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

No annotations are provided, so the description must disclose behavioral traits. It describes the output content but does not explicitly state whether the operation has side effects, requires special permissions, or what happens if the investigation is not completed. 'Export' suggests a read operation but is not explicit, and error conditions are absent.

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 sentence that front-loads the verb and resource, enumerates the output sections, and contains no filler or redundant information. Every element contributes to understanding what the tool does.

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?

With one parameter and no output schema, the description covers the tool's purpose and the content of the output, but it leaves ambiguous how the Markdown is returned (e.g., string, file path) and does not address error cases for non-completed investigations. These are material gaps given the absence of annotations.

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 provides 100% coverage for the single parameter task_id, describing it as 'Task ID of a completed investigation'. The description adds no additional semantic detail about task_id, 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 states the specific verb 'Export', the resource 'completed investigation', and the output format 'Markdown post-mortem', and lists the exact sections (summary, probable cause, affected systems, next actions, checkpoint timeline). This clearly distinguishes it from siblings like investigate_incident, get_investigation, and search_incident_memory, which serve 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 description implies usage when an investigation is completed and a post-mortem export is needed, but it does not explicitly reference alternatives or state when not to use this tool. The distinction from get_investigation or investigate_incident is left to the agent's inference, so guidance is implied rather than explicit.

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 disclosure burden. It clearly indicates a read-only operation ('Fetch') and transparently handles the not-completed case ('final report if completed'). It also specifies the response includes status and checkpoint timeline, leaving little ambiguity about what the agent will 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?

A single, well-structured sentence that front-loads the action and enumerates the return contents. No filler, no redundancy. Every part 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 tool's simplicity (one parameter, no output schema, no annotations) and the schema's full parameter coverage, the description covers the return payload sufficiently. The only gap is the lack of explicit 'use after investigate_incident' guidance in the description, but the schema hints at this, so the tool definition as a whole is 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 covers 100% of the single parameter with a clear description ('Task ID returned by investigate_incident'), so the schema does the heavy lifting. The tool description adds no additional parameter meaning beyond what the schema already provides, fitting the baseline of 3 for 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 uses a specific verb (Fetch) and resource (investigation) and enumerates exactly what is returned: status, checkpoint timeline with per-step status/durations, and final report if completed. This clearly distinguishes it from siblings like investigate_incident (which starts investigations) and export_postmortem (which exports postmortems).

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 does not state when to use this tool versus alternatives, nor does it mention prerequisites or ordering. The only hint is in the schema parameter description ('Task ID returned by investigate_incident'), but this is not part of the tool description itself, so the agent receives no explicit usage guidance from the description.

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?

Without any annotations, the description carries the full burden of behavioral disclosure, and it does well: it reveals that the tool consults episodic memory, invokes an LLM to plan tool calls, queries logs/metrics/runbooks, waits by default, and returns a report. It does not in the description disclose the ticket-creation/memory-write side effects implied by dry_run, but the schema covers that and there is no contradiction.

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 wasted words: the first states the primary action and pipeline, the second states default wait behavior and return value. It is front-loaded and every clause 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?

For a complex orchestrator with no output schema, the description covers the main workflow, default waiting, and the return value, which is enough to call the tool correctly. A minor gap is the lack of explicit mention of the task_id path when wait_seconds=0 and the ability to poll via get_investigation, though the schema does document the immediate-return behavior.

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%, and the schema itself provides strong descriptions for goal, context, dry_run, and wait_seconds, including an example and side-effect clarifications. The description adds no per-parameter detail beyond the schema, so 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 opens with 'Run an AgentCore incident investigation' and then breaks down exactly what that entails: retrieving similar past incidents, planning tool calls with an LLM, querying logs/metrics/runbooks, and synthesizing a report. This clearly differentiates it from sibling tools like get_investigation, search_incident_memory, and export_postmortem, which focus on retrieval/export rather than running a full investigation.

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 makes it clear this tool is for starting a new investigation and returning a synthesized report, including the default blocking behavior. It does not explicitly name alternatives or say 'use get_investigation if you already have a task_id', but the purpose is clear enough for an agent to select it appropriately.

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 full burden. 'Semantic search' implies a non-destructive read operation, which is a useful behavioral clue. However, it does not explicitly state that it is read-only, mention any permissions, or describe limitations (e.g., result sorting, availability of historical data). For a search tool, the implicit non-destructive nature is adequate but not fully explicit.

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 sentences with no redundancy. The tool's core function is front-loaded and the usage hint follows directly. Every word contributes to understanding what the tool does and when to use it.

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 tool with no output schema, the description adequately explains what is searched (past incidents and outcomes) and gives a practical use case. It does not detail the result shape or pagination behavior, but the `limit` parameter and the nature of semantic search make it sufficient for an agent to call it correctly. It could be more complete by mentioning what fields results contain, but it is not a major 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?

The input schema already provides descriptions for both parameters (query and limit) at 100% coverage, so the description adds no additional parameter-level meaning. It simply restates the search intent. Per guidelines, high schema coverage sets the baseline at 3, and the description does not elevate beyond that.

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 and resource: 'Semantic search over AgentCore's episodic memory of past incidents and their outcomes.' This clearly distinguishes from siblings like investigate_incident or export_postmortem by focusing on historical resolution lookup. The purpose is unambiguous and immediately actionable.

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 a clear usage context: 'Use to find how similar incidents were resolved before.' This tells an agent when to invoke it. It does not explicitly mention alternatives or when not to use it, but the context is strong enough that no further guidance is needed for a simple search tool.

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