get_app_definition_schemaGet the JSON Schema an app definition must satisfy: the membership SELECTOR (prefix rules — a slug folder + the member kinds it covers — plus explicit includes/excludes) and display metadata. Fetch this before authoring the definition for create_app / create_app_version.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| json_schema | 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?
Annotations already mark the operation read-only and non-destructive, and the description adds return-content context: it returns a JSON Schema (not the app itself) covering selector rules and metadata. This goes beyond the annotations by clarifying what the response describes and that this is a preparatory 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 main action ('Get the JSON Schema an app definition must satisfy') and then packs the necessary domain specifics into a clearly separated clause. No filler or repetition of the title is present.
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 zero parameters, a present output schema, and read-only annotations, the description is complete for an agent's decision-making: it states what will be fetched, what the schema covers, and when to call it. There is no missing prerequisite or hidden side-effect that an agent would need to discover separately.
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 tool has zero parameters, so the input schema is already complete (100% coverage). The description has no parameter semantics to add, and the baseline of 4 applies cleanly at this parameter count.
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 opens with a specific verb and resource: 'Get the JSON Schema an app definition must satisfy.' It goes beyond the title by detailing the schema's contents (membership SELECTOR prefix rules, explicit includes/excludes, and display metadata), and it names the downstream tools (create_app / create_app_version), making it unmistakably distinct from sibling definition-schema tools.
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?
It gives an explicit timing instruction: 'Fetch this before authoring the definition for create_app / create_app_version.' This clearly establishes when the tool is relevant. It does not name alternative definition-schema tools or state exclusions, but the app-specific context is sufficient for an agent to route correctly.
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.