get_project_storage_usageGet object-storage usage for a project: file count and bytes used against the plan limits.
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | Yes | Project ID (UUID) | |
| environment | No | Environment: staging or production (default: production) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is established. The description adds useful return-value context ('file count and bytes used against the plan limits') but does not disclose additional behavioral traits such as how usage is calculated, whether deleted files are included, or any rate limits.
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, dense sentence that front-loads the core purpose and the key return metrics. Every word contributes useful information, with no repetition of schema details or annotations.
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 read-only, two-parameter tool with no output schema, the description adequately conveys the main return values (file count and bytes used vs. plan limits). It could be slightly more complete by noting whether plan limits themselves are included in the response, but it does not leave an agent without essential calling context.
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 description coverage is 100%, so the parameters (project_id and environment) are already fully documented in the input schema. The description adds no parameter-specific detail beyond mentioning 'a project,' so the baseline of 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 clear verb and resource: 'Get object-storage usage for a project,' and specifies the exact metrics returned ('file count and bytes used against the plan limits'). This distinguishes it from siblings like get_project_usage, which is broader, and list_project_files, which lists files rather than usage totals.
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 when to use the tool: when you need object-storage usage metrics such as file count and bytes consumed relative to plan limits. It does not explicitly name alternatives or exclusion criteria, but the object-storage qualifier provides enough context to differentiate it from related project-level tools.
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.
Most tools are clearly differentiated by domain (project vs graph_project) and action (create, get, list, delete). The main ambiguity is get_project vs get_project_info, which both claim to return detailed project information. Otherwise tool boundaries are clear.
The server follows a strong verb_noun convention, with parallel naming for graph and non-graph tools (create_project/create_graph_project, deploy_staging/deploy_graph_staging). Minor deviations include bulk_create_graph_nodes and fulltext_search_graph, but patterns remain predictable.
48 tools is a heavy surface, even when accounting for the two parallel product domains (relational and graph). Many tools are near-duplicates across domains, and the count exceeds the 25-tool threshold that feels manageable. It would benefit from consolidation or sub-servers.
Both project types have full life-cycle coverage: create, schema management, deployment, rollback, and deletion, plus graph data operations including bulk, search, and traversal. Minor gaps exist, such as no update_graph_relationship and the redundant get_project/get_project_info pair, but agents can accomplish core workflows.