QuassLabs API is answering right now. Last checked 6 min ago. It exposes 30 tools.
30+ deterministic dev and delivery tools: linting, metrics, diagrams, model advice, AI-text checks.
Uptime history
4 days of history
4 days agonow
100.0%
Uptime 24h
91 of 91 checks
30
Tools
read from the server
743 ms
Response time
average over 24h
open, no key
Access
streamable-http
Nothing serious here today
Today is the operative word: we check QuassLabs API every 15 minutes and re-read its code on every release. Watch it and you find out the day that stops being true.
Connect this server
Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 6 min ago.
run in your terminal
claude mcp add api --transport http https://api.quasslabs.com/mcpSettings → Connectors → Add custom connector, then paste this address
https://api.quasslabs.com/mcp~/.codex/config.toml
[mcp_servers.api]
url = "https://api.quasslabs.com/mcp".cursor/mcp.json
{
"mcpServers": {
"api": {
"url": "https://api.quasslabs.com/mcp"
}
}
}.vscode/mcp.json
{
"servers": {
"api": {
"type": "http",
"url": "https://api.quasslabs.com/mcp"
}
}
}Available tools 30
Read directly from the server with tools/list, grouped by what they act on.
If a tool disappears, we record the date.
log
log_analyzer
Analyze a log file. POST raw log text (or an upload_id from the uploads tool, up to 5MB) and get: level breakdown + time range, error clusters (messages normalized so variable ids/numbers collapse into one signature, with counts + first/last seen + a sample), a timeline, detected anomalies (error spikes, retry storms, new error signatures, log gaps/outages, level-ratio shifts) with evidence, and templated recommendations. Handles JSON-lines and plain formats (ISO8601/syslog/CLF/epoch timestamps). Deterministic, single-pass, no LLM.
log_audit_creator
Generate a concrete audit-logging spec for an app. POST your feature list (and optional compliance targets: soc2/hipaa/gdpr/pci) and get a full audit-event catalog (namespaced event names, actor/target/required fields, PII flags, retention), a canonical event envelope schema, a storage plan (append-only, access control, alerting), and per-framework compliance notes. A dev team can implement it directly. Deterministic, no LLM. Retention/compliance figures are common-practice defaults to verify with counsel.
acceptance
acceptance_criteria_validator
QA advisor: validate acceptance criteria quality. POST a list of AC (or a story) and get per-AC problems (vague/untestable, non-atomic, no observable outcome) + a missing-coverage flag if there is no failure-path AC, plus a 0-100 score and pass/needs_work verdict. Tells you exactly what to fix. Deterministic, no LLM.
alignment
alignment_gate
Run an AI system prompt (and optional agent config) through a 45-criterion alignment/safety rubric. Returns a letter grade (A-F), numeric score, per-flag remediation, and a pass/fail verdict.
blm
blm_land_lookup
Look up Bureau of Land Management land status for a US coordinate: BLM ownership, jurisdiction, allowed activities (rockhounding, camping), permit requirements, and special designations (ACEC, Wilderness, WSA, NM/NCA).
buy
buy_credits
Get a Stripe Checkout link to top up the credits on the API key you are calling with. Starter pack: $5 for 500 credits. Pro pack: $20 for 2500 credits. 1 credit = $0.01. Give the returned checkout_url to a human to complete payment in a browser; the credits are added to this key automatically once the payment succeeds, usually within seconds. Calling this tool does not charge anything and costs no credits.
commit
commit_hygiene
Deterministic commit-message hygiene checker: conventional-commit format, vague-subject detection, subject-length limit, a same-message secret-pattern scan (AWS keys, private key headers, Slack/GitHub tokens, inline password=/secret= style credentials - checked against the message text only, never a diff), and an oversized-commit warning from caller-supplied insertions/deletions. POST one commit or a batch (max 200). Deterministic, no LLM, no network.
convert
convert_diagram
Make Mermaid diagrams portable. Takes HTML containing <div class="mermaid"> blocks (or a raw Mermaid source) and returns the same document with every diagram pre-rendered to inline SVG and the Mermaid CDN runtime stripped. A CDN-backed diagram only renders if the reader can fetch and run the runtime; inline SVG needs neither, so the output survives offline readers, email clients, Notion/Confluence (which strip <script>), and PDF export. Rendering happens on our infrastructure; diagram content is not sent to any third party.
coverage
coverage_check
Scope-completeness advisor. POST a brief (or requirements[]) + your stories[] and get a coverage score + the gaps (requirements no story covers), orphan stories (possible scope creep), and missing structural buckets (auth/CRUD/error-states/tests). Tells you what the scope is missing. Deterministic, no LLM.
defect
defect_triage
Bug-triage advisor. POST one bug report (or a list) and get severity (critical/high/medium/low), category, whether it has repro steps, and what info is missing (no repro / no expected-vs-actual / no environment). For a list: priority ranking + likely-duplicate clusters. Deterministic, no LLM.
