Mintlify Docs

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

Annotations already cover the safety profile (readOnly, idempotent, closed-world, non-destructive), and the description adds substantial context beyond them: per-call statelessness with cwd resetting to `/`, no shell variables/aliases/history, 30KB output truncation, the supported command whitelist, and the absence of writes/network/process control. The statelessness caveat is behavior an agent would otherwise get wrong on the second call.

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?

Front-loaded with the purpose and the critical 'not a real shell' disclaimer, and organized under bold headers with concrete examples. The one inefficiency is the ~20-item enumeration of OpenAPI spec paths, which is discoverable via a single `ls /openapi` and bloats the description; otherwise every paragraph earns its place.

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?

With no output schema, the description carries the return-side burden and does so: it documents the 30KB truncation limit, recommends targeted `rg -C`/`head -N` over broad `cat`, and instructs converting `.mdx` paths back to URL paths for user-facing citations. Nothing an agent needs to call this correctly or interpret its output 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% and there is only one parameter, so the baseline is 3, but the description materially deepens it: it explains what the command string should be composed of, supplies worked examples, and — critically — notes that statelessness forces chaining with `&&` or absolute paths rather than relying on `cd`. That is real semantic guidance the schema's one-line description does not carry.

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+resource with explicit scope: a read-only shell-like query against a virtualized in-memory filesystem containing ONLY Mintlify docs and OpenAPI specs. It actively distinguishes itself from the sibling search_mintlify by declaring 'there is no separate get page tool' and positioning itself as the read/exact-match path.

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?

Explicit routing guidance: 'Start with the search tool for broad or conceptual queries... Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path.' It also warns against guessing paths and gives the recovery action (`rg -il`) when a path fails, so both when-to-use and when-not are covered.

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 and openWorldHint=false, so the safety profile is covered. The description adds non-annotation context: the return shape ('contextual content with titles and direct links') and the concrete follow-up convention of appending '.mdx' to a returned path before reading it. It does not mention result limits, ranking, or pagination, so it stops short of a 5.

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?

Front-loaded with purpose, then usage conditions, then the sibling handoff — a sensible ordering with no filler. The trailing parenthetical with the head/cat example is slightly verbose, but it carries concrete operational value rather than padding.

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, yet the description covers what comes back (titles and direct links) and how to escalate to full content, which is the key unknown for a search tool. An agent can call it and act on the results without further information.

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 documented there, including the language example and the 'en' default. The description adds nothing about query syntax or language behavior, so the baseline 3 applies.

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 and resource ('Search across the Mintlify knowledge base') and enumerates the content types retrieved (code examples, API references, guides). It is clearly distinguishable from query_docs_filesystem_mintlify, which is named as the tool for full-page reads.

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?

Gives explicit when-to-use conditions ('answer questions about Mintlify, find specific documentation, understand how features work, locate implementation details') and an explicit alternative with the triggering condition ('If you need the full content of a specific page, use the query_docs_filesystem tool'). Routing is unambiguous.

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.