brain_service_map

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 behavioral disclosure. It states the data source ('lessons tagged with service=...') and outputs, but it does not explicitly declare side effects (e.g., whether it's read-only or mutates state). The example implies a safe query, but for responsible AI use, a clear statement of no side effects or required permissions would be beneficial.

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 concise with three well-structured sentences. The first sentence delivers the core purpose and output, the second specifies the data source, and the third provides usage guidance and an example. Every sentence earns its place without redundancy 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?

Given the absence of an output schema, the description adequately explains the return values ('operators, known OOM failures, and the fixes that worked'). However, it lacks detail on the exact format (e.g., whether results are returned as a list, a map, or structured JSON). For a mapping tool, specifying the output structure more precisely would improve completeness, but it is sufficient for an AI agent to understand the tool's general 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 coverage is 100% with descriptions for both parameters. The description adds value beyond the schema by explaining the relationship between the 'service' parameter and stored lessons ('Matches the `service` tag'). It also provides concrete examples ('prometheus', 'cachly-web') and contextualizes how the output depends on the parameter. This enrichment justifies a score above the baseline of 3.

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 function: 'Map everything the Brain knows about a running service or system' with specific outputs (operators, failures, fixes). It uses a specific verb ('map') and resource ('service'), and distinguishes itself from sibling tools like brain_file_map or brain_discover through its focus on incident triage. The example reinforces the purpose by showing the mapping result for 'prometheus'.

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 recommends use for incident triage: 'when a service is misbehaving (e.g. a restarting pod)'. It implies the primary scenario without explicitly contrasting with alternatives. While it doesn't say 'use X instead when Y', the provided context is clear enough to guide when to invoke this tool. A mention of when not to use it would elevate it to a 5.

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