Honeydew AI Documentation

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

Annotations already declare readOnly/idempotent/non-destructive/non-open-world, but the description adds substantial context beyond them: per-call statelessness with cwd resetting to /, the absence of writes/network/process control, the supported command list, the 30KB output truncation limit, and the .mdx-to-URL path mapping. These are exactly the operational facts an agent needs to avoid malformed calls.

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?

It is long, but front-loaded with the essential scoping claim and organized with bold labels (Workflow, Supported commands, statelessness, Examples) that make skimming easy. A few example lines are somewhat duplicative of the prose, which keeps it from a 5, but given the number of quirks a shell-like tool must disclose, the length is largely justified.

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?

There is no output schema, so the description must explain what comes back, and it does: output is truncated to 30KB per call with guidance to prefer targeted rg -C/head -N over broad cat, and it explains how to translate returned filesystem paths into user-facing URL paths. For a stateless, single-command sandbox tool this covers everything needed to call it correctly.

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 there is a single well-documented command string, so the baseline is 3. The description goes beyond the schema by illustrating concrete command forms, the batching guidance, the && / absolute-path chaining requirement for subdirectory work, and the explicit warning not to assume cd persists across calls, but the raw syntax itself is largely carried by the schema examples.

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 (run a query) against a precisely scoped resource (a virtualized, in-memory filesystem containing ONLY Honeydew Documentation pages) and immediately rules out what it is not (not a real shell). It also distinguishes itself from the sibling search tool by declaring itself the way to read and structurally explore pages, so an agent can separate it from search_honeydew_documentation without opening either schema.

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?

It gives explicit routing: use the search tool for broad/conceptual queries, use this tool for exact keyword/regex, structural exploration, or reading a specific page by path. It also warns against guessing paths and tells the agent the correct recovery step (rg -il) when a path does not exist, including the important note that a missing path does not mean the topic is undocumented.

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 cover read-only, idempotent, non-destructive behavior. The description adds value by explaining the return format (contextual content with titles and links) and how to extend results to full pages via the sibling tool. No contradiction with 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 concise, front-loaded with the core purpose, then usage guidance, then the explicit alternative. Every sentence serves a distinct function with no 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?

For a one-parameter read-only search tool with rich annotations and no output schema, the description adequately covers what it does, when to use it, what it returns, and how to follow up for full content. Nothing necessary for correct invocation 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?

The single parameter 'query' is fully described in the schema with 100% coverage, so the schema carries the load. The description reinforces the query semantics by listing what kinds of content the search covers, but doesn't need to add syntax or format details for a simple string 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?

The description clearly states the tool searches across the Honeydew Documentation knowledge base for relevant information, code examples, API references, and guides. It identifies the resource, the action, and the scope, and implicitly differentiates itself from the filesystem sibling tool by focusing on search results.

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 says when to use this tool (answer questions, find docs, understand features, locate implementation details) and when to use the alternative (query_docs_filesystem_honeydew_documentation for full page content), including exact follow-up commands and the .mdx path extension.

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 convey that this is a write operation (readOnlyHint=false, idempotentHint=false), and the description adds meaningful context: feedback is routed to the docs team and is scoped to documentation content. It stops short of describing what happens after submission (e.g., whether a confirmation is returned, whether it creates an issue), but it does not contradict the annotations and provides useful behavioral framing.

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, all informative. The first sentence states the purpose, the second gives concrete usage criteria, and the third excludes non-documentation feedback. No filler or redundancy; the most important information is front-loaded.

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 tool with no output schema, the description and schema together cover the essential information: what to submit, which page, what kind of feedback, and when to use it. The only minor gap is the lack of detail about the post-submission result or confirmation behavior, which is not critical but would make it fully 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 description coverage is 100%, and the parameter descriptions already explain 'path' (URL path without .mdx extension) and 'feedback' (clear description of the issue). The tool description reinforces the kind of feedback expected but adds no technical semantics beyond the schema, so the baseline score of 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 tool's function: 'Report a problem with this documentation site so the docs team can fix it.' It identifies the specific resource (documentation site) and the action (reporting a problem), and it distinguishes itself from the sibling query/search tools by being feedback-oriented. It also explicitly carves out what it is not for: product support or feedback about the tool/assistant.

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 guidance: 'Use when a documentation page is incorrect, outdated, confusing, incomplete, or has a broken example.' It also provides a clear when-not-to-use boundary: 'not for product support requests or feedback about this tool or assistant.' This is strong, actionable usage direction.

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