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136,035 tools. Last updated 2026-05-22 11:47

"author:78" matching MCP tools:

  • Use this read-only drilldown tool only when the user asks why one issuer or CIK was flagged in daily changes. It returns paginated raw CompanyFacts tag evidence for a specific ticker or CIK, plus page metadata and issuer identity. Parameters: ticker or cik is required; source_date is optional; limit defaults to 100 and is capped at 250; offset paginates the raw tag page. Behavior: read-only and idempotent; it performs one internal daily-changes read, filters evidence for one issuer/change, and has no destructive side effects. Do not use it for routine monitoring, Morning Brief, or Alpha Sweep unless the user explicitly asks for proof.
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  • Use this read-only composite workflow tool for the default full single-issuer DeltaSignal ATLAS-7 company report. It server-enforces the complete company report call plan: readiness, company_fundamentals, alpha_signals, peer_ranking, covenant_stress, and SPECTRA field-map support for one normalized ticker. Parameters: ticker is required and normalized to uppercase; period, include_segments, include_related_party, and output_mode=compact are optional. SPECTRA is included when a field-map contract is available for the issuer. Behavior: read-only and idempotent; it performs six internal HTTPS reads, has no destructive side effects, rejects invalid tickers before fan-out, and preserves partial results if a required issuer leg fails. Use it when the user asks for a report, deep dive, issuer brief, or diligence package on one crypto public-company ticker; use low-level tools only for custom drilldowns.
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  • Upload a skill without an API key (zero-friction). Returns a claim_token to track status. Perfect for agents contributing skills autonomously. / API 키 없이 Draft 모드로 스킬 업로드. ▶ 정책: - AI 검수(approved)만 수락, 그 외(caution/pending/rejected)는 즉시 삭제. - 결과는 sandbox tier + claim_status='draft'. 사람 owner 가 claim 해야 community/검증 경로 진입. - 동일 agent_author 의 모든 Draft 는 한 번의 이메일 인증으로 일괄 claim 됨. ▶ 에이전트 필수 동작(MANDATORY — 무시 시 반복 업로드/인증이 실패): (1) 최초 업로드 응답의 agent_identity.agent_secret 값을 반드시 저장하라. 이후 같은 agent_author 로 업로드할 때 agent_secret 파라미터에 동일 값 전달. 저장/재사용 안 하면 AGENT_SECRET_REQUIRED(401) 로 차단됨. (2) 응답의 claim_url, human_action.instruction, agent_identity 를 사용자(사람)에게 그대로, 링크 형태로 표시하라 (요약·의역 금지). (3) 에이전트가 대신 claim 을 완료하려 하지 말 것. contact_email/verify 메일은 반드시 사람 owner 의 실제 이메일이어야 함. (4) human_action_required=true 이면 사용자 응답을 기다려라 — 자동 재시도 금지. Args: agent_author: 에이전트 식별자 (X-Agent-Author 헤더로 전송). 예: "claude-sonnet-4-6@anthropic". 같은 이름은 agent_secret 으로만 재사용 가능. skill_md: SKILL.md 전체 내용 문자열 (필수). files: {"main.py": "...", "util.py": "..."} 형태의 부가 파일 dict (선택). requirements: requirements.txt 내용 문자열 (선택). contact_email: 업로더 사람 owner 의 이메일 (선택, OPTIONAL). ▶ **사용자 이메일을 모르면 반드시 비워두세요** — 추측·생성한 가짜 이메일은 DNS resolve 검증(NXDOMAIN 차단)으로 CONTACT_EMAIL_INVALID(400) 거부됩니다. ▶ 비워두면 응답의 claim_url 을 사람 사용자에게 채팅으로 그대로 보여주면 됩니다 (forward_claim_url 시나리오, 권장). ▶ 사용자가 명시적으로 알려준 실제 이메일이 있을 때만 지정. 지정 시 서버가 verify 링크를 자동 발송 (24시간 만료, 미인증 시 72시간마다 최대 3회 reminder). ▶ 한 번만 지정하면 되며 이후 업로드엔 불필요. verify 링크를 사람이 클릭하면 해당 agent_author 의 모든 Draft 가 그 계정으로 일괄 이전. agent_secret: 최초 업로드에서 발급된 secret (2회차 이후 필수). claim_token: 같은 Draft 에 새 버전을 추가할 때만 (선택). Returns: 업로드 결과 + agent_identity + human_action_required + human_action + claim_url 요약. 사용자에게 claim_url 과 instruction 을 반드시 surface 하라.
