edgegate_create_pipeline

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description bears full responsibility for behavioral disclosure. It explains important behaviors: first-run compilation + linking via AI Hub, cached composite on subsequent runs, derivation of ttft_ms/tps gates, and a notable cost implication (3× CV cost). This adds substantial context beyond the raw schema.

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 concise and efficiently structured: it opens with the core purpose, then adds one non-redundant detail per sentence (LLM handling, caching, gate derivation, cost). No fluff or repeated schema content.

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 complex tool with 9 parameters, nested objects, and no output schema, the description covers the essential workflow, LLM-specific behavior, caching, and cost tradeoffs. It misses some operational details like return values or how input_specs interacts with detection, but overall it is reasonably complete.

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 only 11%, so the description compensates by explaining the roles of models, devices, and gates, and specifically clarifies the llm_compile_source vs artifact_id choice. It also explains how ttft_ms and tps gate values are derived. However, it does not cover all 9 parameters (e.g., workspace_id, promptpack_id, input_specs remain unexplained in prose).

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 'Create a new EdgeGate regression pipeline' with a specific verb and resource, and goes on to explain what the pipeline enforces (models, devices, gates). This distinguishes it from sibling tools like edgegate_run_gate or edgegate_create_bg_run.

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?

No explicit guidance on when to use this tool versus sibling alternatives. The only usage note ('For LLMs: set llm_compile_source instead of artifact_id') is an internal parameter choice, not a tool-selection guideline.

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