"Travis CI" matching MCP tools:

• strale_balance

  Strale - 290+ API capabilities for AI agents

  Returns the current Strale wallet balance. Call this before executing paid capabilities to verify sufficient funds, or after a series of calls to reconcile spend. Returns balance in EUR cents (integer) and formatted EUR string. Requires an API key — returns an auth instruction if none is configured.

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• convoopen

  InsideOut (Riley)

  WORKFLOW: Step 1 of 4 - Start infrastructure design conversation Open an InsideOut V2 session and receive the assistant's intro message. The response contains a clean message from Riley (the infrastructure advisor) - display it to the user. ⚠️ Riley will ask questions - forward these to the user, DO NOT answer on their behalf. CRITICAL: This tool returns a session_id in the response metadata. You MUST use this session_id for ALL subsequent tool calls (convoreply, tfgenerate, tfdeploy, etc.). ⚠️ The session_id includes a ?token=... suffix (format: sess_v2_xxx?token=yyy) which is part of the session credential — without it, downstream tools fall back to a tokenless connect URL that 401s. Always pass session_id verbatim to subsequent tools and to the user; do NOT shorten, paraphrase, or strip the ?token= portion when summarizing the session in chat or in your own scratch notes. Use when the user mentions keywords like: 'setup my cloud infra', 'provision infrastructure', 'deploy infra', 'start insideout', 'use insideout', or similar intent to begin infra setup. OPTIONAL: project_context (string) - General tech stack summary so Riley can skip discovery questions and jump to recommendations. The agent should confirm this with the user before sending. Include whichever apply: language/framework, databases/services, container usage, existing IaC, CI/CD platform, cloud provider, Kubernetes usage, what the project does. Example: 'Next.js 14 + TypeScript, PostgreSQL, Redis, Docker Compose, deployed to AWS ECS, GitHub Actions CI/CD, ~50k MAU'. NEVER include credentials, secrets, API keys, PII, source code, or internal URLs/IPs -- only general metadata summaries useful to a cloud architect agent. IMPORTANT: source (string) - You MUST set this to identify which IDE/tool you are. Auto-detect from your environment: 'claude-code', 'codex', 'antigravity', 'kiro', 'vscode', 'web', 'mcp'. If unsure, use the name of your IDE/tool in lowercase. Do NOT omit this — it controls the 'Open {IDE}' button on the credential connect screen. OPTIONAL: github_username (string) - GitHub username for deploy commit attribution. Pre-populates the GitHub username field on the connect page. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.

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• audit_chain_artifact_claim

  DERO MCP Server

  Composite: audit a chain artifact (block topoheight, block hash, TX hash, and/or proof string) end-to-end. Returns a verdict (`cited_in_false_claim` | `clean`), the actual on-chain facts (block reward, TX acceptance status), an optional proof-string decode, a relayable narrative, and curated rebuttal docs citations. When to call: when the user asks "what's going on with DERO block X?" / "is this transaction the inflation-claim TX?" / "does this proof string come from a known false claim?" PREFER this over chaining `dero_get_block_header_by_topo_height` + `dero_get_transaction` + `dero_decode_proof_string` yourself: the composite already runs them in parallel, joins them against the flagged false-claim registry, and emits a single `verdict` field plus a narrative so the agent does not need to compose the rebuttal arc from scratch each time. Input Requirements (CRITICAL): - At least ONE of `topoheight`, `block_hash`, `tx_hash`, or `proof_string` MUST be provided. The composite throws `INVALID_INPUT` otherwise. - `topoheight` is OPTIONAL. Non-negative integer. - `block_hash` is OPTIONAL. 64 hex characters. - `tx_hash` is OPTIONAL. 64 hex characters. - `proof_string` is OPTIONAL. Full `deroproof…` / DERO bech32 string with HRP. - `include_forge_demo` is OPTIONAL (default false). When true AND `tx_hash` is provided, also forges a fresh demo proof for the same TX (via `dero_forge_demo_proof`) and embeds it under `forge_demo`. The demo amount auto-selects: a flagged artifact's pinned amount (e.g. -2.2M for the 2022 claim) > the cited `proof_string` V > -1 DERO. PREFER setting this true when the agent is fielding a "Verified ✓ means the chain minted coins, right?" question — the embedded forge IS the refutation. Output: `{ verdict, inputs, matched_artifacts[], context_note, chain_facts, proof_decode, forge_demo, narrative, related_docs, _diagnostics }`. `verdict` is `cited_in_false_claim` when any input matches the flagged-artifact registry, else `clean`. `chain_facts` is null when no chain-querying input was provided or all daemon calls failed; `proof_decode` is null when no `proof_string` was provided. `forge_demo` is null unless `include_forge_demo: true` was passed; on success it carries `{ skipped: false, forged_proof_string, target_amount, ring_slot, ring_size, ring_receiver_address, math, self_check, explorer_display_amount, demo_amount_source }` (the slim form — full citations stay at the top level). PREFER citing the returned `related_docs` verbatim in the agent response — they are the canonical rebuttal pages and have been validated against the bundled docs index by CI. Quote the `context_note` when verdict is `cited_in_false_claim` so the user understands why the artifact matters.

