classifier.dev docs

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is established. The description adds valuable behavioral context: it returns a copy-pasteable example and notes the output is the 'exact request shape,' which helps an agent know what to expect. It does not contradict annotations and adds relevant information beyond the structured hints.

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 two sentences with no redundancy. The first sentence states the primary function and lists the clients; the second provides the usage context. It is front-loaded with the core purpose and every sentence 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?

The tool is simple with only 2 parameters and an output schema exists, so the description does not need to explain return values. The description covers what the tool does, when to use it, and the client options. There are no missing elements that would prevent an agent from calling 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?

The input schema covers 100% of parameters with clear descriptions (client enum and multi_label boolean). The description does not add additional parameter semantics beyond what the schema provides; it only mentions 'given client' without extra detail. Since schema coverage is high, the baseline 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 purpose: returning a copy-pasteable example for specific clients (curl, javascript, python, cli, mcp). It uses a specific verb ('return') and names the resource (classifier.dev) and the exact set of clients. It also distinguishes itself from sibling doc tools by focusing on request shapes rather than documentation.

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 states when to use the tool: 'Use this when you are about to write integration code and want the exact request shape rather than reading the whole reference.' This provides a clear condition and contrasts with an alternative (reading the whole reference), which is likely covered by sibling tools like read_doc.

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 destructiveHint, covering the safety profile. The description adds context about the document types and the ordering instruction, but does not disclose additional behaviors like pagination or performance characteristics. Since annotations carry the safety burden, the description's extra context is useful but not essential to behavioral transparency.

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 a single, reasonably concise sentence that front-loads the purpose and lists examples, then adds a clear usage directive. It is not overly verbose and every clause adds value, though it could be split into two sentences for readability.

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?

The tool has an output schema, so return values need no explanation. The description covers what the tool does, what it returns, and when to use it. It does not mention the filter parameter, but the schema covers that. For a listing tool with rich annotations, this is adequate and 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 filter parameter is fully documented in the schema. The description does not add any parameter-specific information beyond what the schema already 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?

The description clearly states the tool lists all classifier.dev documents, enumerates specific document types, and specifies the returned fields (id, title, section headings, size). It also explicitly contrasts with read_doc by instructing to call this first, distinguishing it from at least one sibling tool.

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 usage guidance: 'Call this first, then read_doc for the one you need.' This clearly tells the agent when to use this tool relative to read_doc. It does not mention search_docs or get_examples, but the primary alternative is addressed, and the 'first' directive gives a clear starting point.

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 destructiveHint, covering the safety profile. The description adds meaningful behavioral detail: the case-insensitive prefix match for section headings, which is not in the schema. It does not discuss error behavior or return format, but these are less critical given the simple read operation.

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 three sentences and front-loads the core action ('Return one document in full, or just one of its sections'). The list of ids is redundant with the schema enum, but it's not verbose. The section guidance is concise and useful. No wasted words.

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 read-only tool with no output schema, the description covers the primary use case and the optional section behavior. Combined with annotations (safety) and full schema coverage, nothing essential is missing. Minor gaps like error handling or return format are acceptable for such a straightforward operation.

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. The description adds value by listing all valid id values (also in schema) and, more importantly, specifying the prefix-match behavior for section, which is not apparent from the schema alone. This goes beyond a baseline 3.

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 clear verb ('Return') and resource ('one document' or 'sections'), with a concrete list of valid ids. It distinguishes itself from siblings (list, search, examples) by focusing on reading a single document, making the purpose unambiguous.

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 provides usage context for the section parameter ('when the whole document is more than you need'), but does not explicitly contrast with siblings like list_docs, search_docs, or get_examples. It implies this is for reading a specific doc, but no exclusions or alternative conditions are given.

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, and destructiveHint=false, so the safety profile is covered. The description adds useful runtime behavior: results are paragraphs, not whole documents, and all query words must appear. This gives an agent a realistic expectation of the output without an output schema.

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 short sentences, each earning its place: purpose, return shape, and usage guidance. The key constraint (all query words must appear) is stated compactly.

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 read-only search tool, the description covers what it does, what it returns, and when to use it. The only minor gap is the unstated meaning of 'limit,' and the absence of an output schema is mitigated by the description's return-value explanation.

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 schema describes 'query' and the description reinforces the all-words-must-match semantics, but the 'limit' parameter has no semantic description in either the schema or the description. With only 50% schema description coverage, the description partially compensates but does not fully document the other 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 opens with a specific verb and resource: 'Full-text search across every classifier.dev document.' It also states the distinctive return shape (paragraphs with document and section), which differentiates it from sibling tools like list_docs and read_doc.

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 last sentence explicitly tells the agent when to choose this tool ('Use it for a specific question') and gives concrete examples, contrasting with 'reading whole documents.' It does not explicitly name sibling tools or state when not to use it, but the usage context is clear.

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