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

No annotations are supplied, so the description carries the full disclosure burden. It does add genuine context by characterizing the corpus as empirically recorded failure modes and boundary decisions rather than general knowledge, which shapes how much the agent should trust results. However, it says nothing about result volume, ranking, pagination, or whether matching is keyword or semantic, which matters for a search tool with no structured metadata.

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?

Three sentences, front-loaded with the resource and scope, then usage and exclusion. The domain enumeration is long but each item narrows the corpus meaningfully. 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?

For a one-parameter read tool with no output schema and no annotations, the description covers what is searched, when to invoke it, and when not to. The remaining unknown is result shape and volume, which is a minor omission for a tool whose output schema is absent but whose behavior is otherwise conventional.

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% for the single required parameter, and the schema already gives examples of natural-language or keyword input. The description adds no guidance on query construction, term selection, or whether multiple concepts can be combined in one query. Baseline 3 applies since the schema does the work.

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 ("Search") and a clearly bounded resource ("operational records") plus an enumerated domain of content: distributed systems failures, MCP/A2A issues, database anomalies, concurrency bugs, runtime errors. That content scope implicitly separates it from targeted siblings like corpusfetch_lookup_exception or corpusfetch_lookup_symptom, but no sibling is named explicitly, so differentiation requires inference.

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 an explicit positive trigger ("Use when the user's problem involves infrastructure, agent-to-agent coordination, authentication, memory management, or observability") and an explicit negative exclusion ("Do not use for general programming questions"). Strong when/when-not coverage; the only gap is that it never names which sibling tool to use instead for a narrower lookup.

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