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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds useful semantics for 'shadow AI' (not yet reviewed by anyone) and discloses that each entry includes where it was seen, but says nothing about result volume, ordering, or whether duplicate sightings are merged.

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 sentence, front-loaded with the core scope and followed by a tight parenthetical of examples plus one clause on returned location data. Nothing is wasted and nothing essential is buried.

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 with no output schema, the description supplies enough to know what domain is covered and that results carry location context. A brief note on result shape or whether entries are deduplicated would make it fully 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 tool takes zero parameters, so the baseline is 4; there is no parameter meaning for the description to supply and it correctly avoids inventing any.

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 the specific resource — unreviewed AI services and models in use across the workspace — and concretizes it with examples (LLM APIs, model endpoints, agent frameworks). It implicitly conveys listing and is clearly distinct from siblings like list_findings or check_mcp_server, though the action verb itself ('list') never appears in the description text.

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?

There is no when-to-use guidance, no mention of alternatives such as list_findings, and no stated prerequisites or exclusions. The agent must infer the tool's role from the name and scope wording alone.

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