QuassLabs API MCP
Provides 20 deterministic developer and delivery tools over MCP, including AI-writing detection, text humanization, model selection advice, dependency checks, log analysis, Jira ticket linting, diagram generation, and SaaS metrics. These tools perform pure computation without LLMs in the request path, ensuring consistent outputs and low costs.
Endpoint and access
No sign-in to connect and list tools. Individual tools may still need a vendor account, credentials, or permissions; check the documented prerequisites.
https://api.quasslabs.com/mcpWhat you can do with it
- Detect AI-writing tells in text
- Rewrite text to remove AI-writing tells
- Analyze log files for debugging
- Validate Jira ticket quality
Source and maintenance
Protocol and capabilities
- Server
- quasslabs-api 0.1.0
- MCP protocol
- 2025-06-18
- Tools
- 30 advertised
- Resources
- Not advertised
- Resource templates
- Not advertised
- Prompts
- Not advertised
{
"tools": {}
}Tools
30 advertised
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.
{
"type": "object",
"required": [
"system_prompt"
],
"properties": {
"model_id": {
"type": "string",
"description": "Optional target model identifier."
},
"agent_config": {
"type": "object",
"description": "Optional agent config (tools, memory, temperature, etc.)."
},
"system_prompt": {
"type": "string",
"description": "The system prompt text to evaluate."
}
}
}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).
{
"type": "object",
"required": [
"lat",
"lon"
],
"properties": {
"lat": {
"type": "number",
"description": "Latitude, -90 to 90."
},
"lon": {
"type": "number",
"description": "Longitude, -180 to 180."
},
"radius_km": {
"type": "number",
"description": "Optional buffer radius in km (0-50, default 1.0)."
}
}
}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.
{
"type": "object",
"properties": {
"bg": {
"type": "string",
"description": "contrast/readable: background/fill hex (#rrggbb)."
},
"fg": {
"type": "string",
"description": "contrast: foreground hex (#rrggbb)."
},
"mode": {
"enum": [
"scan",
"harden",
"contrast",
"readable",
"palette",
"print",
"layout"
],
"type": "string",
"description": "Default \"scan\". scan=check rendered output; harden=fix a mermaid source before rendering; print=print-safety check; layout=arrow-crossing reduction."
},
"brand": {
"type": "string",
"description": "brand palette to use (e.g. appstango). Default from config."
},
"large": {
"type": "boolean",
"description": "contrast/harden: use the 3:1 large-text threshold."
},
"format": {
"enum": [
"svg",
"html"
],
"type": "string",
"description": "scan: content format (default svg)."
},
"source": {
"type": "string",
"description": "harden: the mermaid SOURCE text (not rendered SVG)."
},
"autofix": {
"type": "boolean",
"description": "scan: include suggested corrected colors."
},
"content": {
"type": "string",
"description": "scan/print: the SVG or HTML diagram/document source."
},
"add_init": {
"type": "boolean",
"description": "harden: prepend the %%{init}%% theme block (default true)."
},
"renderer": {
"type": "string",
"description": "layout: mermaid renderer (default \"elk\")."
}
}
}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.
{
"type": "object",
"required": [
"text"
],
"properties": {
"text": {
"type": "string",
"description": "The prose to score (up to 100k chars)."
}
}
}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.
{
"type": "object",
"required": [
"mrr",
"customers"
],
"properties": {
"cac": {
"type": "number"
},
"mrr": {
"type": "number"
},
"new_mrr": {
"type": "number"
},
"customers": {
"type": "number"
},
"prior_mrr": {
"type": "number"
},
"churned_mrr": {
"type": "number"
},
"expansion_mrr": {
"type": "number"
},
"new_customers": {
"type": "number"
},
"gross_margin_pct": {
"type": "number"
},
"sales_marketing_spend": {
"type": "number"
}
}
}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.
