaining how `cd` behaves across calls. For an agent choosing command syntax, this is far more useful than the schema alone.

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 precise verb-resource pair: "Run a read-only shell-like query against a virtualized, in-memory filesystem" containing only documentation pages and OpenAPI specs. It explicitly distances the tool from a real shell, and its workflow note contrasts it with the sibling search tool, making the tool's specific role unmistakable.

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 explicit selection guidance: use the search tool for broad/conceptual queries, and use this tool for exact keyword/regex matching, structural exploration, or reading full pages. It also tells the agent how to compensate for statelessness (use absolute paths or `&&` chaining), which is the kind of practical usage guidance an agent needs.

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 readOnlyHint=true and idempotentHint=true, and the description does not contradict them. It adds useful behavioral context beyond annotations: search returns 'contextual content with titles and direct links', and it clarifies that search does not return full page content, pointing to the filesystem tool for that. This helps the agent set correct expectations about the result.

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 sentences with no filler. The core purpose is front-loaded, usage guidance follows immediately, and the alternative-tool routing is concise and concrete. Every sentence contributes something the agent needs.

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?

Despite having no output schema, the description tells the agent what to expect from results (contextual content, titles, links) and how to get full content when needed. Given the tool's simplicity and strong annotations, nothing essential 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?

Schema description coverage is 100%, so the schema already documents both query and version. The description adds no extra parameter-level detail beyond the schema, but it does contextualize that the returned path can be appended with .mdx. This is useful but not essential for understanding the parameters themselves, so a 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?

States a specific verb ('Search'), a clear resource ('SiteGPT Docs knowledge base'), and the kinds of content returned (code examples, API references, guides). It also distinguishes itself from the sibling query_docs_filesystem tool by noting search returns links/titles, while full content requires the filesystem tool.

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 says when to use: 'Use this tool when you need to answer questions about SiteGPT Docs, find specific documentation, understand how features work, or locate implementation details.' It also gives a concrete alternative for full page content, including how to invoke it with a head/cat command, so the agent knows exactly when to switch 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 indicate this is a non-read-only but non-destructive operation, and the description adds the context that feedback goes to the docs team. However, it does not disclose details like whether submission is asynchronous, whether confirmation is returned, or what happens after submission.

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 focused sentences, with the core action front-loaded and the exclusions clearly separated. Every sentence adds value and there is no redundant phrasing.

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 two-parameter feedback tool with no output schema, the description covers what the tool is for, when to use it, and when not to use it. It might ideally mention what response or follow-up the user can expect, but this is a minor gap.

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 are already well-described in the schema. The tool description adds no extra parameter meaning, but it does not need to since the schema carries the full burden.

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: 'Report a problem with this documentation site.' It clearly differentiates this tool from its siblings by scoping it to documentation feedback rather than querying or searching docs.

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 explicit when-to-use conditions: incorrect, outdated, confusing, incomplete, or broken example. It also provides explicit exclusions: not for product support requests or feedback about the tool/assistant itself.

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