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/openalex-mcp-server --skill api-canvasgit clone --depth 1 https://github.com/cyanheads/openalex-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/openalex-mcp-server/api-canvas)<a href="https://agentmods.dev/skills/cyanheads/openalex-mcp-server/api-canvas"><img src="https://agentmods.dev/badge/skills/cyanheads/openalex-mcp-server/api-canvas/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/openalex-mcp-server/api-canvas"><img src="https://agentmods.dev/badge/skills/cyanheads/openalex-mcp-server/api-canvas.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 | $0.00085 | $0.10180 |
| Opus 5.5 | $0.00034 | $0.04072 |
| Sonnet 5.5 | $0.00017 | $0.02036 |
| Haiku 4.5 | $0.00009 | $0.01018 |
Grade A, and why
api-canvas 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 4d 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 api-canvas — 27 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 — 593 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
DataCanvas is a primitive for storage stashes, canvas computes. The existing IStorageProvider is a key/value abstraction — it can stash blobs but exposes no analytical surface. DataCanvas is the analytical surface: register tabular data from upstream APIs, run SQL across multiple registered tables, and export results as CSV/Parquet/JSON.
Tier 3 — @duckdb/node-api is an optional peer dependency (bun add @duckdb/node-api). Servers that don't enable canvas pay zero install cost. Lazy-loaded on first use.
Disabled by default. Set CANVAS_PROVIDER_TYPE=duckdb to enable. Otherwise core.canvas is undefined.
Cloudflare Workers: unsupported. DuckDB has no V8-isolate build. Setting CANVAS_PROVIDER_TYPE=duckdb on a Worker fails closed with a ConfigurationError at init time.
When canvas earns its keep
Two gates before wiring canvas in — both must be yes. Canvas that fails either is a SQL surface nobody queries.
- Is the data analytical, not just large? Canvas is for tabular/numeric result sets an agent runs SQL over — aggregate, group, join, time-series filter. A discovery/search surface returning categorical metadata (titles, IDs, types, dates) where the workflow is find the record, then drill into it does not qualify, regardless of row count. A 5,000-row search result is still discovery. The gate is shape, not size: the right question is "would an agent write
SELECT … GROUP BYagainst this?", not "does it have many rows?" For name→ID resolution over a bounded list, reach for MCP-side list filtering (see thedesign-mcp-serverskill) instead. - Is it too big to inline? A result that fits the response (≤ ~100 rows of compact data) just gets inlined — no canvas. Canvas is the third option only when shape and size both call for it.
If canvas earns its keep, it carries an obligation: a tool that emits a canvas_id MUST ship a dataframe_query tool in the same server's surface (see the simple-shape Tools row and the Checklist). A canvas_id with no query tool is dead output — the agent literally cannot reach the staged data.
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.
- 4d ago Changed · +7 lines ccd12b0799e3
- 22d ago Changed · +9 lines 9332c0d732ef
- 1mo ago Changed · +21 lines b7ea375ffd05
- 1mo ago First seen · 556 lines · 85 tokens per session scan A a15da8c76763
api-canvas is a skill published in the GitHub repository cyanheads/openalex-mcp-server (16 stars, last pushed 5d ago), licensed Apache-2.0. It adds 85 tokens to every session and 10,180 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to api-canvas, differing in 27 lines, and is treated as a copy.
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