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

No annotations provided. Description only says 'Fetch the text' without stating read-only nature, potential errors, or side effects. Minimal behavioral insight beyond the obvious.

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 concise sentence, front-loaded with the verb. No redundant information. Efficient and clear.

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?

Adequate for a simple fetch tool, but lacks details about return format, error handling, and whether the text is plain or formatted. Could be more complete.

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 the schema already describes both parameters. The description adds no additional meaning beyond 'by URL'. 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 verb 'Fetch', the resource 'text of a PI System documentation page', and the mechanism 'by URL'. It distinguishes from siblings like search_pi_docs and list_pi_bundles.

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 explicit guidance on when to use this tool or when not to. The schema mentions the URL comes from search_pi_docs, but the description itself lacks context or alternatives.

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 of disclosing behavior. It clearly states that it lists all bundles grouped by product area. While it doesn't mention auth/rate limits, for a simple read operation with no parameters, this is adequate and non-contradictory.

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 efficiently conveys the tool's purpose and output structure. No redundant words or extraneous 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?

Given no parameters, no output schema, and a simple listing task, the description fully captures what the tool does. It states the action, resource, and grouping, which is sufficient for an AI agent to decide when to use it.

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?

There are zero parameters, so schema coverage is 100% trivially. The description adds no parameter meaning because none exist. Per guidelines, a baseline of 4 is appropriate for 0 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 clearly states the tool's action ('list'), resource ('PI System documentation bundles'), and result organization ('grouped by product area'). It distinguishes from sibling tools: 'search_pi_docs' implies searching, 'get_page' implies retrieving a single page, while this tool lists all bundles.

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 on when to use this tool versus similar tools like 'search_pi_docs' or 'get_page'. The usage context is only implied by stating it lists all bundles, but no exclusion criteria or alternative suggestions are provided.

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 full burden. It states the tool returns titles, URLs, and excerpts, which implies read-only behavior. However, it does not mention authentication requirements, rate limits, or any other operational constraints. The scope information is helpful, but more detail on behavioral traits would be beneficial.

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 front-loads the purpose and includes key details (scope, return types). Every word serves a purpose, with no redundancy or 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?

For a search tool with 3 parameters and no output schema, the description covers the core functionality and return structure. It does not mention pagination or result ordering, but the n_results parameter handles some of that. The sibling tools are not addressed, but the description is sufficiently complete for an agent to understand the tool's primary use.

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 each parameter having a description in the schema. The tool description does not add extra semantic information beyond what the schema already provides. Baseline score of 3 is appropriate when the schema does the heavy lifting.

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 verb 'Search', the resource 'AVEVA PI System documentation', and the scope ('scoped to docs.aveva.com/category/pi-system'). It also lists the return fields (titles, URLs, excerpts), which distinguishes it from sibling tools like get_page (likely fetches a specific page) and list_pi_bundles (lists bundles).

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 tool is used for searching documentation, but provides no explicit guidance on when to use this tool versus alternatives (get_page, list_pi_bundles). There are no 'when to use', 'when not to use', or 'see also' statements, leaving the agent to infer from the name alone.

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