get_xgr_standard_schemaRetrieve machine-readable JSON schema for XGR standards.
| Name | Required | Description | Default |
|---|---|---|---|
| standard | Yes | Value for the standard parameter. |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Structured result data returned by the XGR MCP gateway. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish that this is a safe, read-only, idempotent operation (readOnlyHint, idempotentHint, destructiveHint false). The description adds no further behavioral details, such as response size or rate limits, but does not contradict annotations.
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, focused sentence that immediately states the action and resource. It contains no redundant words and is 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 the tool has one parameter with an enum, a rich annotation set, and an output schema, the description is largely sufficient. However, it omits any explanation of what 'machine-readable JSON schema' entails, which might be assumed but is not explicit.
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 schema description coverage is 100%, but the property description ('Value for the standard parameter.') adds no semantic value beyond the parameter name. The enum list is provided in the schema, so the agent can infer valid values, but the description does not elaborate on their meaning.
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 uses a specific verb 'Retrieve' and clearly identifies the resource as 'machine-readable JSON schema for XGR standards.' It effectively distinguishes this tool from siblings like get_xgr_doc or get_xgr_standard_reference, which serve different purposes.
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 no guidance on when to use this tool versus alternatives. It does not mention context, prerequisites, or cases where another tool would be more appropriate, leaving the agent without decision support.
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.
There are many overlapping session-listing tools (list_xdala_sessions, get_recent_xdala_sessions, find_latest_xdala_session, list_xdala_session_ids, get_latest_session_payload) and duplicate validation tools (validate_xdala_bundle is an alias for validate_xgr_multibundle). Descriptions help but boundaries are still unclear.
The verb_noun snake_case pattern is consistent across nearly all tools (get_, list_, create_, cancel_, validate_). Minor inconsistencies include the alias validate_xdala_bundle and interchangeable verbs like get_ vs read_ vs resolve_ for similar read operations.
With 95 tools, this is far beyond the 25+ threshold for 'too many'. While the domain is broad, the sheer number makes the tool surface unwieldy and difficult for an agent to navigate.
The tool set covers a wide range of operations: handoffs, session queries, transaction exploration, contract reads, validation, gas requests, and purchases. Minor gaps exist (e.g., no direct workflow editing tools), but overall the surface is remarkably complete.