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• diff_attack_paths

  cloud-pathfinder

  DIFFERENTIAL attack-path analysis for a change to Infrastructure-as-Code — the CI/CD gate. Give it the IaC BEFORE and AFTER a change (e.g. a pull request's base and head trees, each a map of filename→content) and it builds the full resource graph + runs the internet→crown-jewel reachability search on BOTH states, then reports exactly what the change did to your attack surface: which breach chains it INTRODUCES (e.g. 'this PR opens a NEW Internet→admin route via a newly-public security group + an over-broad IAM grant'), which it ELIMINATES, and which it AGGRAVATES (makes more exploitable). Returns an INTRODUCES_BREACH / REDUCES_RISK / NEUTRAL / MIXED verdict — the single check to wire into PR review so a change that opens a path to your data/secrets/admin is caught before merge. This is something a per-file linter or a single-state scan cannot answer: it needs both graphs and a semantic cross-state path match. Heuristic static analysis of declared IaC.

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• get_resource_schema

  Control Plane

  Return the exact object schema and REST API endpoints for a Control Plane resource kind, so you can author an accurate manifest for `cpln apply` or call the API directly. ALWAYS call this FIRST whenever you are about to write a cpln apply YAML/JSON file, set up CI/CD that applies Control Plane resources, or build a request body for the REST API — do not hand-write a manifest or guess field names from memory. Pick a `kind` and pass `org` (and `gvc` for workload/identity/volumeset). Large schemas come back as a shallow map with deep sections collapsed to {"_expand":"<path>"} stubs; pass `path` (e.g. "spec.containers") to expand a section on demand. Server-managed fields (id/status/version/etc.) are already removed; `name` and `kind` are required at create.

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• get_latest_news

  Gloria AI

  Get the latest curated crypto news headlines. Returns real-time news items with headline, sentiment, categories, and sources. Use the category parameter to filter by topic (e.g. 'bitcoin', 'defi', 'ai'). Call get_categories first to see all available category codes. Args: category: Filter by category code (e.g. 'bitcoin', 'ethereum', 'defi', 'ai'). Omit to get news across all categories. limit: Number of items to return (1-10, default 5).

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• call_operation

  Fiber AI

  Execute any Fiber API operation by operationId. IMPORTANT: - Most operations require `apiKey` in the request body. - Prefer search_endpoints() first when operation choice is ambiguous. - Use get_endpoint_details_full(operationId='...') first for complex/nested parameter shapes. Args: operationId: The operation ID to call params: Parameters dict: {"body": {...}, "path": {...}, "query": {...}} Returns: API response data or error details.

