Recorded incidents for a hosting or API provider over a window, kept after the provider’s own status page dropped them.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| provider | No | Provider slug, e.g. cloudflare, openai, github. Omit for every provider. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that incidents are retained in the system after being dropped from the provider's status page, which is valuable context. However, it does not describe the output format, possible empty results, or any errors. It also doesn't explicitly state that this is a read-only operation, though that is implied.
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 a single, well-structured sentence that front-loads the core concept ('Recorded incidents') and includes the key differentiator. Every phrase earns its place; it is concise without unnecessary fluff.
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 annotations and no output schema, the description should explain what the return value looks like. It does not, leaving the agent uncertain about the response structure. However, the tool is a simple read-only lookup, and the schema covers parameters. The description does clarify the historical nature, which is key, but omits output details.
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 description coverage is 50%: the provider parameter is well-described in the schema, but days has no description. The tool description's 'over a window' is an indirect reference to days but does not add concrete meaning. The description does not compensate for the missing days explanation, but the parameter name and constraints provide some clarity.
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 identifies the tool as returning recorded incidents for hosting/API providers over a time window. It differentiates itself from siblings by specifying that these incidents are retained after the provider's own status page drops them, which distinguishes it from provider_incidents or other status-related tools. The scope is specific: provider and time window.
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 usage when historical incidents are needed (since they are kept after the provider's status page drops them), but it does not explicitly state when to use this tool versus alternatives like provider_incidents, nor does it specify exclusions. The context is clear but not fully explicit.
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.