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codebench_delete

Delete a specific run by ID or all runs for a benchmark. Clean up code review and release tracking data with a single command.

Instructions

Delete a single run or all runs for a benchmark.

Args:

  • run_id: Delete a specific run (e.g. "BE-1")

  • benchmark: Delete all runs for a benchmark name

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
run_idNo
benchmarkNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.2.2

TDQS

A3.6/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

There are no annotations, so the description carries the full burden of behavioral disclosure. It states the destructive nature of the operation, but it omits important behavioral details: whether deletion is permanent, whether benchmark deletion cascades to all related runs, what happens with invalid IDs, and what response is returned. This is a significant gap for a destructive mutation tool.

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 compact and front-loaded: the core purpose appears in the first sentence, and the parameter explanations are presented as a minimal bullet-like list. Every sentence earns its place, with no redundant fluff or restatement of the tool name.

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?

Given that this is a destructive tool with no annotations and poorly documented parameters, the description is too thin. It does not state whether arguments are required, what happens if both are provided, whether deletion is reversible, or the effect of deleting a benchmark on its associated runs. The agent has enough to form a basic intention but not enough to invoke the tool confidently in edge cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Since schema description coverage is 0%, the description must compensate, and it does by explaining each parameter's meaning with a concrete example ('BE-1') and distinguishing the two deletion modes. It does not specify constraints like mutual exclusivity or the requirement to provide one of the parameters, but it adds valuable meaning beyond the bare schema.

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 states a specific verb ('Delete') and resource ('a single run or all runs for a benchmark'), clearly distinguishing the tool's two operational modes. This allows an agent to understand what the tool does and how it differs from sibling codebench_* tools without ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear parameter-level usage guidance: run_id deletes a specific run, benchmark deletes all runs for a benchmark. However, it does not explicitly state when to prefer this tool over alternatives, does not say that at least one parameter must be provided, and does not explain what happens if both are supplied. The usage context is implied rather than explicit.

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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