CodeTruth MCP Server
Provides detection of usage patterns specific to Celery, such as task decorators and scheduled tasks.
Provides detection of usage patterns specific to Django, including URL routing, views, models, and management commands.
Provides detection of usage patterns specific to FastAPI, such as route decorators and dependency injection.
Provides analysis of JavaScript codebases using tree-sitter, including import resolution and framework-specific rules.
Provides detection of usage patterns specific to pytest, such as fixtures and test functions.
Provides detection of usage patterns specific to SQLAlchemy, including model classes and session usage.
Provides detection of usage patterns specific to Typer, such as CLI commands.
Provides analysis of TypeScript codebases using tree-sitter, including import resolution and framework-specific rules.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@CodeTruth MCP Servercheck if the function 'process_order' is safe to delete in ./myapp"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
CodeTruth
A verification layer that lets AI agents safely delete code in large codebases.
Agents hallucinate absence of usage. CodeTruth inverts the question — instead of "is this code used?" it asks "can we prove this code is used?" — and only surfaces a symbol for deletion when it fails to find any usage path: no call, no import, no inheritance, no string reference, no reflection target, no framework registration. Detection is deterministic; the agent only reads the evidence and decides. It is a risk assessor for code deletion, not a dead code detector.
Statuses
Status | Meaning | Recommended action |
| zero usage paths found under every analysis rule, and the name verified absent from all repo text outside its own definition |
|
| no usage found, but external exposure can't be ruled out (public API, module, test-only) |
|
| weak evidence exists (string refs, reflection, dynamic module) |
|
| strong reference or framework entry point proven |
|
Related MCP server: PrecisionContextEngine
Install
Requires Python 3.10+. The core is lightweight (networkx + PyYAML);
the MCP server and the JS plugin are opt-in extras.
pip install codetruth # CLI + Python API (dead-code gate, CI, scripts)
pip install "codetruth[mcp]" # + the agent-facing MCP server
pip install "codetruth[javascript]" # + the JS/TS plugin
pip install "codetruth[mcp,javascript]" # everything (or: codetruth[all])Not using an agent? Plain
pip install codetruthis all you need — the CLI (codetruth scan), Python API (from codetruth import scan), HTML report, and--cigate work with no extra dependencies. Themcpextra pulls a web-server stack (pydantic/starlette/uvicorn) and is only for the MCP server, so the core deliberately doesn't require it.Using it with Claude Code / an MCP agent? Install
"codetruth[mcp]", thenclaude mcp add codetruth -- codetruth mcp.If the
codetruthcommand isn't found, your Python scripts dir isn't on PATH — usepython -m codetruth.cli(andpython -m codetruth.mcp_server).
MCP (the primary interface — for agents)
pip install "codetruth[mcp]"
claude mcp add codetruth -- codetruth mcpTools exposed: check_deletion_safety(repo, symbol) (the one to call
before deleting), scan(repo, ...) (the whole review queue),
plan_deletion(repo, symbol) (advisory removal plan), and
scan_workspace(repos, ...) (cross-service usage across repos). The agent
workflow: identify symbol → call check_deletion_safety → only delete on
safe_to_delete; everything else routes to human review.
CLI
codetruth scan ./repo # review queue, strongest candidates first
codetruth scan ./repo -v --json out.json # full evidence
codetruth scan ./repo --app-mode # application (not library) repos:
# public symbols may be safe_to_delete
codetruth scan ./repo --strict # flag orphaned "useless clumps"
codetruth scan ./repo --min-rank 0.5 --group # trim the tail, group by file
codetruth scan ./repo --html report.html # self-contained HTML report
codetruth scan ./repo --ci # exit 1 if dead code exists (report gate)
codetruth scan ./repo --progress # live progress line (auto on a TTY)
codetruth baseline ./repo # accept current findings (see below)
codetruth check ./repo pkg.module:func # one symbol's evidence record
codetruth plan ./repo pkg.module:func # advisory deletion plan (never applied)Long scans show a live progress line (files scanned, then graph/rules/verify
phases) on a terminal; it's auto-silenced when output is piped (--progress /
--no-progress to override). Ctrl+C cancels cleanly.
