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

Discloses key behavior (fetches and caches locally), which is helpful. But with no annotations, it could detail more (e.g., idempotency, error handling, authentication needs).

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?

Two sentences, no fluff. The main action is front-loaded, and the second sentence adds helpful use-case context.

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?

Adequate for a simple add tool, but missing info on return value, behavior on duplicates, and error scenarios. No output schema makes this gap more noticeable.

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 has 100% description coverage, so baseline is 3. The description ties the url to fetching, adding marginal value, but does not elaborate on name or description beyond schema.

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 action (add), resource (llms.txt), and method (by URL). It also distinguishes from siblings like del, edit, list, view_content by focusing on creation.

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 says 'Use when the user wants to save, import, or subscribe to an llms.txt', providing clear usage context. However, it does not explicitly list when not to use or compare to alternatives.

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?

Discloses that both the entry and cached content are deleted, which is behavior beyond the schema. However, no annotations are provided, and the description does not mention permissions, reversibility, or error conditions.

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, front-loading the main action and providing a helpful hint. No unnecessary 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 one-parameter tool with no output schema, the description covers the core action and how to obtain the ID. It lacks detail on side effects or success/failure indicators but is adequate overall.

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 the parameter is well-documented with type, format, and pattern. The description adds minimal value by referencing the need for ID, but it is sufficient given high schema coverage.

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 deletes an llms.txt entry and its cached content, with a specific verb and resource. It distinguishes itself from sibling tools (add, edit, list, view_content) by focusing on deletion.

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 guidance to call 'list' first if the ID is unknown, helping the agent understand usage context. Does not explicitly state when not to use, but the instruction is clear.

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, the description carries the full burden. It discloses the re-fetch behavior when url changes, which is a useful side effect. However, it does not mention whether the update is destructive, if permissions are needed, or error scenarios. The disclosure is adequate but incomplete.

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 long, front-loaded with the core purpose, and includes a practical tip. Every sentence is necessary and free of extraneous information.

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 edit tool with 4 parameters and no output schema, the description covers purpose, prerequisite, and a key behavioral note. However, it lacks mention of what the tool returns (e.g., updated entry or success status) and error handling, which are gaps 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 input schema has 100% coverage with descriptions for all 4 parameters. The description adds no parameter-specific meaning beyond what the schema already provides (e.g., 'new unique llms name', 'new unique llms.txt url'). Thus it meets the baseline but adds no extra value.

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 it updates name, url, or description of an llms.txt entry by id. This distinguishes it from sibling tools: add (create), del (delete), list (list all), view_content (view). It specifies the resource and the action, making it easy for an AI to select correctly.

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 advises calling `list` first if the id is unknown, providing clear prerequisite guidance. However, it does not explicitly mention when not to use this tool or alternatives beyond the implicit context of having the id.

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 provided, so the description carries the full burden of disclosing behavior. It clearly states that the tool lists all entries and returns specific fields (id, name, url, description). It does not mention any limitations, sorting, or other behavioral details, but for a simple list operation with no parameters, this is adequate. The transparency is good but not exhaustive.

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 consists of two short sentences. The first defines the tool's purpose, and the second provides usage guidance. Every word is necessary, and there is no redundancy or fluff. It is well-structured and 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?

Given that the tool has no parameters, no output schema, and no annotations, the description is fully complete. It explains what the tool does, what it returns, and when to use it. No additional information is needed for an agent to select and invoke 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 has zero parameters, so schema coverage is 100%. The description does not add any parameter semantics because there are none. According to guidelines, baseline is 3, which is appropriate as there is no parameter information to enhance.

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 'list' and clearly identifies the resource as 'all saved llms.txt entries', including the fields returned (id, name, url, description). It distinguishes from siblings like add, del, edit, and view_content, which are for individual operations, by focusing on listing all entries.

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 when the user asks what's available, or to look up an id for other tools.' This provides clear context, though it does not explicitly mention when not to use it or list alternative tools. However, the guidance is precise and helpful.

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?

Although no annotations are provided, the description discloses that the returned text contains absolute URLs for 'view_doc', which is helpful. As a read operation, it lacks mention of side effects or authentication, but the verb 'Read' implies non-destructive behavior.

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?

Two sentences, zero waste, front-loaded with the core action. Every sentence adds value: purpose, outcome hint, and guidance.

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 simplicity of the tool (one parameter, no output schema), the description is complete. It explains what the tool does, what the output contains, and how to use it in conjunction with siblings.

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?

Only one parameter (id) with full schema coverage. The description adds minimal meaning beyond the schema—just 'by id' and the URL hint. Baseline 3 is appropriate since the schema already describes the parameter well.

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 action ('Read'), the resource ('stored llms.txt document'), and the input ('by id'). It distinguishes from siblings by referencing 'list' and 'view_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?

Explicitly advises to call 'list' first if the id is unknown, and notes that returned URLs can be fetched via 'view_doc', providing clear context for when to use this tool versus alternatives.

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 provided, and the description only mentions fetching content without disclosing any behavioral traits such as read-only nature, authentication requirements, error conditions, or side effects. This leaves the agent with minimal insight beyond the basic 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?

Two short, front-loaded sentences convey the action and typical usage without any superfluous words. Every sentence adds value.

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 description is adequate for a simple fetch tool with one parameter, but lacks details about the output format (e.g., plain text, HTML) or error handling (e.g., what if URL not found). No output schema exists to fill this gap.

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?

Although the schema covers the parameter with a description, the tool description adds context by specifying that the URL is 'absolute' and 'linked from a stored llms.txt', enhancing the agent's understanding beyond the schema.

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 action ('Fetch a document content'), specifies the resource ('linked from a stored llms.txt'), and differentiates from the sibling 'view' tool by noting that the URL is typically discovered via 'view'.

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 clear context: 'Typically called with a URL discovered via `view`', indicating a workflow. Does not explicitly state when not to use, but the guidance is sufficient for an agent to select this tool over siblings.

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