Match names (probabilistic)

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

With readOnly/idempotent/destructive annotations already covering safety, the description adds meaningful behavior: Jaro-Winkler similarity, threshold semantics, and tolerance for legal-suffix drift with a concrete example. No contradiction with annotations.

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 with the main verb and resource front-loaded. The legal-suffix example is compact and illustrates the tool's value without wasting space.

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 three-parameter primitive with full schema coverage, rich annotations, and an output schema, the description covers algorithm, input semantics, and intended tolerance. No material gap prevents correct invocation.

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 descriptions already cover all three parameters (100% coverage), so the baseline applies. The description adds only contextual color about counterparty names and 'given threshold,' but no new semantics for parameter format or defaults.

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?

Clearly identifies a specific verb (score/report) and resource (two counterparty names), and names the algorithm (Jaro-Winkler) plus threshold-based output. This separates it from amount-matching and reconciliation siblings.

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 this is for fuzzy name matching, especially with legal-suffix variation, but it never names alternative tools or states conditions for choosing between them. Siblings like match_amounts_with_fx_drift and explain_match are left to the agent to distinguish.

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