list_datasetsWhat reference datasets are available? Lists every published dataset with its title, description, record count and last-verified date. Covers regulatory thresholds, version compatibility, certification registers, licensing rules and more. Use the returned slug with search_records or get_record.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, and openWorldHint=false, which the description aligns with. The description adds valuable behavioral details: that the tool returns every published dataset with specific fields (title, description, record count, last-verified date) and covers a broad scope of reference data. It does not contradict annotations. Slightly more detail about potential pagination or limits could push it to a 5, but the description adds enough context beyond annotations to score well.
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 remarkably concise: two sentences and a usage instruction. Every sentence is informative with no filler. The opening question makes it engaging and front-loaded. There is zero waste.
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 the tool has no parameters and no output schema, the description fully covers what the tool does, what it returns, and how to use the output with sibling tools. It mentions the scope of datasets (regulatory thresholds, version compatibility, etc.), which adds completeness. Nothing is missing for a simple list operation.
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 input schema has zero parameters, so there is no parameter information to supplement. Baseline for 0 parameters is 4. The description adds value by explaining what the output contains and how it can be used, which indirectly covers the tool's semantics.
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 lists published datasets with specific attributes (title, description, record count, last-verified date). The opening question 'What reference datasets are available?' makes the purpose immediately obvious. While it mentions using the returned slug with sibling tools, it does not explicitly differentiate itself from those siblings (e.g., when to search vs. when to list), leaving slight room for ambiguity.
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 clear context: use this tool to see available datasets. It also gives explicit guidance on what to do with the output ('Use the returned slug with search_records or get_record'). However, it does not state when NOT to use it (e.g., if you already have a slug, skip this) or mention alternatives, so it falls short of a 5.
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 unique role: get_record retrieves by ID, list_datasets enumerates available datasets, search_records performs substring search within a dataset, and verify_quote checks claims against published records. There is no overlap or ambiguity between them.
All tool names follow the same verb_noun snake_case pattern (get_record, list_datasets, search_records, verify_quote), making the action and target clear and predictable.
Four tools is an appropriate size for a focused reference/verification server. Each tool serves a distinct step in the workflow, and none are redundant or missing.
The tool set covers the core flow of discovering datasets, searching records, retrieving specific records, and verifying claims. Minor gaps exist (e.g., no explicit dataset detail endpoint), but the tools are sufficient for the server's stated purpose.