| name | optimize-mcp-tool-metadata |
| description | Run AI-driven tool-metadata optimization experiments for MCP servers using runVariantExperiment. Use when asked to improve tool descriptions, input schemas, or parameter descriptions, fix tool triggering, optimize tool discoverability, or run variant experiments against an eval dataset. Produces a measured, regression-guarded improvement proposal — never edits server source unless explicitly asked. |
| metadata | {"author":"gleanwork","version":"1.0.0"} |
Optimize MCP Tool Metadata
Drive runVariantExperiment from @gleanwork/mcp-server-tester to find tool-metadata changes — tool descriptions, input-schema documentation, parameter descriptions — that measurably improve LLM tool triggering. You (the agent) supply the judgment — which rewrite to try next — and the library supplies the mechanism: baseline runs, variant injection, comparison, ranking, regression guarding, and a structured proposal.
Scope: what variants can and cannot change
Overrides change what the host sees, not what the server accepts. The LLM forms tool calls against the overridden metadata, but those calls execute against the real server. Variants must stay wire-compatible:
- Safe to vary: the tool
description; descriptive text inside inputSchema — property descriptions, enum documentation, examples, format hints.
- Never vary: parameter names, types,
required arrays, or schema structure. The server still validates real calls — structural changes measure server rejections, not discoverability.
- Out of scope entirely: tool behavior, response shapes, auth, transport, server builds. Those need project-based A/B testing (different server configs via Playwright projects, or
runServerComparison), which is a different workflow.
Operating Rules
- Never mutate the eval dataset. It is the behavioral contract. Variants are runtime data passed via
toolOverrides.
- Never edit the MCP server source unless the user explicitly asks for source remediation. Your deliverable is the experiment result's
proposal.
- Respect the regression guard. A variant that fixes two cases but breaks one is not a win. Leave
allowRegressions at its default (false) unless the user accepts trade-offs.
- Mind cost. Every candidate is a full eval run (cases × iterations × LLM calls). Prefer few, well-reasoned variants per round over shotgun spreads.
Prerequisites
- An
mcp_host eval dataset with toolsTriggered expectations (use the write-mcp-host-eval skill to create one).
npm install ai @ai-sdk/<provider> and the matching API key env var.
Step 1 — Establish what you're optimizing
Pick the metric before proposing anything:
| Metric | Use when |
|---|
passRate | General improvement across all expectations (default). |
toolRecall | The LLM misses required tools — descriptions are not discoverable. |
toolPrecision | The LLM calls extra tools — descriptions over-trigger or overlap. |
toolF1 | Balance both. |
Diagnose first: run the dataset once, read caseResults[].mcpHostTrace (calls marked unexpected, tools in missed) to see which tools mis-trigger and why.
Step 2 — Propose variants from evidence, not vibes
Good variant hypotheses come from failure analysis. Each maps to a metadata surface:
- Missed triggers (
missed tools) → tool description: it doesn't match user vocabulary. Add concrete trigger phrasing: what the tool finds, when to use it, example asks.
- Over-triggering (
unexpected calls) → tool description: too broad or overlaps a sibling tool. Add "Use this ONLY for…" scoping and explicit "do not use for…" contrast.
- Wrong arguments → input-schema documentation: property descriptions are vague. Describe each parameter's expected shape with an example value (text only — never change names, types, or
required).
Each variant needs a stable id and a description of the hypothesis it tests — these flow into reports.
Step 3 — Run the experiment
Static A/B (one round, fixed candidates):
import { test, expect } from '@gleanwork/mcp-server-tester/fixtures/mcp';
import {
loadEvalDataset,
runVariantExperiment,
} from '@gleanwork/mcp-server-tester';
test('optimize search description', async ({ mcp }, testInfo) => {
const dataset = await loadEvalDataset('./data/host-evals.json');
const result = await runVariantExperiment(
{
dataset,
metric: 'toolRecall',
defaultLlmIterations: 10,
variants: [
{
id: 'search-v2-trigger-phrases',
description: 'Hypothesis: missed triggers due to vocabulary gap.',
tools: {
search: {
description:
'Search internal company knowledge — documents, policies, wiki pages, announcements. Use when the user asks to find, look up, or locate company information by topic.',
},
},
},
],
},
{ mcp, testInfo }
);
console.log(JSON.stringify(result.proposal, null, 2));
expect(result.proposal?.recommendation).not.toBe('reject');
});
Iterative loop (you react to each round's evidence):
const result = await runVariantExperiment(
{
dataset,
metric: 'toolRecall',
maxRounds: 4,
minImprovement: 0.05,
defaultLlmIterations: 10,
async proposeVariants({ round, baseline, history, bestSoFar }) {
const last = history.at(-1)?.best;
const stillFailing = last?.comparison.unchangedFailures ?? [];
const regressedAttempts = history.flatMap((r) =>
r.candidates.filter((c) => c.disqualified)
);
if (round > 0 && stillFailing.length === 0) return [];
return [nextCandidate(round, bestSoFar)];
},
},
{ mcp, testInfo }
);
What the callback receives each round:
baseline — the original no-override run.history — every prior round: each candidate's result, comparison (vs original baseline), metricValue, metricDelta, disqualified.bestSoFar — the best non-disqualified candidate yet.
Return [] to stop early. The runner also stops on maxRounds or when a round's gain is below minImprovement.
Step 4 — Read the result honestly
result.proposal.recommendation is the verdict:
apply — the winner improved the metric with zero regressions. Report delta, improvedCaseIds, and the exact toolChanges.reject — the best attempt regressed cases (regressedCaseIds says which). Report what broke; do not soft-pedal it as a partial success.inconclusive — nothing beat baseline. Say so plainly and propose a different class of hypothesis (e.g. schema descriptions instead of tool description), or recommend expanding the dataset if failures look like dataset gaps.
Also check result.reason: no-improvement after round 1+ means your hypotheses plateaued — stop iterating rather than burning budget on rephrasings of the same idea.
Step 5 — Deliver the proposal
Emit the final proposal as structured output for the user:
{
"variantId": "search-v2-trigger-phrases",
"metric": "toolRecall",
"baselineValue": 0.62,
"candidateValue": 0.91,
"delta": 0.29,
"toolChanges": { "search": { "description": "…" } },
"improvedCaseIds": ["find-policy", "lookup-wiki"],
"regressedCaseIds": [],
"recommendation": "apply"
}
Pair it with the human-readable summary: what changed, why it worked (tie back to the Step 2 hypothesis), the metric movement with iteration count (e.g. "+29% recall over 10 iterations/case"), and the exact replacement text the user can paste into their server.
Checklist
