Mintlify Docs

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

Annotations already declare readOnlyHint/idempotentHint/openWorldHint=false, and the description still adds substantial non-obvious behavior: statelessness (cwd resets to /, no variable/alias persistence), no writes/network/process control, 30KB output truncation with guidance to prefer targeted reads, and the path-to-URL mapping needed for citations. That is real context beyond the structured fields.

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 critical constraints (sandbox, read via head/cat, don't guess paths, stateless) under bold headers, so skimming works. It loses a point for the exhaustive enumeration of 25 OpenAPI file paths, which could have been expressed as a `/openapi/{lang}/{name}.json` pattern, and for examples that repeat commands already introduced in prose.

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 carries the return-value burden and does: 30KB truncation limit, batching advice, and the path→URL conversion rule for citing pages. For a single freeform-string tool, nothing an agent needs in order to call it correctly 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?

With a single freeform `command` string at 100% schema coverage, the description goes well past the schema by enumerating the supported command set (rg, grep, find, tree, ls, cat, head, tail, stat, wc, sort, uniq, cut, sed, awk, jq), noting `--help` availability, and supplying seven concrete invocation examples. An agent can construct a valid command without guessing.

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 (run a read-only shell-like query against a virtualized in-memory filesystem of Mintlify docs and OpenAPI specs) and immediately bounds scope with 'ONLY' and 'NOT a shell on any real machine.' It explicitly distinguishes itself from the sibling search_mintlify by naming search as the tool for broad/conceptual queries and this one for exact matching and structural exploration.

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 an explicit routing rule ('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'), and explains the non-obvious fact that there is no separate get-page tool — reading is done via head/cat on .mdx paths. It also pre-empts the main failure mode: a missing path does not mean undocumented, so use rg -il.

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 real value beyond that by disclosing the return shape (contextual content with titles and direct page links) and the path convention for deepening a search hit. It stops short of anything about result limits, ranking, or pagination, which keeps it off 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?

The purpose and use cases are front-loaded in the first two sentences, and the sibling handoff comes last where an agent needs it. The parenthetical `.mdx` example is slightly wordy but earns its place because it prevents an otherwise likely failure on the follow-up read.

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 carries the burden of describing returns — and it does, explaining that results are contextual content with titles and direct links. Combined with explicit sibling routing and the follow-up read recipe, an agent has everything needed to call this tool and act on its results 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%, so both parameters are already documented in the schema and the baseline is 3. The description does not add syntax, query-construction advice, or any mention of the `language` filter — it only frames the search broadly. Adequate, but it leaves the schema to do all the parameter work.

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 ("Search across the Mintlify knowledge base") and enumerates the content types returned — information, code examples, API references, guides. It also explicitly distinguishes itself from the sibling tool by routing full-page reads to query_docs_filesystem_mintlify, so an agent can pick correctly 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 states concrete when-to-use cases (answering Mintlify questions, finding docs, understanding features, locating implementation details) and names the alternative with the exact condition that selects it: use query_docs_filesystem when the full page content is needed. It even supplies the mechanical follow-up recipe (append `.mdx`, e.g. `head -200 /some/page.mdx`), leaving nothing to inference.

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=false, destructiveHint=false, openWorldHint=true, and idempotentHint=false, so the write/effect profile is covered. The description adds useful outcome context ('so the docs team can fix it') but says nothing about confirmation, rate limits, or what happens to submitted feedback, so it only marginally extends the structured data.

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 tight sentences, front-loaded with the action and purpose, followed by trigger conditions and exclusions. Every sentence earns its place 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 two-parameter feedback tool with no output schema, the definition covers purpose, triggers, exclusions, and target audience completely. Annotations handle the mutation/effect profile, so nothing an agent needs to call this correctly 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 description coverage is 100% and both parameters are well documented in the schema itself (path format, feedback content). The description adds no parameter-level detail beyond what the schema provides, so the baseline of 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+resource ('Report a problem with this documentation site') and makes the target explicit ('feedback about the documentation content itself'). It is clearly distinguishable from the read-only sibling tools query_docs_filesystem and search_sorsa_developer_platform, which retrieve rather than submit.

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 enumerates the triggering conditions ('incorrect, outdated, confusing, incomplete, or has a broken example') and adds clear exclusions ('not for product support requests or feedback about this tool or assistant'). This is a textbook when-to-use / when-not-to-use statement.

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