meta_capabilitiesReturn a live inventory of all active endpoints and MCP tools. Use this first to discover what the API can do before making calls. Returns tool count, endpoint list, MCP-exposed tools, and usage notes. Deterministic -- no LLM cost.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -nullNew value: +{
+ "additionalProperties": true,
+ "description": "Structured JSON response. Shape varies by tool; most return an 'ok' boolean plus a 'result' or 'results' field with the tool's data payload.",
+ "type": "object"
+}Output schema / (root)Previous value: -{
- "properties": {
- "error": {
- "type": "string"
- },
- "ok": {
- "type": "boolean"
- },
- "result": {
- "type": [
- "object",
- "array",
- "string",
- "null"
- ]
- }
- },
- "type": "object"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, and the description adds useful context by noting it is 'Deterministic -- no LLM cost.' This goes beyond the annotation by explaining cost and predictability. It also mentions returning 'usage notes,' which adds behavioral context. No contradictions found.
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 three sentences, each earning its place: purpose, usage, and return details plus a cost note. It is front-loaded with the core action and is appropriately sized for a simple no-parameter tool.
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's simplicity (0 params, read-only, output schema exists), the description is complete. It covers what it returns (tool count, endpoint list, MCP tools, usage notes), when to use it, and its deterministic/cost behavior. No critical information appears missing.
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 0 parameters, so the baseline is 4 per the rubric. The description does not need to explain parameters, and the input schema is empty. The focus on return values is appropriate and is backed by the presence of an output schema.
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 opens with a specific verb and resource: 'Return a live inventory of all active endpoints and MCP tools.' It clearly distinguishes itself from sibling tools by being a meta-level discovery tool, while siblings are domain-specific (color, accessibility, etc.).
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 explicitly advises 'Use this first to discover what the API can do before making calls,' giving clear when-to-use guidance. It does not mention when not to use it, but the meta-tool nature makes that less critical. No alternatives are named, but none exist for this purpose.
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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