{
"type": "object",
"properties": {
"story": {
"type": "string"
},
"strictness": {
"enum": [
"normal",
"strict"
],
"type": "string"
},
"acceptance_criteria": {
"type": "array",
"items": {
"type": "string"
}
}
}
}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.
{
"type": "object",
"required": [
"stories"
],
"properties": {
"brief": {
"type": "string"
},
"stories": {
"type": "array",
"items": {
"type": "string"
}
},
"requirements": {
"type": "array",
"items": {
"type": "string"
}
}
}
}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.
{
"type": "object",
"properties": {
"report": {
"type": "string"
},
"reports": {
"type": "array",
"items": {
"type": "string"
}
}
}
}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.
{
"type": "object",
"required": [
"task"
],
"properties": {
"task": {
"type": "string"
},
"top_n": {
"type": "integer",
"default": 1,
"maximum": 10,
"minimum": 1,
"description": "How many ranked models to return as primary+fallbacks (rest land in also_considered). Default 1 = primary alone."
},
"models": {
"type": "array",
"items": {
"type": "string",
"minLength": 1
},
"maxItems": 50,
"minItems": 1,
"description": "Optional allowlist - restrict ranking to only these model identifiers (e.g. models you already have access to). Any catalog model not in this list is excluded, listed in do_not_use."
},
"tokens_in": {
"type": "number"
},
"tokens_out": {
"type": "number"
},
"constraints": {
"type": "array",
"items": {
"type": "string"
}
},
"volume_per_month": {
"type": "number"
}
}
}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.
{
"type": "object",
"required": [
"task"
],
"properties": {
"task": {
"type": "string",
"description": "What you are trying to do."
},
"tools": {
"type": "array",
"items": {
"type": "string",
"minLength": 1
},
"maxItems": 20,
"minItems": 1,
"description": "Optional allowlist - restrict matching to only these endpoint names."
},
"top_n": {
"type": "integer",
"default": 1,
"maximum": 20,
"minimum": 1,
"description": "How many ranked endpoints to return. Default 1 = best match only."
},
"context": {
"type": "string",
"description": "Optional extra text to fold into matching - e.g. a prior model_advisor rationale, or more detail than fits in `task`."
}
}
}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).
{
"type": "object",
"required": [
"text"
],
"properties": {
"text": {
"type": "string",
"description": "The prose to rewrite (up to 100k chars)."
}
}
}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.
{
"type": "object",
"properties": {
"filename": {
"type": "string",
"description": "Required with upload_id so the manifest type can be detected."
},
"manifests": {
"type": "array",
"items": {
"type": "object",
"required": [
"filename",
"content"
],
"properties": {
"content": {
"type": "string"
},
"filename": {
"type": "string"
}
}
},
"description": "Up to 20 manifest files inline."
},
"upload_id": {
"type": "string",
"description": "Alternative: a single manifest uploaded via the uploads tool."
}
}
}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.
{
"type": "object",
"properties": {
"stack": {
"type": "string"
},
"features": {
"type": "array",
"items": {
"type": "string"
},
"description": "App features, e.g. [\"user login\", \"payments\", \"file upload\"]."
},
"compliance": {
"type": "array",
"items": {
"enum": [
"soc2",
"hipaa",
"gdpr",
"pci"
],
"type": "string"
}
},
"description": {
"type": "string",
"description": "Optional free-text app description (scanned for features)."
}
}
}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.
{
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "Raw log content (up to 1M chars)."
},
"top_n": {
"type": "number",
"description": "Top clusters to return (1-50, default 10)."
},
"format": {
"enum": [
"auto",
"json",
"plain"
],
"type": "string"
},
"upload_id": {
"type": "string",
"description": "Alternative: a log file uploaded via the uploads tool."
}
}
}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.
{
"type": "object",
"properties": {
"ticket": {
"type": "object",
"description": "One ticket: {key, summary, description, acceptance_criteria, issue_type, estimate_hours|story_points, priority, labels, components, epic_link, ...}. Missing fields ARE findings."
