Learn the Dao — Tao Te Ching

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

With no annotations, the description carries the full burden of behavioral disclosure. It clearly discloses the core matching behavior ('matches the SITUATION, not the words') and gives a concrete example. It could further note limitations around interpretation or result relevance, but the disclosed behavior is substantive and non-obvious.

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 appropriately sized at three sentences, front-loads the core purpose, and uses every sentence to add value: what it does, when to use it, and how it differs from a sibling. The parenthetical examples and the chapter-63 illustration enhance clarity without bloating.

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 absence of an output schema, the description discloses the tool's expected return ('the chapter... and the one line to hold'). It also covers usage context and example inputs. It does not clarify the behavior of the optional `limit` parameter or the exact return format, leaving minor gaps, but overall it is complete for a focused consultation tool.

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 0%, so the description must compensate. It richly explains the required `situation` parameter ('in their own words,' 'describes a situation') and provides examples. The optional `limit` parameter is not described, but its meaning is fairly inferable from its name and min/max constraints, and it is secondary to the tool's core purpose.

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 a specific verb and resource: 'find the chapter of the Tao Te Ching that speaks to it, and the one line to hold.' It explicitly distinguishes itself from siblings by noting it 'matches the SITUATION, not the words,' using the example that a never-shared-word situation reaches chapter 63.

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 this when someone describes a situation... rather than asking about the text.' It also names an alternative ('Unlike search_chapters') and clarifies what makes this tool different, providing concrete examples of applicable situations.

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?

With no annotations provided, the description carries the full burden. It discloses that both renderings are returned, each includes attribution and a citation string, and importantly flags that the Kooi rendering is NOT public domain. This licensing caution is valuable behavioral context beyond a simple 'get'.

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: the first states the core purpose, the second adds attribution/citation details, and the third notes the licensing constraint. Every sentence earns its place, and the most critical 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 chapter-retrieval tool with one parameter and no output schema, the description covers return content (both renderings), citations, and a licensing caveat. This is sufficient for an agent to decide when to use it and what to expect, without needing additional behavioral notes.

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 shows only one parameter 'number' with 0% description coverage. The description adds meaning by clarifying that this number refers to the chapter and reiterates the valid range (1-81). It does not fully explain behavior for invalid inputs, but for a single integer parameter with min/max, this is adequate.

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 uses the specific verb 'Get' and identifies the resource as 'one chapter (1-81)', while also specifying the two renderings (Legge and Kooi). This clearly distinguishes it from sibling tools list_chapters and search_chapters, which have different purposes.

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 intended usage is implied: call this when you need a specific chapter by number, as opposed to listing or searching. It does not explicitly state 'use this instead of list_chapters' or mention exclusions, but the clear context and sibling names provide enough guidance for an agent.

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?

With no annotations provided, the description carries the full burden. It discloses that the tool lists all chapters, includes titles and attribution blocks for each available rendering, and implies it is a read-only operation. This adds behavioral context beyond the schema, though it doesn't detail output format; for a simple list tool, this is adequate.

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, front-loaded sentence that clearly states the action, scope, and included content, with no redundant or vague language.

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 zero-parameter tool with no output schema and no annotations, the description is sufficient: it tells the agent what the tool does, what it returns, and when to use it. Minor omissions like exact output formatting are acceptable given the simplicity; the description is largely 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?

The input schema has zero parameters, so the baseline is 4. The description adds no parameter-specific details because none exist, and none are needed. It correctly focuses on the action and output.

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 uses a specific verb 'List' with a clear resource ('all 81 chapters of the Tao Te Ching') and scope ('all 81'), distinguishing it from sibling tools like get_chapter (likely single chapter) and search_chapters (likely search).

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 phrase 'Start here to orient' provides clear context for when to use the tool (initial orientation), but it does not explicitly mention alternatives or when not to use it. This is a clear usage hint without exclusions, aligning with a score of 4.

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?

No annotations are present, so the description carries the full burden. It discloses the scope ('full corpus') and the return format ('matching lines with their chapter numbers'), which adds useful context. However, it does not state whether the operation is read-only, explain limit behavior, or mention any potential variations such as case sensitivity or sorting. The basics are covered, but deeper behavioral traits are missing.

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 only two sentences, with the action and result front-loaded in the first sentence and usage guidance in the second. Every sentence serves a purpose, with no unnecessary repetition or filler.

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 relatively simple with two parameters and no output schema. The description covers the core purpose and usage, but omits parameter semantics for 'limit' and does not mention any constraints on the search (e.g., minimum query length is in schema but not described). It is adequate for a basic search tool but not fully complete given the absence of annotations and output schema.

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 descriptions cover 0% of parameters, so the description must compensate. It does explain that 'query' is a word/phrase to search for, which aligns with the schema, but it completely omits any explanation of the 'limit' parameter and its effect on results. This is a significant gap for a two-parameter tool.

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 uses the specific verb 'Search' with the resource 'full corpus', and clearly states the output (matching lines with chapter numbers). It distinguishes the tool from sibling tools such as get_chapter and list_chapters by emphasizing corpus-wide search rather than retrieving a specific chapter or listing chapters.

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 a clear usage scenario: 'when the user half-remembers a line and you need to find where it is.' This tells the agent when to use it, though it does not explicitly mention alternatives or when-not-to-use conditions. The context is clear enough for most cases.

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