Agent Utility Network

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

There are no annotations, so the description carries the full burden. It does disclose a key behavior: normalization is deterministic and produces a canonical form. However, it does not explain what canonicalization entails (e.g., trimming, case folding, handling of unknown entities) or error 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?

A single sentence that is front-loaded and contains no filler. Every word contributes to explaining the tool's purpose and behavior.

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 one-parameter normalization tool with no output schema, the description adequately states input and output conceptualization. It could add edge-case behavior or examples, but the low complexity makes this mostly 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 0%, but the description compensates by clarifying that the sole parameter is an organization/entity name. For a single self-descriptive parameter with minLength 1, this is sufficient semantic guidance.

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 a specific verb ('Normalize') and names the resource ('organization/entity name') plus the intended outcome ('deterministic canonical form'). It clearly differentiates from unrelated siblings like json_repair and network_capabilities.

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?

No guidance is given about when to use this tool versus alternatives, when not to use it, or what conditions make it the right choice. The sibling tools appear unrelated, but the description still provides no usage context.

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 transforms malformed JSON into valid structured JSON, but it does not mention behavior when repair fails, error handling, or whether the operation is lossy. For a transformation tool, these are notable gaps.

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, efficient sentence with no redundant words. It fronts the key verb and follows with the output. Every word contributes meaning.

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 (one string parameter, returns valid JSON), and the description covers input and output. However, without an output schema or annotations, it would be stronger to state the return type explicitly and mention failure behavior. It is mostly complete for its simplicity.

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 only provides the parameter name 'input' with type string and minLength, with no description (0% coverage). The description compensates by indicating that the input is the malformed JSON to be repaired, adding needed semantics to the parameter. It does not, however, give further detail on format or constraints.

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 a specific verb 'Repair' and clearly identifies the resource as 'common malformed JSON', and states the outcome ('return valid structured JSON'). It is clear and distinct in purpose, though it does not explicitly distinguish itself from sibling tools like entity_normalize or network_capabilities, which are contextually unrelated.

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 only states what the tool does. It gives no guidance on when to use it versus alternatives, nor any exclusions or prerequisites. The usage context is only implied by the verb 'repair'.

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 are absent, so the description carries the full burden. It only states 'Discover,' implying a read-only operation, but does not describe what the tool returns, whether it requires any network or auth setup, or any side effects. This is a minimal disclosure with no added behavioral context.

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 entire description is a single, focused sentence with no extraneous words. It front-loads the action and resource, making it easy to parse.

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 tool's zero-parameter schema and lack of output schema, the description is largely sufficient for making a call. However, it does not state what the response will contain or how the discovered utilities relate to the sibling tools, leaving minor gaps.

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 tool has zero parameters and schema description coverage is 100% (vacuously). Per the baseline for 0-param tools, a score of 4 is appropriate; the description adds nothing about parameters but none are needed.

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 with a specific verb ('Discover') and resource ('paid utilities available on the Agent Utility Network'). It distinguishes itself from sibling tools (entity_normalize, json_repair, text_extract_signals) which are specific utility operations, whereas this is about network capabilities.

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 context that this tool is for discovering paid utilities, but gives no explicit instructions on when to use this tool versus alternatives. It does not mention any exclusions or use-case conditions, leaving the agent to infer that it should be used before or instead of specific utility tools.

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 behavioral burden and correctly states the core non-destructive operation and extraction categories. However, it does not disclose what happens with no matches, whether duplicates are preserved, whether dates are validated, or what shape the extracted result takes.

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?

Single sentence, front-loaded with the verb and resource, and every phrase adds discrimination by specifying exact extraction categories. There is no filler or repetition.

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 and the description is clear about input and extraction targets, but there is no output schema and the description does not describe the return format or empty-result behavior. This leaves some runtime uncertainty for an agent trying to predict the tool's response.

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% and the schema only says 'text' is a string with minLength 1. The description compensates by clarifying that the input is free-form text to be scanned and what patterns to look for, giving the lone parameter meaningful semantics beyond its name.

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 a specific verb ('Extract') with a clear resource ('text') and enumerates exact signal types: URLs, emails, money amounts, and ISO dates. This clearly distinguishes it from siblings like entity_normalize and json_repair, which target different transformations.

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 implies its use case—extracting discrete signal types from unstructured text—but does not explicitly state when to choose it over sibling tools. No alternatives or exclusions are mentioned, so the agent must infer the boundary from the tool name and sibling context.

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