dependency
dependency_check
Dependency-manifest hygiene check. POST package manifests (package.json, requirements.txt, pyproject.toml, go.mod, Gemfile, composer.json, pubspec.yaml, lockfiles) or an upload_id and get findings with severity + concrete fixes: wildcard/unbounded versions, missing lockfiles, unpinned installs, non-https indexes, install-script risks, dependency-confusion signals, and matches against a static snapshot of famously compromised packages. NOT a live CVE feed - pair with npm audit / pip-audit / osv-scanner for that. Deterministic, no network, no LLM.
describe
describe_diagram
Turn a plain-English description into Mermaid diagram source. Describe the flow, system, schema or timeline you want ("a login flow with an OAuth branch and a failure path") and get back complete, valid Mermaid - no need to know the syntax. The mermaid field of the response is directly usable as the mermaid_source input of convert_diagram, so describe -> render is a two-call pipeline. Generation runs on our infrastructure via AWS Bedrock; your description is not sent to any third-party service beyond that.
description
pr_description_lint
Deterministic PR description quality linter: flags a missing/too-short body, absent test evidence, absent stated rationale, an unstructured long body, and - only for PRs that look like bugfixes (title prefix fix/bug/hotfix/patch, or an explicit is_bugfix flag) - missing repro steps. Optionally requires a linked issue/ticket reference. Single PR only, no batch. Deterministic, no LLM.
diagram
diagram_contrast
Diagram quality QA. Catch and fix the things that ruin diagrams: (1) unreadable label contrast - POST a mermaid/SVG or HTML diagram, get every WCAG-AA failure (incl. mermaid <style>-block label colors, with unresolvable fills flagged honestly, not false-passed) + auto-corrected colors; (2) harden a mermaid SOURCE before it renders - rewrites every fill/label pair and remaps fills no label color can rescue, which scanning cannot do because a per-node style line overrides the theme block; (3) HTML-looks-fine-but-prints-broken - checks for the print-color-adjust/page-break gotchas and returns the fix CSS; (4) arrows crossing objects - returns the ELK-renderer config + layout rules that minimize crossings. Pure deterministic - no AI, no rate limit.
estimate
estimate_sanity
Deterministic classifier for story/task hour estimates against benchmarks: flags each estimate in_range, borderline, over, under, or unbenchmarked. Supply either an explicit {min, max} range per type (benchmarks) or a list of historical actual hours per type (historical) that this endpoint derives a mean/stdev variance band from - explicit ranges win when both are given for the same type. No benchmark numbers are built in; a type with neither input is reported unbenchmarked, never guessed at. POST one estimate or a batch (max 200). Deterministic, no LLM.
feedback
feedback
Submit free-text feedback about the API - a bug, a feature request, a docs gap, a billing question, or anything else. Tied to your key so we can follow up, but not billed and not a support-ticket SLA. Auth required; not billed.
humanize
humanize
Rewrite text to remove AI-writing tells. POST prose and get it back with the mechanical tells fixed (AI vocabulary swapped for plain words, filler phrases stripped, em-dash overuse reduced, significance inflation deflated) plus a change log, before/after ai-ness scores, and a residual list of tells that need human judgment (not auto-rewritten). Code fences, inline code, URLs, and quotes are never touched. Deterministic, no LLM. Sister to humanizer_score (the detector).
humanizer
humanizer_score
Detect AI-writing tells in text. POST prose and get a 0-1 ai_ness score + band (low/medium/high) + the EXACT patterns that fired (AI vocabulary, significance inflation, promotional language, filler phrases, negative parallelism, rule-of-three, em-dash overuse) with sample spans. Explainable, not an opaque classifier - every point is a named, located pattern. Deterministic, no LLM. The rewrite is a separate step.
model
model_advisor
LLM selection + cost advisor. POST a task description (+ optional volume/tokens/constraints like local-only or cheap) and get a recommended model + fallback chain + do-not-use (with reasons), the complexity tier, and per-run + monthly cost. A maintained model+price catalog. Deterministic, no LLM. Tells the AI/user which model to use and what it costs.
rate
rate_call
Rate a specific prior metered call - thumbs up or down, plus an optional comment - keyed by the request_id that call's response reported. Scoped for now to rating compress calls, so you can tell us when a compression was too aggressive or dropped something it shouldn't have. Resubmitting the same request_id overwrites your previous rating rather than adding a second one. Auth required; not billed - rating a call you already paid for should not cost more.
readability
readability_grade
Deterministic Flesch Reading Ease and Flesch-Kincaid Grade Level for a block of text, plus word/sentence/syllable counts and a plain-language reading-ease band (very_easy through very_confusing). Syllable counting is the standard vowel-group heuristic (same approximation used by textstat and most open readability tools), not a dictionary lookup. Optionally pass target_grade to get an on_target flag (within +/-2.0 grade levels). Different metric family from humanizer_score - this is pure readability statistics, not AI-writing-tell detection. Deterministic, no LLM.
