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

The description adds meaningful behavioral context beyond the readOnlyHint and destructiveHint annotations: it states the operation is anonymous and read-only, warns against putting secrets in queries, and flags results as untrusted data. It also sets expectations that results are compact candidates requiring follow-up fetches. No contradiction with annotations exists.

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 dense but every sentence earns its place: purpose, when to use, when not to use, query order, output behavior, and safety warnings. Key constraints are front-loaded, with the alternative guidance and security notes placed later but still scannable.

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 complexity (9 parameters, 5 enums, an output schema, and safety-sensitive semantics), the description is remarkably complete. It covers usage criteria, exclusions, query strategy, return behavior, and security posture, leaving little an agent needs to guess before calling the tool correctly.

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 only 11%, so the description carries the burden of explaining parameters. It provides a valuable search workflow tying q, mode, product, and filters together: exact literal → product + literal → normalized symptom → filters. Several enum parameters like signal, status, and type remain only structurally defined, but their meaning is fairly inferable from the enum names.

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 and resource: "Search shared, revisioned technical Problems and Solutions." It clearly distinguishes this tool from siblings like fetch, get_changes, and whoami by situating it as a knowledge lookup for known issue types. The title and description align without tautology.

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 positive conditions (infrastructural, protocol- or tooling-related errors; before broad web research) and explicit negative conditions (trivial syntax errors, simple edits, project-specific decisions). It also provides a concrete query-order strategy, which is strong practical guidance for when and how to use the tool versus alternatives.

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