The --ci gate is advisory like everything else: it fails the build so a
human looks at provably-dead code — it never deletes. Mark false alarms with
# codetruth: keep or a .codetruth.toml entrypoint.
Adopting on an existing codebase (baseline)
A gate that fails on all pre-existing dead code never gets switched on. Accept the current state once, commit the baseline, and the gate only fails on newly introduced dead code:
codetruth baseline ./repo # writes .codetruth.baseline.json — commit it
codetruth scan ./repo --ci # now fails ONLY on new safe_to_delete codeThe baseline keys on symbol ids, so line churn doesn't invalidate it. A
previously-hedged symbol whose deadness becomes provable (its last caller
was removed) counts as new. When accepted findings get cleaned up, the gate
tells you to refresh with codetruth baseline.
What gets scanned (scope)
CodeTruth scans the directory you point it at. It never descends into
dependency, VCS, build, or environment folders — they're pruned from the walk
(so they don't slow it down or pollute results): node_modules, .git/.hg/
.svn, .venv/venv/env/virtualenv, site-packages, __pycache__,
build/dist/.eggs/wheels, the various caches, and vendored-code dirs
(vendor, third_party, _vendor, vendored). So a virtualenv or installed
package left inside your repo won't be treated as your code.
To exclude your own folders (generated code, migrations, fixtures), add a
.codetruth.toml at the repo root:
[codetruth]
ignore_paths = ["generated/", "migrations/", "**/fixtures/**"]Ignored folders are pruned from the walk too, so excluding a large directory
also makes the scan faster. To scan just one package of a monorepo, point
codetruth scan at that package's directory.
Python API
from codetruth import scan, check_deletion_safety
result = scan("./repo")
for rec in result.candidates():
print(rec.status.value, rec.symbol, rec.evidence_against_deletion)Cross-repo / cross-service (workspace scan)
Single-repo analysis can't see that an endpoint is called over the wire or a shared package is imported by a sibling service — the exact usage that makes distributed deletion dangerous. Scan several repos as one system:
codetruth workspace ./service-api ./service-worker ./shared-libfrom codetruth import scan_repos
ws = scan_repos(["./service-api", "./service-worker"])
for xref in ws.crossrefs:
print(xref.symbol, "<-", xref.reason)It matches HTTP routes to client calls (a FastAPI/Flask route linked to a
requests/httpx call in another repo, path templates and params normalized)
and shared imports across repos. A symbol that looks dead in its own repo
but is reached cross-repo is raised from likely_dead/safe_to_delete to
uncertain_dynamic_risk with an explicit reason — the overlay only ever moves
a verdict toward keep. Also exposed as the scan_workspace MCP tool.
Runtime evidence (v1.5)
Static analysis can't see cross-service usage (HTTP calls, queues, cron in
other repos). @codetruth.track logs real invocations in production:
import codetruth
@codetruth.track
def maybe_dead(): ...Or instrument a whole package with zero source edits:
import codetruth.runtime
codetruth.runtime.instrument_package("myapp") # or CODETRUTH_AUTOTRACK=myappThen feed the trace back: codetruth scan ./repo --runtime-log runtime.jsonl.
Observed calls promote a symbol to definitely_used; "0 calls over N days"
becomes the strongest evidence tier for deletion.
Tracing is production-safe: each process writes its own runtime-<pid>.jsonl
(merged at read — no lock contention between workers), and a daemon thread
flushes counts every $CODETRUTH_FLUSH_INTERVAL seconds (default 60), so
long-running servers land evidence without a clean exit.
Finding useless clumps (strict reachability)
codetruth scan ./repo --strict asks a harder question: is this code
reachable from any real entry point (HTTP route, CLI command, __main__,
test, declared entrypoint)? Code that is internally well-connected — functions
calling each other — but never reached from an entry point surfaces as an
orphaned clump, with every member carrying a cluster field listing its
fellow members so the whole island can be reviewed (and deleted) as a group.
Dead-cluster grouping also applies in default mode whenever unreachable
symbols reference each other.
Configuration (.codetruth.toml)
Teach the scanner about usage it can't see:
[codetruth]
app_mode = true # public symbols are internal (application)
entrypoints = [ # externally-reached symbols (cron, RPC, ...)
