iris-eval/mcp-server

Evaluate Runs

evaluate_runs

Idempotent

Re-score every trace in a run under current rules into a new run, preserving the source so rule changes are compared, not overwritten.

Instructions

Re-score every trace in a run under the current rules into a new run, so a rules change is compared, not overwritten.

What it does. Writes the verdicts into a NEW run; the source run is never modified. Traces already scored under the current ruleset are skipped, so a repeat call does no work. Local, no model call. Full detail: iris://capabilities.

When not to use it. To score a new execution (log_trace, then evaluate_output).

Returns. JSON: source_run, run, ruleset_hash, traces, evaluated, already_current, failed, passed, not_checked, summary, rules_changed.

Errors. IRIS_INVALID_ARGUMENT (empty run id, or the target run holds verdicts); IRIS_UNKNOWN_TRACE (source run empty); IRIS_STORAGE_ERROR. Unscorable traces are listed in failed. Failures return {"error":{code,message,recovery}} with isError; follow recovery.

Siblings. compare_runs — compare the two runs after; evaluate_output — re-score one trace; get_traces — read a run.

Input Schema

Name	Required	Description
`run`	Yes	the run to re-score — whatever you passed as `run` on log_trace
`into`	No	Run id for the new verdicts; defaults to <run>+reeval-<ruleset hash>, stable across repeats
`label`	No	a name for the new run, shown in listings

Output Schema

Name	Required	Description	Default
`run`	Yes
`failed`	Yes
`passed`	Yes
`traces`	Yes
`summary`	Yes
`evaluated`	Yes
`source_run`	Yes
`not_checked`	Yes
`ruleset_hash`	Yes
`rules_changed`	No
`already_current`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.10.1Oct 26, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations declare write, idempotent, non-destructive; the description adds the important context beyond that: verdicts go to a NEW run, the source run is never modified, already-current traces are skipped so repeats are no-ops, and it is local with no model call. It also enumerates error codes and recovery behavior, which annotations cannot convey.

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?

Front-loaded with the core behavior, then sectioned into What it does / When not to use / Returns / Errors / Siblings. Despite its length, every line carries actionable information and is scannable rather than rambling.

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

Completeness5/5

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

Covers scope, safety, idempotency, error modes, and sibling routing. An output schema exists so the Returns list is a bonus rather than a requirement, and no behavior an agent needs to call this correctly is missing.

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?

Schema coverage is 100% so the baseline would be 3, but the description adds real meaning: 'run' is whatever was passed to log_trace, and 'into' defaults to a stable <run>+reeval-<ruleset hash>. It stops short of explaining 'label' explicitly, so it is not a full 5.

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?

States a specific verb and resource ('re-score every trace in a run under the current rules into a new run') and immediately clarifies the non-destructive semantics ('compared, not overwritten'). This distinguishes it cleanly from evaluate_output and compare_runs without opening any schema.

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

Usage Guidelines5/5

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

Has an explicit 'When not to use it' section pointing to the correct alternative (log_trace, then evaluate_output) and a 'Siblings' block that routes to compare_runs, evaluate_output, and get_traces. When-to-use, when-not, and alternatives are all present.

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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