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 cyanheads/calculator-mcp-server --skill tool-defs-analysisgit clone --depth 1 https://github.com/cyanheads/calculator-mcp-serverWrote 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/cyanheads/calculator-mcp-server/tool-defs-analysis)<a href="https://agentmods.dev/skills/cyanheads/calculator-mcp-server/tool-defs-analysis"><img src="https://agentmods.dev/badge/skills/cyanheads/calculator-mcp-server/tool-defs-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cyanheads/calculator-mcp-server/tool-defs-analysis"><img src="https://agentmods.dev/badge/skills/cyanheads/calculator-mcp-server/tool-defs-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00177 | $0.04277 |
| Opus 5 | $0.00088 | $0.02139 |
| Sonnet 5 | $0.00035 | $0.00855 |
| Haiku 4.5 | $0.00018 | $0.00428 |
Grade A, and why
tool-defs-analysis 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 10d 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.
This is a copy
100% identical to tool-defs-analysis — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 280 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
Every string in a tool/resource/prompt definition is part of an LLM-facing API contract. The model reads the description, every parameter .describe(), the output schema, the recovery hints — and decides what to call and how. Definition language drifts: an internal mapping leaks into a parameter doc during a fix, a self-referential output description survives a refactor, a default that suited the developer at scaffold time stays after the typical call shape changes.
This skill is the review-time pass for that drift. Read each definition the way a mid-tier model with no project context would — can it pick the tool, fill the fields, and recover from errors using only the rendered schema?
| Skill | Lens |
|---|---|
design-mcp-server |
Authoring rules at write-time |
field-test |
Behavior testing + a narrow 3-category leak audit |
security-pass |
Injection, scopes, input sinks |
tool-defs-analysis (this) |
LLM-facing language across the existing surface |
field-test already audits descriptions for implementation leaks, meta-coaching, and consumer-aware phrasing during its catalog step — that's a fast shallow pass alongside live tool calls. This skill is the deeper review: 16 categories, every field, every recovery hint, every default value, with file:line citations — plus a cross-surface pass for the drift no single file shows.
Read-only. This skill produces a report; the maintainer applies fixes. While running it, do not run git, do not stage or commit, do not update the changelog, do not run devcheck, do not invoke wrapup or release workflows. Fixes flow through the normal authoring path (edit the definition, then re-run this skill if you want to verify).
When to Use
- After a polish session or refactor that touched definitions
- Before a release, alongside
polish-docs-metaandsecurity-pass - When the user says "review my tool definitions", "audit descriptions", "are my tool descriptions any good"
- After scaffolding a new server but before it ships
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.
- 10d ago First seen · 280 lines · 177 tokens per session scan A 1d1f27b114eb
tool-defs-analysis is a skill published in the GitHub repository cyanheads/calculator-mcp-server (1 stars, last pushed 18d ago), licensed Apache-2.0. It adds 177 tokens to every session and 4,277 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to tool-defs-analysis, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
adeu-redlining
Use this skill when reviewing, editing, redlining, or negotiating an existing Microsoft Word document (.docx) — including proposing edits as tracked changes, accepting or rejecting existing tracked changes, replying to comments, comparing two versions, sanitizing author metadata, or finalizing a contract for…
wiki
Rebuild a flow-first project wiki using the mimirs wiki MCP tool. Use when the user asks to generate, rebuild, refresh, or write the wiki for a codebase.
scout
Research the web for solutions, competitors, alternatives, or prior art for a decision facing this project — then store the findings in project memory so they survive the session. Use when choosing a library or approach, comparing tools, checking what others do, or evaluating whether to build vs adopt.
plan
Design an implementation plan before writing code — where the change lands, what it will touch, what could break, and the steps in order. Use when asked to plan a feature, scope a change, or figure out how to approach an edit before making it. To assess a change that already exists (a diff, refactor, or rename), use…
research
Answer a hard, open-ended question about how the project works or is built by synthesizing every source — code, structure, git history, prior decisions, discussion, caveats — and verifying each claim against the source. Use for deep cross-cutting questions that span more than one area. Narrower siblings — a single…
explore
Build an accurate mental model of an unfamiliar codebase, feature, or area before changing it — where it lives, how it connects, what it does, and why. Use when asked how something works, where something is, when onboarding to a repo, or before editing code you don't know. For a cross-cutting question that needs…