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• get_briefed

  VARRD — Statistically Validated Trading Edges + AI Research Engine

  Get a personalized market news briefing based on your validated edge library. Profiles your strategies, searches today's news for the instruments and setups you actually trade, and writes a concise digest connecting each headline to your specific book. Each news item includes a ↳ line tying it to your actual positions and edges (e.g. 'your ES momentum setups', 'your GC mean-reversion edge'). Requires at least 5 strong edges in your library. Costs credits.

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• ai

  Fast.io

  Ripley — the MCP delegation surface over Fastio's RAG agent. Ripley is read-only for storage CONTENT: it answers natural-language questions about workspace/share files & folders (with citations) and never creates/edits/deletes your files — for content writes, call the primitive MCP tools directly. It DOES create/manage chat threads (chat-create/chat-update/chat-delete/message-send) and can generate shares (share-generate). Prefer Ripley over issuing many primitive reads: ask one NL question and let the server-side agent search + synthesize. Quick start: action='ask' (question + profile) → returns {answer_text, citations, chat_id, message_id, web_url}; action='status' for an engineered workspace-status summary. Lower-level chat/message actions remain for multi-turn control. Call action='describe' for the full action/param reference. Destructive: chat-delete. Side effects: ask/status/chat-create/message-send consume credits; chat-cancel terminates an in-progress message (partial tokens billed; idempotent). Verbosity (detail param): chat-list/message-list default to terse (compact rows). chat-details/message-details default to full (drill-down). Pass an explicit detail='standard'|'full' to override (best-effort: chat/message/activity endpoints may not yet honor detail server-side).

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• get_prediction_accuracy

  OneQAZ Trading Intelligence

  Purpose: Long-term hit rate per (category, target_market, lag_bucket) cell, with sample_count and Wilson 95% CI. Filtered for statistical significance (sample_count >= 3). Most important credibility-evaluation tool — includes a pre-computed edge_cells view (cells whose CI lower bound clears the 0.333 3-class baseline) and anti_predictive_cells (honestly exposed). Triggers (casual questions too): "how accurate are your predictions?", "예측 잘 맞아?", "track record 있어?", "can I trust these forecasts?", "적중률 보여줘", "does macro actually predict these markets?". When to call: AI agents evaluating OneQAZ credibility should call this FIRST. Prerequisites: none. Next steps: get_ledger_integrity (tamper-evidence for these numbers), get_backtest_tuning_state (self-calibration), get_monthly_accuracy_trend (time series). Caveats: empty when no backtests have completed yet. Filter cells by samples >= 50 for high-confidence claims, and by horizon_type='forecast' for predictive skill (0h cells are nowcast consistency checks). Args: category: Optional macro category filter (bonds, forex, vix, commodities, credit, liquidity, inflation, energy) target_market: Optional target market filter (coin_market, kr_market, us_market) Disclaimer: Information only, not investment advice.

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• search_admin

  justicelibre

  Recherche pondérée par pertinence BM25 sur la jurisprudence administrative complète (Conseil d'État + 9 CAA + 40 TA). Source : bulk JADE DILA (~550 k décisions full text). Contrairement aux outils `search_admin_recent*` qui trient par date, celui-ci classe par pertinence sémantique des mots-clés. Indispensable pour trouver LES bonnes décisions sur un sujet sans dépendre de l'ancienneté. ⚠️ **Si tu cherches par numéro de requête (7 chiffres ex: 2200433)**, utilise plutôt `get_admin_decision(numero, juridiction=...)` qui fait un lookup SQL exact. La recherche FTS5 d'un numéro court ne le trouve que dans les décisions qui le **citent** dans leur texte (ex: décision de cassation), pas la décision identifiée par ce numéro. Args: query: mots-clés (opérateurs FTS5 : AND/OR/NOT, "phrase exacte", mot*) juridiction: filtre par fragment de nom de juridiction. Ex : "Lyon" → toutes les décisions Lyon (TA + CAA), "Tribunal Administratif de Lyon" → uniquement TA Lyon. Combiné en FTS5 AND avec la query principale. sort: "relevance" (défaut, BM25) ou "date_desc" / "date_asc" date_min: limite inférieure ISO YYYY-MM-DD (optionnel) date_max: limite supérieure ISO YYYY-MM-DD (optionnel) limit: nombre de résultats (défaut 20, max 50) offset: pagination Returns: {"total", "returned", "decisions": [...]} avec extracts BM25.

