Fonto Docs

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, providing safety. The description adds behavioral context by stating it fetches 'full content' and explains the slug format. This is sufficient; 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?

Two sentences, no waste. First sentence states purpose and method, second sentence provides workflow guidance. Every word adds 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 has a single required parameter, clear annotations, and an output schema, the description covers necessary preconditions (find slug first), the fetch action, and the slug format. It is complete for an AI agent.

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 slug parameter with an example. The description adds semantic value by explaining the slug is 'the part of the URL after /latest/', which aids correct usage.

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' and the resource 'full content of a Fonto documentation page'. It also specifies the method 'by its slug' and distinguishes from siblings by advising to use search_fonto_docs or list_pages first to find the slug.

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 tells when to use this tool: after finding the slug via search_fonto_docs or list_pages. It guides the agent to use sibling tools first, providing clear workflow context.

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, destructiveHint false. The description adds 'returns all matches without ranking', which is consistent. However, it states filtering by 'product or ancestry keyword', whereas the input schema only filters page titles by keyword, creating slight ambiguity about the actual field being filtered.

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 sentences, front-loaded with purpose, no superfluous words. Every sentence serves a distinct role: purpose, return behavior and use case, and alternatives.

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 required parameter) and the presence of an output schema, the description covers all essential aspects: what it does, when to use, and explicit alternatives. No critical information 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?

Schema coverage is 100%, so baseline is 3. The description does not add significant parameter semantics beyond the schema; it only provides usage context ('useful when you know the product area or part of the page title'). No additional details about the keyword parameter's format or expected values.

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 specifies the verb 'Filter', the resource 'Fonto documentation pages', and the criteria (title, product, or ancestry keyword). It distinguishes the tool from siblings by explicitly mentioning search_fonto_docs for full-text search and the fonto://catalog resource for a complete listing.

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?

Provides explicit guidance on when to use ('useful when you know the product area or part of the page title') and when not to use, with direct alternatives ('use search_fonto_docs' and 'use the fonto://catalog resource').

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, idempotentHint, and openWorldHint. The description adds value by stating that results are ranked by relevance and include titles, descriptions, and slugs, which goes beyond annotations without contradicting them.

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 concise, well-structured sentences. No wasted words. Front-loaded with the core purpose and immediately useful details.

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 search tool with one parameter and an output schema, the description fully covers purpose, return format, and usage context. No gaps given the tool's complexity.

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% with a descriptive parameter name and example. The tool description does not add extra meaning beyond the schema, meeting the baseline expectation. No additional clarification needed for the single required 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?

Description clearly states the tool searches Fonto XML documentation via full-text search, notes ranking by relevance, and specifies return fields (titles, descriptions, slugs). It explicitly differentiates from siblings like get_fonto_page and list_pages by focusing on keyword lookup.

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?

Provides clear when-to-use guidance ('best for looking up a concept, API name, or feature by keyword'), but does not explicitly mention when not to use or name alternative tools for different needs. The context of sibling tools indirectly clarifies boundaries.

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