Lipe Court Record

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

Annotations already mark the tool as read-only, idempotent, and non-destructive; the description adds useful behavioral context by listing what is returned: exact PDF page, reviewed evidence, provenance, permanent citation URL, and classification. It does not contradict the annotations and adds value beyond them.

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 focused sentences with no filler: the first states the tool's purpose and the second gives an actionable instruction. The key 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 single-parameter fetch operation with an output schema present, the description covers what the tool retrieves and how to use the result. It is slightly thin on prerequisites or alternatives, but the low complexity and annotations keep it nearly 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 schema has a single undocumented 'id' parameter (0% coverage), so the description carries the burden. It conveys that the id refers to a specific search result, but it does not explicitly state where the id comes from or its expected format, leaving some ambiguity.

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 names a specific action (fetch), a specific resource (one search result's PDF page, evidence, provenance, and citation URL), and identifies the operation as retrieving a single item rather than listing or searching. This clearly distinguishes it from siblings like search and get_case_overview.

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 the tool is used after a search result is selected ('one search result's'), and tells the agent to use the returned classification and citation in the answer. However, it does not explicitly state when to prefer fetch over get_case_overview or list_case_topics, beyond the implication that fetch is for detailed evidence of a single result.

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 meaningful context: the result is concise, reviewed, and includes evidence classifications and page citations. No contradiction with annotations.

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 with no filler. The first sentence states exactly what is returned and the second gives usage guidance. Every clause earns its place, and the most critical identifying 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 zero-parameter, read-only overview tool with an output schema present, the description is fully sufficient. It names the case, the content delivered, the intended audience, and the recommended follow-up actions. Nothing needed to invoke 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?

The tool has zero parameters, so the schema places no burden on the agent. The description appropriately adds no parameter details because none exist, and the baseline for a zero-parameter tool is 4.

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 ('Return') and a distinct resource ('a concise, reviewed orientation to the Lipe case with evidence classifications and page citations'). It clearly differentiates from sibling tools by positioning itself as the orientation entry point rather than a search, fetch, or topic-listing 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?

Explicitly directs when to use it: 'Use for first-time users, then search and fetch before answering detailed questions.' This provides both the trigger condition and the recommended follow-up sequence, leaving no ambiguity about its role relative 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is fully covered. The description adds the 'Lipe corpus' scope and that the tool returns suggested questions, but it does not disclose additional behavioral traits such as ordering, limits, or relationship to other tools. Given the annotations, this is adequate but not rich.

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 short sentences, front-loads the action and object, and immediately follows with a practical usage trigger. Every word 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 zero-parameter, read-only listing tool with full annotations and an output schema, the description fully covers why and when to call it. It names the corpus, the topic/evidence scope, and the user intent that should select this tool. Nothing essential 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?

The tool has zero parameters and 100% schema coverage, so parameter semantics are trivially complete. The description appropriately avoids inventing parameter details. A baseline of 4 is warranted because there are no parameters to explain.

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 specific verb ('List') and resource ('evidence topics covered by the Lipe corpus and suggested questions'), clearly identifying what the tool returns. It does not explicitly distinguish itself from siblings like get_case_overview or search, but the object and scope are concrete enough for an agent to select it.

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 an explicit trigger: 'Use when the user asks what the connector knows or what they should investigate.' It lacks a when-not-to-use or alternative routing statement, but the usage context is clear and actionable.

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 establish read-only, idempotent, non-destructive behavior, and the description does not contradict them. It adds behavioral context by indicating that searches return result IDs and that fetch must be called on those IDs before answering, which is useful beyond the structured annotations.

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 filler. The first sentence states the action and scope, and the second provides usage and follow-up guidance. 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?

Given the low complexity, rich annotations, and presence of an output schema, the description is complete enough for correct invocation. It covers what to search, when to call it, and what to do with the results, so an agent can use this tool without needing additional context.

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 defines a required string 'query' with 0% description coverage, so the description must compensate. It does so by specifying what the query searches over—case facts, testimony, positions, rulings, and jury findings—and by framing the query as a case-specific factual question. This adds meaning despite not detailing query syntax.

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 the Lipe court record for case facts, testimony, positions, rulings, and jury findings.' This clearly defines what the tool does and its scope, and the distinction from overview/topic siblings is implied through the focus on case-specific factual content.

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: 'Call this for every case-specific factual question.' It also provides a follow-up workflow with fetch on relevant result IDs. It does not explicitly contrast with get_case_overview or list_case_topics, but the use case is clear enough.

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