portkey-admin-mcp

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 feedback is 'linked via trace_id' and 'can include custom metadata for analysis', which adds useful context about data relationships and extensibility. However, it doesn't mention permissions, rate limits, or what happens on success/failure, leaving behavioral gaps for a mutation tool.

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 two sentences with zero waste. The first sentence states the core purpose, and the second adds key usage details. It's front-loaded and efficiently structured, with every phrase earning 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 mutation tool with no annotations and no output schema, the description is adequate but incomplete. It covers the what and why but lacks details on behavioral aspects like error handling, authentication needs, or response format. Given the 4 parameters and nested objects, more context would help the agent use it 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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'custom metadata for analysis' and linking to 'trace_id', but doesn't provide additional syntax, examples, or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

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 with specific verbs ('create feedback', 'capture user feedback') and resources ('trace/request', 'AI generations'). It distinguishes from siblings like 'update_feedback' by focusing on creation rather than modification, and from other create tools (e.g., 'create_api_key') by specifying the feedback domain.

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 for when to use this tool: 'to capture user feedback (thumbs up/down, ratings) on AI generations' and 'linked via trace_id'. It doesn't explicitly state when not to use it or name alternatives like 'update_feedback', but the context is sufficient for typical usage scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.