ai_economy_dashboard

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

With no annotations provided, the description carries the full burden. It discloses that the tool 'Returns' data and enumerates the types of information delivered, which implies a read-only operation. However, it does not mention any side effects, permissions, rate limits, or limitations. The output overview is helpful but lacks deeper behavioral context.

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 two sentences, front-loaded with the core purpose and then listing return categories. It is compact and every phrase adds information. The list is a bit long but acceptable for a dashboard that returns multiple data types.

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 there is no output schema and no annotations, the description provides a clear list of return values, which is good. However, it does not explain how the input parameters (especially metrics and date_range) map to the outputs, nor does it clarify the exact scope of 'all 10 MCPs'. The tool is moderately complex (nested date_range, array metrics), so the description could be more 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?

Schema description coverage is 67%, and the description does not add meaningful parameter details. It mentions 'usage metrics' but does not clarify what the metrics array should contain or how date ranges and organization_id are used. The description relies on the schema alone, and with only partial coverage, it fails to compensate for the missing date_range field description.

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 purpose: a cross-ecosystem analytics dashboard that returns usage metrics, compliance score, learning progress, security status, PQC readiness, and recommended actions. It uses a specific verb ('Returns') and resource ('dashboard'), and distinguishes itself from siblings like ai_governance_assess or ai_market_intelligence by focusing on an integrated view across all 10 MCPs.

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 use for organization-wide cross-ecosystem analytics via the phrase 'across all 10 MCPs', but it does not explicitly say when to use this tool over alternatives like ai_trust_score or ai_sector_compliance. There are no explicit exclusions or alternate tool recommendations, so guidance is only implicit.

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