metrics_delete
Delete a metric
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
CompletionKit
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
B3.2/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only states 'Delete a metric' without specifying whether the deletion is permanent, whether it cascades to related data, or if any special permissions are required. This falls short of disclosing the consequences of the operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the action and contains no unnecessary words. It is optimally concise and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is insufficiently complete for a destructive operation. There is no output schema, no annotations, and no explanation of success/failure behavior, reversibility, or error conditions. While the tool is simple, the lack of context leaves the agent without a full understanding of the operation's implications.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema lists a single required 'id' parameter with no description, and schema description coverage is 0%. The description implies that the id is the metric's identifier, but it does not explicitly document the parameter's role or format. The simplicity of having a single parameter mitigates the lack of detail, but the description still adds minimal semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Delete a metric' clearly states the verb (delete) and the resource (metric), making its purpose unambiguous. It distinguishes itself from sibling tools like metrics_create, metrics_get, and metrics_update by specifying exactly the delete operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided. The description does not explain when to use this tool instead of alternatives, nor does it mention any prerequisites or side effects, leaving the agent without context for making a selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
B3/5.0
Disambiguation5/5
Each tool targets a distinct resource and action, with clear separation across agreements, datasets, judges, metrics, prompts, runs, tags, and usage. Even similar tools like datasets_create vs datasets_create_from_url and runs_generate vs runs_rerun are explicitly differentiated in their descriptions.
Naming Consistency5/5
The overwhelming majority of tools follow a consistent plural_resource_action snake_case pattern (e.g., datasets_create, metrics_update, runs_retry_failures). The only slight deviation is promptfoo_import, but it is still descriptive and does not break the overall predictability.
Tool Count1/5
With 54 tools, the server far exceeds the 25+ threshold considered too many, and approaches the 50+ extreme mismatch level. Even for a broad LLM evaluation platform, this count is excessive and likely to overwhelm agents, making tool selection more error-prone.
Completeness4/5
The toolset provides full CRUD for core resources (datasets, metrics, prompts, runs, tags) plus lifecycle operations like publish, generate, regrade, and retry. It also includes cross-cutting utilities (usage, import, provider credentials). Minor gaps exist, such as no update/delete for agreements and no cross-run response search, but these are non-essential for the primary workflows.