},
"profile": {
"enum": [
"normal",
"strict"
],
"type": "string"
},
"tickets": {
"type": "array",
"items": {
"type": "object"
},
"description": "Alternative: batch of up to 50 tickets."
}
}
}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.
{
"type": "object",
"properties": {
"story": {
"type": "object",
"description": "One story: {id, title, story: {as_a, i_want_to, so_that, summary, type_label?, on_screen?}, acceptance_criteria, dependencies?: [id], screen?, hours?: {<field>: number}, risk?, milestone_code?}. Missing fields ARE findings."
},
"stories": {
"type": "array",
"items": {
"type": "object"
},
"description": "Alternative: batch of up to 50 stories. Dependency/duplicate/required-companion checks are evaluated across the whole batch."
},
"rate_config": {
"type": "object",
"properties": {
"rates": {
"type": "object",
"description": "Per type_label: {hours_field, hours_per_screen, tolerance?}. Flags hours[hours_field] that deviate from screens * hours_per_screen by more than tolerance (default 0.15)."
},
"risk_levels": {
"type": "array",
"items": {
"type": "string"
},
"description": "Valid risk values. Default high/medium/low."
},
"check_screen_match": {
"type": "boolean",
"description": "Flag when story.on_screen disagrees with story.screen."
},
"risk_required_types": {
"type": "array",
"items": {
"type": "string"
},
"description": "type_labels that must carry a valid risk level."
},
"milestone_code_pattern": {
"type": "string",
"description": "Regex the milestone_code field must match."
},
"required_companion_types": {
"type": "array",
"items": {
"type": "object",
"properties": {
"type": {
"type": "string"
},
"label": {
"type": "string"
}
}
},
"description": "Batch-level only: for each milestone group (milestone_code prefix before the first \"-\"), flag if no story has this type_label."
}
},
"description": "Optional. Enables team-specific checks, all off by default."
}
}
}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.
{
"type": "object",
"properties": {
"flag_z": {
"type": "number",
"maximum": 10,
"description": "Default 2.0. Stdevs from the historical mean beyond which a result is \"over\"/\"under\" instead of \"borderline\". Must be >= borderline_z.",
"exclusiveMinimum": 0
},
"estimate": {
"type": "object",
"description": "One item: {id?, type, hours}. type and hours are required."
},
"estimates": {
"type": "array",
"items": {
"type": "object"
},
"description": "Alternative: batch of up to 200 items."
},
"benchmarks": {
"type": "object",
"description": "Optional. Per type: {min?, max?}. Wins over historical for the same type.",
"additionalProperties": {
"type": "object",
"properties": {
"max": {
"type": "number"
},
"min": {
"type": "number"
}
}
}
},
"historical": {
"type": "object",
"description": "Optional. Per type: array of past actual hours (max 500 each), used to derive mean/stdev when no explicit benchmark exists for that type. Needs at least 2 values to produce a band.",
"additionalProperties": {
"type": "array",
"items": {
"type": "number"
}
}
},
"borderline_z": {
"type": "number",
"maximum": 10,
"description": "Default 1.0. Stdevs from the historical mean before a result is \"borderline\" instead of \"in_range\".",
"exclusiveMinimum": 0
}
}
}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.
{
"type": "object",
"required": [
"pr"
],
"properties": {
"pr": {
"type": "object",
"properties": {
"body": {
"type": "string"
},
"title": {
"type": "string"
},
"is_bugfix": {
"type": "boolean"
}
},
"description": "{title?, body?, is_bugfix?}. is_bugfix overrides title-based inference when supplied."
},
"require_issue_link": {
"type": "boolean",
"description": "Default false. When true, flags a missing issue/ticket reference in the title or body."
}
}
}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.
{
"type": "object",
"properties": {
"commit": {
"type": "object",
"description": "One commit: {sha?, message, insertions?, deletions?}. message is required."
