Real-time status for 75+ AI services (OpenAI, Anthropic, Cursor). No auth, CORS-enabled.
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Average 4.3/5 across 3 of 3 tools scored.
Each tool has a distinct purpose: current status (filterable), incident history (per service), and listing all services. No overlap.
All names follow a consistent verb_noun pattern with snake_case: check_ai_status, get_incident_history, list_ai_services.
Three tools is appropriate for a focused status-checking server, covering the core needs without excess.
Covers listing, current status, and incident history. Minor gap: no tool to drill into a specific incident's details, but not critical.
check_ai_statusAInspectCheck the current operational status of AI services tracked by Prismix. Returns status indicator (operational/degraded/outage), active incident count, and 30-day uptime. Covers 75+ services: OpenAI, Anthropic, Cursor, Mistral, Perplexity, Google AI, and more. Pass a service name/id to filter, or omit to see all.
| Name | Required | Description | Default |
|---|---|---|---|
| service | No | Service ID or partial name (e.g. "openai", "anthropic", "cursor"). Case-insensitive. Omit to check all 75+ services at once. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden. It discloses that the tool returns a status indicator, incident count, and uptime, covering 75+ services. The read-only nature is implied by 'check' and the return of status, but not explicitly stated. However, it provides sufficient transparency for an agent.
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 comprised of three concise sentences, front-loaded with the main action. Every sentence adds necessary detail (return values, coverage, parameter usage) without redundancy.
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 single optional parameter, no output schema, and simple task, the description is complete. It explains the returned fields and the scope of services, which is sufficient for an agent to invoke the tool correctly.
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 coverage is 100%, so baseline is 3. The description adds value by explaining the filter behavior: case-insensitive, partial names, optional. This goes beyond the schema's description, clarifying usage nuances.
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's purpose: 'Check the current operational status of AI services tracked by Prismix'. It specifies the return values (status indicator, incident count, uptime) and distinguishes from siblings like get_incident_history and list_ai_services.
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 gives clear guidance on when to use the optional service parameter vs omitting it: 'Pass a service name/id to filter, or omit to see all.' It does not explicitly contrast with sibling tools, but the purpose differentiation is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_incident_historyAInspectGet the recent incident history (last 30 days) for a specific AI service — past outages and degradations with impact level, start time, and resolution/duration. Useful for answering "has Anthropic been reliable lately?" or "how often does Cursor go down?". Also returns the 30-day uptime percentage and total recent-incident count.
| Name | Required | Description | Default |
|---|---|---|---|
| service | Yes | Service ID or partial name (e.g. "anthropic", "openai", "cursor"). Case-insensitive. Required. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It clearly indicates a read-only operation (retrieving history) and lists return fields (impact level, start time, resolution/duration, uptime percentage, incident count). No side effects or contradictions.
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 concise sentences with no waste. The first sentence defines the core function and return data; the second provides usage examples and additional return info. Efficiently structured.
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 one simple parameter and no output schema, the description covers the key aspects: what it retrieves (history), scope (30 days), and return fields. Lacks explicit differentiation from siblings and does not mention authentication requirements, but overall sufficient.
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 coverage is 100% for the single required parameter. The description adds minimal meaning beyond the schema, essentially repeating the schema's description of the 'service' parameter. Baseline 3 is appropriate.
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 retrieves recent incident history (last 30 days) for a specific AI service, specifying details like impact level, start time, and resolution/duration. It distinguishes itself from siblings: 'check_ai_status' presumably gives current status, 'list_ai_services' lists services, while this tool provides historical data.
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 example queries ('has Anthropic been reliable lately?') indicating appropriate use cases. However, it does not explicitly state when not to use this tool or mention alternatives like 'check_ai_status' for current status.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_ai_servicesAInspectList all 75+ AI services tracked by Prismix with their current status. Returns service ID, name, category, and operational indicator.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns service ID, name, category, and operational indicator, which is useful. However, it does not mention pagination, rate limits, or whether the list is sorted. For a simple list tool with no parameters, this is adequate but could be enhanced.
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 concise sentences, front-loaded with the core action. Every word adds value, and there is no redundancy or 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 no parameters, no output schema, and a simple list operation, the description covers the core functionality and return fields. It could mention if the list is sorted or if it returns all items at once, but overall it is fairly complete for this tool's simplicity.
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?
There are zero parameters, and the schema coverage is 100% (trivially). The description correctly omits parameter details. With no parameters to document, a baseline of 4 is appropriate.
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 all AI services tracked by Prismix with current status. It specifies verb, resource, and scope. The sibling tools check_ai_status and get_incident_history are distinct operations, so this tool is well-differentiated.
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 for obtaining a broad list of services but does not explicitly state when to use this tool vs alternatives like check_ai_status or get_incident_history. No when-not-to guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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