get_narrative_strength

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

No annotations are provided, so the description carries the full burden. It describes the algorithm (eigenvector centrality, rolling windows) but does not disclose rate limits, auth requirements beyond 'Pro tier', or whether the operation is destructive. It adds some behavioral context but not comprehensively.

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?

Two sentences, no fluff. The core concept is front-loaded with 'co-mention graph centrality' and method details are provided succinctly. Every part of the description adds value.

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 2 parameters, no output schema, and no annotations, the description adequately covers the tool's purpose and methodology. It does not elaborate on return format or pagination, but for a centrality measure tool, this is sufficient.

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 schema descriptions like 'Entity slug.' and 'Rolling window.' The description adds context by linking 'rolling windows' to the eigenvector centrality method but does not significantly expand on parameter meaning beyond what the schema already provides.

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 computes co-mention graph centrality using eigenvector centrality in rolling windows, specifically identifying consolidating vs fading narratives. This is a specific verb+resource+method combination that distinguishes it from siblings like track_narrative.

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 mentions 'Pro tier' which indicates access restrictions but does not provide explicit when-to-use or when-not-to-use guidance, nor does it suggest alternatives. The context is clear but lacks exclusion criteria.

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