evaluation_failuresRead evaluation trajectories with errors or non-positive rewards to diagnose model failures. Use it to inspect, troubleshoot, and debug underperforming evaluation runs.
Read evaluation trajectories with errors or non-positive rewards.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| offset | No | ||
| evaluation_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
readOnlyHint=true already tells the agent this is a safe read. The description's real contribution is defining what counts as a 'failure' (errors or non-positive rewards), which is genuine behavioral context beyond the annotation. It says nothing about pagination behavior despite limit/offset existing.
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?
A single tight sentence with the resource and the filter front-loaded. Every word earns its place and there is no filler.
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?
An output schema exists, so return values need not be described. However, with 0% schema description coverage and no mention of evaluation_id or the limit/offset paging semantics, the definition is thin for a tool an agent must scope correctly on first call.
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% across three parameters, so the description carries the full burden — yet it never mentions evaluation_id, limit, or offset. The required scoping parameter evaluation_id is entirely unexplained, leaving the agent to infer its meaning from the name alone.
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 specific verb (read) and resource (evaluation trajectories) plus a discriminating filter (errors or non-positive rewards), which separates it from evaluation_get and experiment_rollouts. It stops short of naming those siblings explicitly, so an agent must infer the boundary.
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?
Usage is implied by the filter phrase — call this when you want only failing/negative-reward trajectories — but there is no explicit when-to-use, when-not, or named alternative. An agent can guess the intent but gets no routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.