list_readings
List every reading this server can return, what each one measures, and which require a key. Call this first if unsure which tool answers a question.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
List every reading this server can return, what each one measures, and which require a key. Call this first if unsure which tool answers a question.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It reveals that the tool lists available readings and notes key requirements, implying a read-only scope. However, it does not mention return format, pagination, or whether the listing itself requires authentication, leaving some behavioral aspects unspecified.
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 no redundancy. The opening verb 'List' immediately conveys the action, and the second sentence adds practical usage guidance. 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 zero-parameter discovery tool with no output schema, the description includes the core elements: what it lists (all readings), what details it provides (measures and key requirements), and when to use it. This is complete for the tool's simple, meta purpose.
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, so the baseline for this dimension is 4. The description appropriately omits parameter details, as the schema already confirms no parameters and coverage is 100%.
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 states a specific verb 'List' and resource 'every reading this server can return', along with what each measures and key requirements. This clearly distinguishes it from sibling tools that focus on individual readings or analysis, establishing its role as a comprehensive index.
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?
Explicitly instructs 'Call this first if unsure which tool answers a question', providing a clear when-to-use scenario. However, it does not list exclusions or name alternative tools, so it lacks the full 'when-not/alternatives' specificity for a top score.
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 targets a distinct query type: package status, stack review, provider incidents, model pricing, watchlist changes, etc. Even similar-sounding tools like check_package and check_stack are clearly differentiated by granularity (single package vs. whole manifest). The descriptions further remove ambiguity.
All names use lowercase snake_case, but the pattern is mixed: some are imperative verb_noun (check_package, find_model, watch_add) while many are noun phrases (advisory_severity, provider_incidents, runtime_deadlines). This is readable but not a consistent verb_noun style, so there is noticeable inconsistency.
At 31 tools, the count exceeds the 'too many' threshold (25+). While the domain is broad, the agent must navigate a large surface with many similarly scoped utilities, making selection harder. A more consolidated set (e.g., grouping related readings) would improve appropriateness.
The tool surface covers a wide range of supply-chain intelligence: package advisories, provider status, model pricing, runtime EOL, and watchlist changes. The only notable gap is lifecycle management for the private watchlist (e.g., no watch_remove or watch_list), but the overall coverage is strong.