roki_get_schemaReturn a fully dereferenced JSON Schema by name (e.g. "PaymentCreateRequest", "Payment", "WebhookEvent"). Use it to know the exact field names, types and constraints.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Schema name. Omit to list all available schemas. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by explaining 'fully dereferenced' (indicating resolved references) and the optional listing behavior when 'name' is omitted. This goes beyond annotations without contradicting them.
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?
Two sentences, front-loaded with the primary action, and packed with useful examples and behavioral notes. No filler words or redundant information.
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?
Despite having no output schema, the description explains what is returned (a fully dereferenced JSON Schema), how to use it, and when to use it. For a simple lookup tool with one optional parameter, this is 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 coverage is 100% (the 'name' parameter has a description). The tool description enhances this by giving concrete example values (PaymentCreateRequest, Payment, WebhookEvent) and clarifying that omitting the parameter lists all schemas, aligning with the schema's note.
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?
Description starts with a specific action ('Return a fully dereferenced JSON Schema'), names the resource ('JSON Schema'), and clarifies the scope ('by name'). Examples of valid schema names make it clear what the tool is for and distinguish it from sibling tools like roki_get_operation or roki_get_doc_section.
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?
States a clear use case: 'Use it to know the exact field names, types and constraints.' This tells the agent when to choose it. It doesn't explicitly list alternatives or exclusions, but given the tool's narrow scope, the context is sufficient.
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 has a clearly distinct purpose, from searching docs to validating requests to verifying webhook signatures. Even similarly named tools like roki_get_integration_example and roki_scaffold_integration are differentiated by 'complete runnable' vs 'skeleton'.
Tools mostly follow a roki_verb_noun pattern (get, list, search, validate, verify), but a few deviate with noun-only names like roki_status and roki_sandbox_info. The prefix and clear verbs make the pattern predictable overall.
18 tools is on the heavy side, but each serves a distinct role in the integration workflow covered by this server. The count is justified given the breadth of documentation, validation, sandbox, and example generation features.
The tool surface covers the full lifecycle of an integration: discovery (search_docs, list_operations), learning (guides, examples, schemas), validation (validate_request), sandbox testing (sandbox_try), verification (verify_webhook_signature), and post-integration auditing. No obvious gaps for the server's stated purpose.