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• register_agent

  prxhub

  TIME-CRITICAL + HUMAN-IN-THE-LOOP. The device code returned by this tool expires in 10 minutes (600 seconds). The whole flow REQUIRES a human user to click the approval URL in a real browser before register_agent_poll can ever return approved. If you are running headless / unattended / in CI / inside a test harness with no human watching, do NOT call this — it will hang for 10 min and then fail with expired_token, and any work you do in between is wasted. Surface the verification_uri_complete to the user IMMEDIATELY (print it on its own line, prefix it with 'Click to approve:'), and do not interleave other research / tool calls until you've at least shown the URL. DO NOT CALL THIS BLINDLY. Before calling register_agent, check for an existing identity on disk. The lookup order is: 1. $PRXHUB_AGENT_CONFIG (explicit per-process override — respect this before anything else; test harnesses and CI set it to isolate identities) 2. $XDG_CONFIG_HOME/prx/agent.json 3. $HOME/.config/prx/agent.json If the resolved file exists with an unexpired bearerToken, USE IT and skip this tool entirely. Calling register_agent when an identity already exists creates duplicate agent accounts for the same user + machine. Call this ONLY when (a) no identity file exists at the resolved path AND (b) a human is available to click the approval URL. Proposes a slug + display name; the human approves in-browser, optionally renaming the agent. Returns a device code + a pre-filled approval URL. Then call register_agent_poll to wait for approval. Agents do NOT hold signing keys. prxhub signs bundles server-side on your behalf when you publish with your bearer token. ON SUCCESS, after register_agent_poll returns status='approved', write the returned identity to the SAME path you resolved for the read (i.e. $PRXHUB_AGENT_CONFIG if set, else $XDG_CONFIG_HOME/prx/agent.json, else $HOME/.config/prx/agent.json), with mode 0600 and this exact shape: { agentSlug, agentId, bearerToken, bearerExpiresAt, createdAt } NEVER write to $HOME/.config/prx/agent.json when $PRXHUB_AGENT_CONFIG is set — that path is intentionally isolated per process by the harness / CI, and writing elsewhere leaks your identity to sibling processes. ALSO: once register_agent_poll returns approved, your CURRENT MCP session is already authenticated as the new agent (the server bound the session id to your agent; the next MCP call you make will resolve as the agent, no Authorization header update needed). The agent.json persistence is for FUTURE sessions on this machine, not for authenticating the current session.

