Fetch one learned schema with every field, its type, whether it is optional, and how often it actually appears — the real shape of the payload, derived from what this account received rather than guessed from training data.
| Name | Required | Description | Default |
|---|---|---|---|
| version | No | Version number, or "current" (the default). | |
| schema_id | Yes | The schema id. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It adds useful context that the schema is derived from actual account-received data, not guessed from training data, which is a behavioral trait. However, it does not disclose potential error behavior, side effects, or confirm it is a safe read-only operation beyond the verb 'fetch'.
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 action ('Fetch one learned schema') and then elaborates with relevant detail. Every word contributes value with no redundancy or filler.
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 simple 2-parameter fetch tool with no output schema, the description adequately covers what is returned (fields, types, optionality, frequency) and notes the account-specific provenance. It could mention the return format, but the summary of contents is sufficient for most use cases.
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 input schema already provides descriptions for both parameters (schema_id and version), achieving 100% coverage. The tool description does not add additional parameter-level meaning, so the baseline score of 3 applies.
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 fetches one learned schema, listing exactly what details are included (fields, types, optionality, appearance frequency). It distinguishes from siblings like list_schemas (which lists schemas) and diff_schema (which compares schemas) by focusing on a single schema's full detail.
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 a single schema's detailed structure is needed, and the context is clear. However, it does not explicitly mention alternatives or when not to use this tool, so it falls short of a 5 but has clear context.
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 resource and action: buckets, events, schemas, deliveries, forwarding, verification, replay, and waiting. Even similar pairs like latest_event vs list_events are clearly differentiated by purpose, with no overlapping responsibilities.
Most tools follow a consistent verb_noun pattern with underscores, such as create_bucket, list_events, and delete_bucket. The only slight deviation is 'latest_event', which uses an adjective instead of a verb, but it remains intuitive and does not disrupt the overall predictability.
At 17 tools, the set is slightly above the typical well-scoped range of 3-15, but the domain covers buckets, events, schemas, deliveries, configuration, and more, so each tool serves a distinct and justified purpose. The count feels appropriate for the platform's breadth rather than excessive.
The set covers the primary lifecycle for buckets, events, and schemas, including create, read, list, and delete operations. However, there is no way to update or delete a forwarding rule after creation, and no explicit update operation for bucket metadata, leaving notable gaps in managing configurations.
