Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add gleanwork/mcp-server-tester --skill optimize-mcp-tool-metadatagit clone --depth 1 https://github.com/gleanwork/mcp-server-testerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gleanwork/mcp-server-tester/optimize-mcp-tool-metadata)<a href="https://agentmods.dev/skills/gleanwork/mcp-server-tester/optimize-mcp-tool-metadata"><img src="https://agentmods.dev/badge/skills/gleanwork/mcp-server-tester/optimize-mcp-tool-metadata.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00077 | $0.01954 |
| Opus 5 | $0.00039 | $0.00977 |
| Sonnet 5 | $0.00015 | $0.00391 |
| Haiku 4.5 | $0.00008 | $0.00195 |
Grade A, and why
optimize-mcp-tool-metadata scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 insideinputSchema— property descriptions, enum documentation, examples, format hints. - Never vary: parameter names, types,
requiredarrays, 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
allowRegressionsat 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_hosteval dataset withtoolsTriggeredexpectations (use thewrite-mcp-host-evalskill to create one). npm install ai @ai-sdk/<provider>and the matching API key env var.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 171 lines · 77 tokens per session scan A c91076780374
optimize-mcp-tool-metadata is a skill published in the GitHub repository gleanwork/mcp-server-tester (19 stars, last pushed today), licensed MIT. It adds 77 tokens to every session and 1,954 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…