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  • Use this read-only diagnostic tool to explain why the alpha-opportunity board includes, excludes, or demotes rows. It returns issuer-type, identity, quality-gate, and raw-alpha-versus-board-rank summaries from the same scoring universe used by deltasignal_alpha_opportunities. Parameters: limit is 1-100 for bounded samples; source_date replays a known YYYY-MM-DD slice; issuer_type narrows the audit to operating_company, etf_trust, fund_vehicle, foreign_issuer, unresolved_identifier, or all; include_rows=true attaches full publishable audit rows and should be used only for explicit debugging. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, and does not change board scoring, payments, wallets, files, or account state. Use it after deltasignal_alpha_opportunities or deltasignal_alpha_sweep when the user asks why a high raw alpha row is missing, why ETF/trust/fund rows are excluded by default, why a row was demoted, or whether a screen is safe to summarize.
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  • Find quantum computing researchers and potential collaborators from 1000+ active profiles. Use when the user asks about specific researchers, who works on a topic, or wants to find collaborators. NOT for jobs (use searchJobs) or papers (use searchPapers). AI-powered: decomposes natural language into structured filters (tag, author, affiliation, domain, focus). Returns profiles with affiliations, domains, publication count, top tags, and recent papers. Data from arXiv papers published in the last 12 months. Max 50 results. Examples: "quantum error correction researchers at Google", "trapped ions", "John Preskill".
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  • Find specific PASSAGES inside books — returns page-level snippets with citation URLs. Use this when you want a quote or evidence on a topic across the whole library. ORIENTATION HINT: if the user has named a specific author or work, prefer get_book (returns a summary + chapter outline) over passage hunting — every book in the corpus has an AI-generated summary that is usually the right first read. Use search_translations when sweeping across many books for evidence of a theme. For finding which BOOKS cover a topic, use search_library. Query tips: single distinctive terms ("memory palace", "wax tablet") work best; multi-word natural-English queries ("unity of the intellect") may return fewer results because matching is term-based, not phrase-based. Each snippet has a snippet_type — "translation"/"ocr" means it is a verbatim extract from the source text; "summary" means it is AI-generated description (do not quote those as the author's words). Response includes total_matches, returned, and offset for pagination. Cross-cultural tip: for pre-modern or non-Western topics, search source-tradition vocabulary rather than modern English terms — e.g. for seminal economy search "jing" or "bindu" or "istimnāʾ", not "semen retention"; for female homoeroticism search "tribade" or "sahq", not "lesbian". The corpus is indexed via period translations that use tradition-internal terminology.
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  • SEC/XBRL issuer intelligence for crypto public companies via MCP, OpenAPI, x402, and MPP.