regex
regex_lint
Static safety analysis of a regular expression plus an English explanation of what it matches. Parses the pattern into an AST and flags the structures that cause catastrophic backtracking (ReDoS): nested unbounded quantifiers like (a+)+, repeated alternations whose branches can start with the same character like (a|ab)*, adjacent unbounded quantifiers over overlapping character sets like \d+\d+, and implausibly large bounded repeats. The pattern is COMPILED but never EXECUTED against input - running a caller-supplied regex is itself the denial-of-service vector. Detection is a structural heuristic, so a risk of "none" means no known dangerous shape was found, not a proof of safety. Deterministic, no LLM.
saas
saas_metrics
SaaS health advisor. POST your MRR/customers (+ optional churn/CAC/margin/growth) and get ARR/ARPA/LTV/CAC/payback/Rule-of-40/NRR/GRR computed, each with a health tag, plus an overall grade + a ranked "what to fix first" focus list. Deterministic math + advisory, no LLM.
sprint
sprint_health
Sprint/delivery health advisor. POST 1-24 sprints (committed/completed points, optional per-ticket estimate-vs-actual data) and get computed metrics each with a good/warn/bad health tag: velocity avg + trend + stability, commitment reliability, spillover rate, mid-sprint scope change, estimate accuracy (median + chronic-overrun pct), and WIP concentration per assignee. Plus an overall grade and a ranked what-to-fix-first focus list. Pure math + advisory, no LLM.
story
story_lint
Deterministic structural quality linter for user stories - a sibling to ticket_lint but scoped to story-writing concepts any team recognizes: narrative completeness (As-a/I-want-to/So-that/Summary), acceptance criteria presence, duplicate/unknown dependency IDs, a "Manage X" story with no CRUD split, list stories missing a pagination mention. An OPTIONAL rate_config unlocks team-specific checks (screen-vs-narrative match, hour-rate-per-screen tolerance, required risk level by story type, milestone-code format, required companion story types per milestone) - omit it and only the universal checks run. No house rates or milestone conventions are hardcoded. POST one story or a batch (max 50). Deterministic, no LLM.
supply
supply_chain_scan
Scan package manifests, lockfiles, and MCP server configs for known supply-chain compromises. The only scanner treating MCP configuration as a security surface. Returns findings with severity and remediation.
ticket
ticket_lint
Full Jira-ticket quality linter (whole ticket, not just AC - use acceptance_criteria_validator for the deep AC pass). POST one ticket or a batch (max 50) and get per-ticket findings with severity + concrete fixes: missing/weak summary or description, missing AC or estimate, bugs without repro/expected-vs-actual/environment, missing epic link, points-vs-hours mismatch, oversized estimates to split, urgent-but-thin tickets. Returns score, grade, and a ready/needs_work verdict per ticket plus batch summary. Type-aware rules (story/bug/task/epic/subtask), strict profile available. Deterministic, no LLM.
tool
tool_advisor
Which QL-API endpoint fits this task. POST a task description and get the ranked endpoint(s) that best match, each with a confidence label (high/medium/low). Deterministic keyword matching over this platform's own endpoint catalog, no LLM. Pairs with model_advisor (which itself is one of the recommendable endpoints, so a "which model" task surfaces it) - call them independently and chain the results yourself; optionally pass a prior model_advisor result's rationale in as `context` to bias matching.
uploads
uploads
Get a presigned S3 PUT URL to upload a file (policy doc, config, log, up to 5MB) that a file-ingesting endpoint then reads by upload_id. Two-step: call this, PUT your file to the returned url, then pass upload_id to the consuming endpoint. Uploads auto-expire in 1 day and are readable only by your key. Auth required; not billed.
worklog
worklog_guardrail
Worklog/timesheet policy checker - run a batch of worklog entries through configurable guardrails BEFORE posting them to a time-tracking system. Checks: rounding increment (default 0.25h, float-safe), per-entry min/max, per-author daily totals, duplicates, over-estimate alerts (never suggests inflating hours or estimates - flags to PM instead), future dates, weekends, immutable-author edit protection, missing description/ticket. Returns pass/warnings/blocked verdict + per-entry violations with fixes + hour totals by author and date. Caller supplies the reference date - fully deterministic, no LLM.
Endpoints
| URL | Transport | State | Latency | Checked |
|---|---|---|---|---|
| https://api.quasslabs.com/mcp | streamable-http | answering | 966 ms | 6 min ago |
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QuassLabs API — questions
Answers built from our own checks of this server.
What can QuassLabs API do?
It exposes 30 tools, read directly from the server on our last check. Among them: acceptance_criteria_validator, alignment_gate, blm_land_lookup, buy_credits, commit_hygiene, convert_diagram and 24 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README.
How MCP servers expose tools in the first place →
Is QuassLabs API working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 91 of 91 checks got a reply (100.0%), average response time 743 ms. The bar chart above shows every period we have measured.
How do I connect QuassLabs API?
Copy the ready config from this page. We generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each in the form that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does QuassLabs API need an API key?
No. QuassLabs API completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 30 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is QuassLabs API?
It answers our handshake in 743 ms on average, which is faster than 12% of all working MCP servers we measure. That is on the slow side — worth knowing if the tool sits inside an interactive loop. The comparison comes from our own checks across the whole registry, every 15 minutes.