delete_deployment
Request deprovisioning for one deployment by id.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| deployment_id | Yes |
Request deprovisioning for one deployment by id.
| Name | Required | Description | Default |
|---|---|---|---|
| deployment_id | 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states 'request deprovisioning,' which suggests a removal action, but it does not reveal whether the operation is irreversible, what side effects occur (e.g., associated resources), or if it's asynchronous. The description is insufficient for a deletion tool.
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, concise sentence with no wasted words. It effectively communicates the core purpose without extraneous content.
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's simplicity (one parameter, no output schema, no annotations), the description is barely adequate. It lacks details on return values, error conditions, or behavioral aspects like irreversibility, which are important for an agent to use the tool correctly.
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 0%, so the description must compensate. It adds 'by id' indicating the parameter identifies the deployment, but provides no further meaning—such as where to obtain the id or constraints (e.g., format). For a single required parameter, the description is minimally adequate but could be more informative.
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 clearly states the action ('request deprovisioning') and the target ('one deployment by id'), using a specific verb and resource. It effectively distinguishes the tool from siblings like create_deployment or get_deployment.
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 implies usage when you want to delete a deployment, but it provides no explicit guidance on when not to use this tool or what alternatives exist (e.g., cancel_job or disable_skill for similar scenarios). No exclusions or context are mentioned.
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 target distinct resources or actions, but there is some overlap (e.g., run_repository_fix vs run_repository_pipeline vs simulate_repository) that could cause confusion. Overall, descriptions help differentiate.
Tool names are primarily snake_case with a verb_noun pattern, but there are inconsistencies (e.g., single-word verbs like 'simulate', 'tokenize', and mixed prefixes like 'preview_', 'product_'). The pattern is readable but not uniform.
With 140 tools, the server is extremely over-scoped for typical MCP usage. This overwhelms agents and suggests poor separation of concerns, likely violating the principle of minimal tool surfaces.
The tool set covers a wide range of functionalities including data onboarding, simulation, decisions, repository management, and admin operations. Minor gaps exist (e.g., no update_agent_run), but core workflows are well-supported.