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toolprint

What your agent's tool surface costs, and what it can do.

Point it at a laptop and get back the number of tokens your MCP tools consume on every request before you type anything, how many of them can delete, send or spend, and how many of those sit behind no authentication.

Offline. No account. Nothing is transmitted.

uvx toolprint scan                    # inventory, no network, no subprocesses
uvx toolprint scan --connect          # retrieve live tool definitions
uvx toolprint scan --connect --html report.html

Why the numbers are per-context

A conversation loads exactly one client in exactly one project. That pairing is the only unit in which "what do my tools cost per conversation" has an answer.

Scope precedence is real and mostly invisible: Claude Code resolves local over project over user, Cursor and VS Code resolve project over user, and in every case the whole entry from the winning scope is used — nothing is merged. So a machine with 19 configured entries may resolve to 11 distinct servers, of which one context loads 8. The other 11 entries are not a conservative overestimate; they describe no state the system is ever in. Entries that lose are reported as shadowed configuration, because editing one has no effect and produces no warning.

What it reports

Cost Per-tool and per-context token counts, as a share of your window
Capability Every tool classified read / write / external / irreversible
Mislabelled tools Tools whose safety annotation their own name or schema contradicts
Auth posture none, env_var, literal_secret, helper_command, oauth
Hygiene Credentials stored literally in config files, shadowed entries, unreachable servers
Versions Which servers your config pins, what each claims to be, and what is actually on disk — read from the package manager's cache, offline. The claim disagrees with the package more often than not

Findings carry stable ids (AUTH-001, COST-001, HYG-002, …) with fixed titles and standard remediation, so two reports are comparable.

Output

Everything renders from findings.json; nothing renders from raw collected data.

toolprint scan --connect --findings findings.json --html report.html
toolprint viewer report.html      # empty viewer; drop any findings.json on it
toolprint demo demo.html          # synthetic report, to see the output first

The HTML report is a single self-contained file. All CSS and JS inline, no CDN, no fonts, no analytics, and a Content-Security-Policy of default-src 'none' with connect-src 'none' — it has no capability to transmit what it displays. It opens from file:// and prints cleanly to PDF.

Drift

Once a baseline exists, check reports what changed without anyone reviewing it.

toolprint baseline --connect --by "$USER" --note "reviewed"
toolprint check --connect --fail-on high
toolprint approve --tool contact_delete --by "$USER" --note "expected in 4.2"

Thirteen rules, most severe first: effect-class escalation and revoked safety annotations are critical; a description that changed while its schema did not is high, because that is the rug-pull signature — an attacker rewriting a tool's instructions has to leave the schema alone or the tool stops working. Invisible characters and cross-server references appearing in text are high too. Breaking schema changes, new tools and safety hints that changed without being revoked are medium; additive changes, removals, vendor annotations and version bumps are low.

A revoked annotation means a guarantee was withdrawn — readOnlyHint going false, or destructiveHint going true. An annotation that merely changed is not that, and reporting it as though it were made every vendor metadata key a critical finding. Those are reported separately, with the keys named.

Baselines refuse to bless a suspicious state. Trust-on-first-use means a baseline approves whatever is in front of it, so baseline inspects the current surface for invisible characters, homoglyphs and cross-server shadowing first, and will not write a clean baseline over any of them without --force.

Exceptions require a reason and an expiry. A suppression that cannot lapse becomes permanent by neglect, and then nobody reads the report at all. Expired exceptions stop suppressing and say so in the output.

Hashes are per component — description, schema, annotations — rather than one blob, because only that split shows the description-moved-schema-didn't correlation. Canonicalisation normalises structure and never content: transfer and transfеr (Cyrillic е) hash differently on purpose, so a homoglyph substitution is caught rather than normalised away.

CI

toolprint scan --format json --fail-on high
toolprint check --format sarif --out toolprint.sarif

Exit 0 below the threshold, 1 at or above it, 2 on execution error, 3 when the baseline is missing or was written by a different heuristics version.

SARIF drops into GitHub code scanning, so findings land as annotations on the pull request. There is a ready-made action — see docs/github-action.md.

Assessment bundles

toolprint kit mcp_collect.py writes a standalone, dependency-free script under 400 lines that someone else can run on their own machine to produce a redacted bundle. It has exactly one outbound call — reachable only under --connect, and only to their own MCP servers. Redaction is an allowlist in one visible constant. See docs/collection-kit-README.md.

What it catches, and what it does not

Catches: context cost and its distribution · unauthenticated servers exposing destructive capability · mislabelled tools · credentials in config files · shadowed configuration · servers that fail to start.

Does not catch: a server that is malicious from the first scan — this detects change and misconfiguration, not initial badness · a server doing exactly what it advertises · runtime behaviour that changes without the tool definition changing · whether a description will actually manipulate a given model.

It composes with scanners that address the first case rather than replacing them.

Install

uvx toolprint scan          # no install
pipx install toolprint
pip install "toolprint[tokens]"   # optional tiktoken for exact token counts

Python 3.9+. The core has no required dependencies. Without tiktoken, token counts use a character ratio calibrated against real tool definitions (4.55 chars/token, ~4% median error), and the method used is recorded per server.

Why it reports what it reports

DESIGN.md covers the decisions most likely to surprise you: why cost is per context, why annotations give a floor and a ceiling, why canonicalisation refuses to normalise Unicode, and why the drift rules are ordered as they are.

Does drift actually happen?

dit4e/toolprint-watch checks 27 public MCP servers daily against an approved baseline and commits what changed, so the git history of that baseline is an open record of how often tool definitions move. It is a separate repository so a year of observation commits does not bury this one.

Worth knowing what it cannot see: of 18 well-known public remote MCP endpoints, two served tools/list without credentials. Remote servers are the category where a definition change is least visible to the people using them — no package update, no lockfile line — and also the category nobody outside the vendor can audit. That is an argument for running toolprint check in your own CI, where you already hold the credentials, rather than waiting for someone else to notice.

Status

Early, but complete through drift. scan, check, baselines, approvals, bundles, the HTML viewer and SARIF are built and dogfooded. Opt-in peer benchmarking is next; findings.json already carries the benchmark field it will populate.

Licence

Apache-2.0.

Metadata

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