delete_schemaPermanently delete a schema and all its versions. Cannot be undone; datafiles or workflows bound to it keep the dangling reference and simply skip schema validation.
| Name | Required | Description | Default |
|---|---|---|---|
| schema_id | Yes | the schema id to delete |
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| deleted | Yes |
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?
Beyond the destructiveHint annotation, the description discloses irreversible deletion, the removal of all versions, and downstream consequences: bound datafiles/workflows keep dangling references and skip schema validation. This is substantial behavioral context that annotations alone do not provide.
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 front-loaded sentences carry the essential purpose, irreversibility, and downstream effects with no filler or repetition. Every sentence earns 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 destructive single-parameter tool with an output schema and strong annotations, the description is complete: it names the target, scope, irreversibility, and effect on bound resources. No critical caller decision is left undocumented.
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?
Input schema coverage is 100%, so the schema already documents the single schema_id parameter. The description adds that the deletion affects all versions of that schema, but this is a behavioral consequence rather than new parameter-format semantics. Baseline 3 is appropriate.
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 states a specific action and resource: 'Permanently delete a schema and all its versions.' It clearly distinguishes this delete_schema tool from the many other delete_* siblings by naming the exact target and scope.
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 clearly implies this tool is for permanent removal of a schema, but it does not explicitly state when to choose it over alternatives such as unpublish_schema or how it differs from deleting other resources. Usage must be inferred from the 'permanently delete' wording.
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.
Every resource family follows the same verb+noun pattern and each tool name uniquely identifies a resource-action pair (create_app vs create_app_version vs update_app vs publish_app). Closest overlaps like analyze_resource vs get_resource_graph and patch_datafile vs update_datafile are explicitly differentiated by their descriptions, so misselection risk is low despite the scale.
Names are almost uniformly verb_noun snake_case with a consistent lifecycle vocabulary: create/get/update/delete/list/publish/unpublish/version. Minor outliers like whoami and run_schedule_now are idiomatic and do not break the predictability of the set.
At 93 tools this far exceeds the calibration's 50+ extreme-mismatch case. The count is inflated by repeating create/get/update/delete/version/publish/unpublish across ten resource families; even though each family is systematic, the combined surface is very hard for an agent to navigate and keep in context.
Core CRUD/publish/version lifecycles are present for apps, workflows, endpoints, schedules, schemas, datafiles, and api templates, and dependency analysis is well covered. However, secret creation/updating, asset upload, custom-domain deletion, and version-range enumeration for several resource types are absent or left to the external dashboard, so agents hit a few manual dead ends.