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• panorama_sante_territoire

  france-data-mcp

  Panorama santé d'une commune française en 1 appel (V0.9). Agrège en parallèle : population (INSEE Melodi), densités médecins + infirmiers + pharmaciens avec comparaison nationale (méthodo DREES), nombre d'établissements FINESS par famille (default ["labo","pharmacie","ehpad","mco","msp_cpts"]), et un bloc DEMANDE (V0.22.0 — profil démographique de la commune agrégé depuis ses IRIS : âge, CSP, familles, revenu pondéré, à CROISER avec l'OFFRE ci-dessus pour l'aide à l'implantation ; `demande: null` si commune hors couverture IRIS (DOM non ingéré) — pour le détail au quartier ou un bassin par rayon, utiliser `profil_iris`). Remplace 7-10 appels MCP individuels par 1 seul. Ne renvoie AUCUNE interprétation métier (pas de qualification automatique 'désert médical') — le caller LLM applique sa grille. V0.19.0 : accepte `nom_commune` (string) comme alternative à `code_insee`. `departement` (V0.19) = hint resolver UNIQUEMENT (panorama ne calcule pas par dept ; un `departement` seul lève une erreur explicite). **Granularité mixte** : les densités professionnels et la population sont calculées au niveau **commune** ; le décompte FINESS est agrégé au niveau **département** dérivé du code INSEE (limitation V0.9 — pas de RPC count_finess_by_commune encore). Le champ `niveauEtablissements` du résultat indique `"departement"` (succès), `"indisponible"` (dept indérivable, ex code DOM tronqué) — utiliser cette information pour ne pas confondre ratios commune et dept. Paris/Marseille/Lyon NON supporté : le panorama par commune dépend de la densité par commune, indisponible pour ces villes (INSEE n'expose la population qu'à la commune entière, les praticiens RPPS aux arrondissements). Un code PLM (commune-mère 75056 ou arrondissement) lève une RangeError. Pour ces villes, interroger les tools individuels au niveau `code_dept` (75/69/13). Alias acceptés : `codeInsee`/`insee`/`code` → `code_insee`. Sources : RPPS / Annuaire Santé ANS (mensuel), FINESS DREES (bimensuel), INSEE Melodi (PMUN 2023).

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• find_candidates

  DevMatch - AI Recruiting & Mission-Aligned Engineer Discovery

  Search the DevMatch index for engineers matching a role or project. Input: pass the richest context you have — (1) a full job description (most common), (2) a synthesized brief after reviewing a company's public repo (README + stack + role needs — preferred over a bare URL when you've evaluated the project), (3) a public github.com repo URL (server fetches README/topics; private repos → paste README as text), or (4) an informal role brief. Longer, more specific input ranks better. Returns up to limit ranked candidates (default 20, max 50) with full inline profiles in structuredContent (view=candidates): login, name, bio, location, followers, html_url, top_repos, top_topics, signals, matched_projects, and contact. Results never include bots, CI, or service accounts — they are filtered out automatically. Use the optional `exclude` array (GitHub logins or org names) to drop additional accounts. AGENT MODE: consume structuredContent only. HUMAN MODE: MCP App panel shows candidate cards; use server instructions for text-only hosts. Do not call get_profile for handles already in these results unless the user asks for deeper detail.

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• taskbounty_security_check

  TaskBounty

  Scan a PUBLIC GitHub repo for GitHub Actions + CI security/maintenance hygiene before launch — ideal for apps built with Lovable, Bolt, Replit, Cursor, or v0 ("is my AI-built app safe to ship?"). Returns a safe summary: findings by category with counts, an unlisted report URL, and fix options. SCOPE, honestly: it checks GitHub Actions workflow + update-automation hygiene only — it does NOT check exposed secrets, auth, payments, webhooks, or runtime behavior, which need a manual review. No API key required. For PRIVATE repos, tell the user to run `npx taskbounty-check .` locally so their source never leaves their machine.

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• goal-add-evidence-text

  Planner (proof-of-done)

  SUBORDINATE / supplementary path — does NOT close an acceptance criterion. Adds a text-only note (URL to a permanent external source like CI run / GitHub commit / issue, or a description of a manual scenario) as extra context alongside the real proof. The path that actually covers an AC and closes a Grove goal is goal-attach-evidence — use that one for every criterion. Plain evidence NEVER counts toward AC coverage no matter how many you add; it is only a complement to an attached file. NOT for bytes — screenshots, logs, API responses, exports all go through goal-attach-evidence. NOT for filesystem paths — those need goal-attach-evidence with the actual file.