"jobs.nightly:run",
"services.handlers.*",
]
ignore_paths = ["migrations/", "vendor/**"]Inline: a # codetruth: keep comment on (or above) a definition marks it as
an entry point.
Deletion plans (advisory)
codetruth plan ./repo pkg.mod:symbol (also the plan_deletion MCP tool, and
attached automatically to every safe_to_delete record) describes exactly what
a removal would involve: the decorator-to-end source span, imports that become
orphaned, and any __all__ entry. CodeTruth never applies a plan — it is
information for whoever decides.
Review-queue ranking
Every record carries a rank_score in [0, 1] — a deterministic ordering
heuristic (not a calibrated probability; see PLAN.md §4). Higher means weaker
evidence of use, so scan() and the CLI surface the strongest deletion
targets first. Within uncertain_dynamic_risk it separates a lone
string-literal reference from forty fuzzy attribute-name matches, so a big
review queue is triageable instead of flat.
Performance
Scans are cached at <repo>/.codetruth/index.json, keyed by a fingerprint of
every source and config file's (mtime, size). An unchanged repo returns the
cached result (≈15× faster on an 8k-symbol repo); any file change triggers a
full rescan. The cache never patches the graph incrementally — a stale
cross-file edge could mask a real usage path, so correctness always wins.
Bypass with --no-cache (CLI) or force_rescan (MCP). Add .codetruth/ to
.gitignore.
Architecture
Layer 1 Symbol Extraction codetruth/languages/python/extractor.py
Layer 2 Relationship Graph codetruth/languages/python/edges.py (strong/weak edges)
Layer 3 Semantic Rules codetruth/languages/python/rules.py + codetruth/rules/python/*.yaml
Layer 4 Evidence + Decision codetruth/core/evidence.py (4-way status)The core engine is language-agnostic (codetruth/core/, LanguagePlugin
interface).
Python is the full plugin. Framework awareness covers FastAPI/Flask/
Starlette routes, Django (signals, URLs, admin, management commands,
migrations), Celery, click/Typer, pytest, SQLAlchemy events, and — as of
0.5.0 — declarative schema models: fields of pydantic BaseModel/
BaseSettings/SQLModel, Django models/forms, DRF serializers,
TypedDict/NamedTuple, and marshmallow/msgspec are treated as framework-used
(populated, validated and serialized, not referenced like ordinary attributes),
transitively through subclasses, with the Config/Meta convention honoured.
Function-signature annotations (def f(u: User) -> Order) create usage edges,
so a model referenced only in type hints stays alive. New framework rules go
in codetruth/rules/python/*.yaml — no code changes.
JavaScript/TypeScript (pip install "codetruth[javascript]", then
scan --language javascript): tree-sitter extraction, ESM/CommonJS import
resolution, tsconfig/jsconfig paths + baseUrl aliases and monorepo
workspace packages, barrel re-export chains, Vue SFC (.vue)
scripts, package.json entry points (incl. scripts), Express/Fastify/emitter
callback handlers, React/JSX component and event-handler usage, string/config
wiring, eval poisoning, and external-base cautions — the shared evidence,
ranking, cluster, backstop, and cache layers work unchanged. Validated on real
apps (RealWorld React, preact, jupyterlab) with zero false positives; a
hand-labelled JS recall study is the remaining polish. Go is a stub.
Validation
The metric that matters is false positives — a symbol called
safe_to_delete that's actually used. Across 10 real Python packages
(requests, flask, click, jinja2, werkzeug, rich, pydantic, urllib3,
sqlalchemy, networkx — 36,457 symbols), the false-positive audit is
0 — and it still finds genuine dead code (e.g. urllib3._url_from_pool,
rich._svg_hash, requests.dict_to_sequence). Empirical calibration on
labelled data: safe_to_delete is 100% dead, monotone across tiers. JS is
validated on the RealWorld React app and preact (0 unsafe verdicts).
Reproduce with scripts/validation_report.py; details in
validation/VALIDATION.md.
Known limitations
Cross-service usage is invisible to static analysis alone — runtime tracing is the partial fix.
100% certainty is impossible;
safe_to_deletemeans "no usage path found under the defined rules," not a mathematical proof.Framework rule coverage (Layer 3) is a maintained knowledge base, never finished. New rules go in
codetruth/rules/python/*.yaml— no code changes.
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