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coval-ai

Coval MCP Server

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by coval-ai

create_run

Launch an evaluation run by specifying an agent, persona, and test set. Optionally add tags to filter and track runs.

Instructions

Launch evaluation: agent + persona + test_set. Optionally add tags for filtering. Poll get_run until status=COMPLETED to see metrics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesThe unique ID of the agent to evaluate. Get this from list_agents.
persona_idYesThe unique ID of the persona to use. Get this from list_personas.
test_set_idYesThe unique ID of the test set to run against. Get this from list_test_sets.
metric_idsNoOptional list of metric IDs to evaluate. Uses agent defaults if omitted.
optionsNoRun configuration options
tagsNoTags for categorizing and filtering the run (max 20 tags, 200 chars each). Filter later with list_runs filter='tag="regression"'.
metadataNoCustom metadata for tracking purposes
Behavior4/5

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

Without annotations, the description reveals that the tool launches an asynchronous process and advises polling get_run for completion. This provides behavioral transparency beyond just 'create a run', though it could mention resource limits or prerequisites.

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 two sentences, front-loading the core purpose and then adding key usage hints. No redundant or unnecessary text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (7 params, nested objects, no output schema), the description covers the essential action and follow-up (polling). It could elaborate on the return value or error conditions, but it's fairly complete for a launch tool.

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 covers all parameters with descriptions (100% coverage), so the description adds minimal extra meaning. The note about tags for filtering is already in the 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 clearly states the tool launches an evaluation combining agent, persona, and test_set. It distinguishes itself from sibling tools like create_agent or create_test_set by focusing on the run action.

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 implicit guidance by mentioning polling get_run for results, but it does not explicitly state when to use this tool versus alternatives or when not to use it.

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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