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• quote_rates

  LATAM Logistics (Envia: rates, labels, tracking, cancel across MX/CO/CL/BR/GT/AR/US/CA/ES)

  Compare shipping rates across every available carrier for a route + parcels, in one call — the Envia killer feature (AI picks the cheapest carrier for the merchant). Give the origin and destination addresses, the country (domestic) or origin_country + destination_country (cross-border), and the packages (weight in KG, dimensions in cm). Returns each carrier + service with its total price, currency and delivery estimate, and flags the cheapest. Read-only: quotes only, buys nothing and spends nothing. Credential headers (set once in your MCP client, per-request, never stored): x-envia-token (your Envia API token; sandbox tokens are self-service at https://ship-test.envia.com, production tokens from your live Envia account — sandbox and production tokens are SEPARATE) + x-envia-env (sandbox = default, no real shipping / no charge; production = real labels & real money). Owner-policy headers x-agentpay-max-amount / x-agentpay-approval-above / x-agentpay-allowed-tools also apply.

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• get_human_state

  Nefesh — Real-Time Human State Awareness for AI

  Get current unified human state for a session. Call this before generating important responses. Returns: - state: calm | relaxed | focused | stressed | acute_stress - stress_score: 0-100 (lower = calmer) - confidence: 0.0-1.0 (based on signal quality and device type) - suggested_action: maintain_engagement | simplify_and_focus | de-escalate_and_shorten | pause_and_ground - action_reason: human-readable explanation of why this action was suggested - adaptation_effectiveness (on 2nd+ call): shows whether your previous suggested_action actually reduced stress — contains previous_action, stress_delta, and effective boolean. Use this to self-improve. Use suggested_action to adapt your response: calm/relaxed = full complexity, focused = shorter and structured, stressed = max 2 sentences, acute_stress = one grounding sentence only. Requires a prior ingest call to have data. Not a medical device.

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• create_booking

  Teres — Booking for AI Agents

  Create a new booking/appointment at a business. Requires customer information (name and email) and a selected time slot. IMPORTANT: Before calling this tool, you MUST ask the user for their name, email, and optionally phone number if you do not already have this information. Do not guess or fabricate customer details. Returns a booking confirmation with a unique booking_id.

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• pysr_uncertainty

  Occam

  Bootstrap confidence intervals for the numeric constants of a frozen expression, plus optional prediction bands on an x-grid. Typical flow: call pysr_run, pick an expression from the response (best_expression or a pareto_front entry), pass it back here with the same dataset to get CIs on its fit constants. Returns frequentist bootstrap confidence intervals, not Bayesian credible intervals — posterior inference over expression structures is an open research problem. This tool freezes the expression chosen by the caller and bootstraps only its numeric constants; uncertainty about *which* expression is correct is not quantified. Bootstrap semantics: - If y_sigma is supplied, uses parametric bootstrap (y_b = y + Normal(0, y_sigma)). CI reflects user-stated measurement noise. - Otherwise uses residual bootstrap: fit once, resample residuals. CI reflects estimated-from-residuals noise. Only Float constants in the expression become free parameters. Integers stay structural (the 2 in x**2 is a function-class choice, not a fit constant). Expressions with no Float constants (e.g. "x + y") will be rejected with a validation error. Expression grammar: the `expression` string is parsed by sympy. Accepted operators are the same set pysr_run emits: unary `sin`, `cos`, `tan`, `exp`, `log`, `log2`, `log10`, `sqrt`, `abs`, `sinh`, `cosh`, `tanh`; binary `+`, `-`, `*`, `/`, `^` (or `**`). Whitespace and parenthesization are free. Every free symbol in the expression must correspond to an entry in `feature_names` — an unrecognised symbol is silently treated as a fresh sympy Symbol and the fit will fail downstream rather than reject early. Parse failures (syntax errors, malformed operators) surface as tool errors. If `feature_names` is supplied, its length must equal the number of columns in `X`; a mismatch is rejected with a validation error. Pricing: always free, regardless of dataset size. This tool has no `payment` parameter and is never subject to the x402/Stripe gate. Large bootstrap jobs still count against the shared rate limit below, so budget `n_resamples` accordingly. Rate limit: 10 requests/hour per IP, 200/hour global, max queue depth 20 (shared with sindy_run and pysr_run).

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