},
"commits": {
"type": "array",
"items": {
"type": "object"
},
"description": "Alternative: batch of up to 200 commits."
},
"max_lines_changed": {
"type": "integer",
"description": "Default 500. Total insertions+deletions above this triggers a large_commit finding.",
"exclusiveMinimum": 0
}
}
}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.
{
"type": "object",
"required": [
"text"
],
"properties": {
"text": {
"type": "string",
"description": "Prose to analyze. Max 100,000 characters."
},
"target_grade": {
"type": "number",
"maximum": 20,
"minimum": 0,
"description": "Optional target Flesch-Kincaid grade level, 0-20."
}
}
}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.
{
"type": "object",
"required": [
"pattern"
],
"properties": {
"flags": {
"type": "string",
"description": "Optional inline flags, any of \"imsxau\" (ignorecase, multiline, dotall, verbose, ascii, unicode)."
},
"pattern": {
"type": "string",
"description": "The regular expression source, without delimiters. Max 2,000 characters."
}
}
}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.
{
"type": "object",
"required": [
"sprints"
],
"properties": {
"sprints": {
"type": "array",
"items": {
"type": "object"
},
"description": "Oldest first: [{name, committed_points, completed_points, added_points?, spillover_points?, tickets?: [{key, estimate_hours, actual_hours, status, assignee}]}]."
},
"team_size": {
"type": "number"
},
"sprint_length_days": {
"type": "number",
"description": "Default 10."
}
}
}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.
{
"type": "object",
"required": [
"entries",
"today"
],
"properties": {
"today": {
"type": "string",
"description": "Reference date \"YYYY-MM-DD\" for future-date checks."
},
"policy": {
"type": "object",
"description": "Optional overrides: rounding_increment, max_hours_per_day, max_hours_per_entry, min_hours_per_entry, immutable_authors, require_description, require_ticket, over_estimate_policy (alert|block|ignore), future_dates (block|alert), weekend_policy (allow|alert)."
},
"entries": {
"type": "array",
"items": {
"type": "object"
},
"description": "Up to 500: [{author, date \"YYYY-MM-DD\", hours, ticket?, description?, estimate_hours?, already_logged_hours?, id?, edit?, delete?, new?}]."
}
}
}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.
{
"type": "object",
"properties": {
"content_type": {
"type": "string",
"description": "MIME type of the file you will PUT (e.g. text/plain, application/json). Default application/octet-stream."
}
}
}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.
{
"type": "object",
"required": [
"message"
],
"properties": {
"message": {
"type": "string",
"description": "What you want us to know. Up to 4000 characters."
},
"category": {
"enum": [
"bug",
"feature_request",
"docs",
"billing",
"other"
],
"type": "string",
"description": "Defaults to \"other\"."
},
"endpoint": {
"type": "string",
"description": "Optional. Which /v1/* endpoint or MCP tool this is about, e.g. \"/v1/ticket-lint\"."
},
"severity": {
"enum": [
"low",
"medium",
"high"
],
"type": "string",
"description": "Optional. Omit if not applicable."
},
"contact_ok": {
"type": "boolean",
"description": "Optional, defaults to false. Whether we may follow up with you about this."
}
}
}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.
{
"type": "object",
"properties": {
"pack": {
"enum": [
"starter",
"pro"
],
"type": "string",
"description": "Which pack to buy. Defaults to \"starter\" ($5 / 500 credits). \"pro\" is $20 / 2500 credits."
},
"packs": {
"type": "integer",
"maximum": 100,
"minimum": 1,
"description": "How many of that pack. Defaults to 1."
}
}
}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.
{
"type": "object",
"required": [
"request_id",
"rating"
],
"properties": {
"rating": {
"enum": [
"up",
"down"
],
"type": "string"
},
"comment": {
"type": "string",
"maxLength": 1000,
"description": "Optional. What was good or wrong about that call's result."
},
"request_id": {
"type": "string",
"maxLength": 100,
"description": "The request_id from the response of the call you are rating."
