Return the dataset's source, as-of date, coverage counts, and per-field confidence. Call this before relying on any answer, to check how current the underlying data is and which fields are primary-source verified.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for disclosing behavioral traits. It accurately describes the tool's output (read-only metadata retrieval) and implies non-mutating behavior through the verb 'return.' It doesn't mention latency, potential errors, or prerequisites, but for a simple provenance check this is reasonable. The phrase 'primary-source verified' adds useful context about data quality semantics beyond a mere return type.
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, both essential. The first sentence lists the exact data points returned, and the second provides actionable usage context. There is no fluff, redundancy, or unnecessary elaboration—every word earns its place.
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 tool with no parameters and no output schema, the description must clarify what the tool returns and when to use it. It fully covers this: it names the four types of information returned (source, date, coverage, confidence) and explains the purpose (checking data currency and field verification). Given the tool's simplicity and the absence of sibling overlap, this is 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 has zero parameters, and the schema confirms this (with 100% coverage). The description correctly avoids inventing parameter details, which aligns with the baseline for no-parameter tools. No semantic enhancement is needed beyond what the schema already conveys.
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 returns specific dataset metadata (source, as-of date, coverage counts, per-field confidence) using a specific verb ('Return'). This unambiguously distinguishes it from sibling tools (list_jurisdictions, lookup_jurisdiction) which focus on jurisdictional data rather than dataset provenance.
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 provides explicit usage guidance: 'Call this before relying on any answer, to check how current the underlying data is and which fields are primary-source verified.' This directly tells the agent when to invoke this tool and what to evaluate, effectively guiding tool selection. While it doesn't name alternatives, the context makes the distinction clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: dataset_provenance provides metadata about the dataset itself, list_jurisdictions returns a filtered list of states, and lookup_jurisdiction returns details for a single state. There is no ambiguity between listing and looking up, and the provenance tool is obviously separate.
Two tools follow the verb_noun pattern (list_jurisdictions, lookup_jurisdiction), but dataset_provenance deviates as a noun_compound. The naming is still readable and predictable, but the one deviation prevents a perfect score.
With exactly 3 tools, the server is well-scoped for its purpose: a focused read-only data lookup service. Each tool serves a necessary function, and there is no bloat or triviality.
The tool set covers the full lifecycle for this domain: checking data provenance, listing all jurisdictions with filtering, and retrieving details for a specific jurisdiction. No obvious gaps exist for a read-only regulatory lookup service.