}
}
}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.
{
"type": "object",
"properties": {
"ecosystems": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional ecosystem filter."
},
"archive_url": {
"type": "string",
"description": "Presigned HTTPS URL to a ZIP (for large archives)."
},
"archive_base64": {
"type": "string",
"description": "Base64-encoded ZIP of manifests/lockfiles/MCP configs."
}
}
}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.
{
"type": "object",
"required": [
"description"
],
"properties": {
"description": {
"type": "string",
"maxLength": 8000,
"description": "What the diagram should show, in plain language. Include the nodes/steps/entities you care about; the generator fills in sensible structure."
},
"diagram_type": {
"enum": [
"auto",
"flowchart",
"sequence",
"class",
"state",
"er",
"gantt",
"pie",
"mindmap",
"timeline"
],
"type": "string",
"description": "Force a specific Mermaid diagram type. Default \"auto\" - the generator picks the type that fits the description."
}
}
}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.
{
"type": "object",
"properties": {
"html": {
"type": "string",
"description": "HTML document or fragment containing <div class=\"mermaid\"> blocks. Mutually exclusive with mermaid_source."
},
"theme": {
"enum": [
"default",
"dark",
"forest",
"neutral",
"base"
],
"type": "string",
"description": "Mermaid theme. Default \"default\"."
},
"background": {
"type": "string",
"description": "\"transparent\" (default), \"white\", or a hex color."
},
"output_format": {
"enum": [
"html",
"svg"
],
"type": "string",
"description": "Default \"html\". \"svg\" returns the raw inline SVG and requires mermaid_source."
},
"mermaid_source": {
"type": "string",
"description": "A single raw Mermaid diagram source. Mutually exclusive with html."
},
"inject_lightbox": {
"type": "boolean",
"description": "Add click-to-enlarge overlay CSS+JS for small-text diagrams. Default false."
}
}
}Prompts
Not advertised
Resources
Not advertised
30-day availability
Aggregated from read-only remote checks; this is not a tool-execution or security guarantee.
- Successful checks
- 1 of 1 (100%)
- Average latency
- 740 ms
- 95th percentile latency
- 740 ms
Maintenance history
Public check and source-refresh events. Editorial and ownership records remain private.
- · probe · passed · 740 ms · metadata changed · 30 tool(s)
Connect from your client
Each snippet registers the same endpoint; pick the client you use.
Claude Code
claude mcp add --transport http quasslabs-api-mcp https://api.quasslabs.com/mcpCodex
codex mcp add quasslabs-api-mcp --url https://api.quasslabs.com/mcpCursor
{
"mcpServers": {
"quasslabs-api-mcp": {
"url": "https://api.quasslabs.com/mcp"
}
}
}Cline
{
"mcpServers": {
"quasslabs-api-mcp": {
"type": "streamableHttp",
"url": "https://api.quasslabs.com/mcp",
"disabled": false
}
}
}VS Code
{
"servers": {
"quasslabs-api-mcp": {
"type": "http",
"url": "https://api.quasslabs.com/mcp"
}
}
}Gemini CLI
gemini mcp add --transport http quasslabs-api-mcp https://api.quasslabs.com/mcpClaude Desktop
{
"mcpServers": {
"quasslabs-api-mcp": {
"url": "https://api.quasslabs.com/mcp"
}
}
}Windsurf
{
"mcpServers": {
"quasslabs-api-mcp": {
"serverUrl": "https://api.quasslabs.com/mcp"
}
}
}Let your agent find its tools
Find servers like QuassLabs API MCP from your agent. Add Koskamo once to search the index for endpoints, access requirements, and client setup. Public discovery needs no key.
Other developer tools servers
Do you run QuassLabs API MCP?
Prove ownership with a DNS record, a well-known file, or your Official MCP Registry entry, and your edits publish immediately. Proofs are re-checked weekly; admins can still hide or remove a listing.