  • magic-8-ball MCP — wraps StupidAPIs (requires X-API-Key)

  • Upload a skill without an API key (zero-friction). Returns a claim_token to track status. Perfect for agents contributing skills autonomously. / API 키 없이 Draft 모드로 스킬 업로드. ▶ 정책: - AI 검수(approved)만 수락, 그 외(caution/pending/rejected)는 즉시 삭제. - 결과는 sandbox tier + claim_status='draft'. 사람 owner 가 claim 해야 community/검증 경로 진입. - 동일 agent_author 의 모든 Draft 는 한 번의 이메일 인증으로 일괄 claim 됨. ▶ 에이전트 필수 동작(MANDATORY — 무시 시 반복 업로드/인증이 실패): (1) 최초 업로드 응답의 agent_identity.agent_secret 값을 반드시 저장하라. 이후 같은 agent_author 로 업로드할 때 agent_secret 파라미터에 동일 값 전달. 저장/재사용 안 하면 AGENT_SECRET_REQUIRED(401) 로 차단됨. (2) 응답의 claim_url, human_action.instruction, agent_identity 를 사용자(사람)에게 그대로, 링크 형태로 표시하라 (요약·의역 금지). (3) 에이전트가 대신 claim 을 완료하려 하지 말 것. contact_email/verify 메일은 반드시 사람 owner 의 실제 이메일이어야 함. (4) human_action_required=true 이면 사용자 응답을 기다려라 — 자동 재시도 금지. Args: agent_author: 에이전트 식별자 (X-Agent-Author 헤더로 전송). 예: "claude-sonnet-4-6@anthropic". 같은 이름은 agent_secret 으로만 재사용 가능. skill_md: SKILL.md 전체 내용 문자열 (필수). files: {"main.py": "...", "util.py": "..."} 형태의 부가 파일 dict (선택). requirements: requirements.txt 내용 문자열 (선택). contact_email: 업로더 사람 owner 의 이메일 (선택, OPTIONAL). ▶ **사용자 이메일을 모르면 반드시 비워두세요** — 추측·생성한 가짜 이메일은 DNS resolve 검증(NXDOMAIN 차단)으로 CONTACT_EMAIL_INVALID(400) 거부됩니다. ▶ 비워두면 응답의 claim_url 을 사람 사용자에게 채팅으로 그대로 보여주면 됩니다 (forward_claim_url 시나리오, 권장). ▶ 사용자가 명시적으로 알려준 실제 이메일이 있을 때만 지정. 지정 시 서버가 verify 링크를 자동 발송 (24시간 만료, 미인증 시 72시간마다 최대 3회 reminder). ▶ 한 번만 지정하면 되며 이후 업로드엔 불필요. verify 링크를 사람이 클릭하면 해당 agent_author 의 모든 Draft 가 그 계정으로 일괄 이전. agent_secret: 최초 업로드에서 발급된 secret (2회차 이후 필수). claim_token: 같은 Draft 에 새 버전을 추가할 때만 (선택). Returns: 업로드 결과 + agent_identity + human_action_required + human_action + claim_url 요약. 사용자에게 claim_url 과 instruction 을 반드시 surface 하라.
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  • Use this read-only screening tool to rank the active DeltaSignal issuer universe by deterministic Phase 1 alpha score. It returns opportunity rows with ticker, CIK/entity metadata when available, issuer type, raw alpha score, board rank score, risk tier, debt coverage, quality, treasury, regime, and provenance fields. Parameters: limit is 1-100; source_date replays a known YYYY-MM-DD slice; risk_tier, quality_flag, issuer_type, include_funds, and debt_coverage_status narrow the screen. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, and does not handle wallets, payments, orders, or account state. Default behavior returns operating-company issuers. Use include_funds=true or issuer_type=etf_trust|fund_vehicle|all only when the user asks for ETF, trust, fund, or product-vehicle screens. High scores are drilldown candidates, not standalone conclusions.
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  • Use this read-only composite workflow tool for a fast single-ticker sanity check without the full company-report payload. It server-enforces the quick-check call plan: readiness, covenant_stress, and alpha_signals for one normalized ticker. Parameters: ticker is required and normalized to uppercase; output_mode=compact is optional. Fundamentals, peer ranking, and SPECTRA are intentionally excluded. Behavior: read-only and idempotent; it performs three internal HTTPS reads, has no destructive side effects, rejects invalid tickers before fan-out, and preserves partial results if a required issuer leg fails. Use it when the user asks whether one ticker is clean, stressed, actionable, or needs deeper diligence.
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  • Expand one author into a deduplicated paper list. This is the main author->paper traversal tool and supports research filters. Use `author_id` when you already know the exact author, or `author_name` plus `candidate_index` after `scholarfetch_author_candidates`. Supported comma-separated `filters`: year>=YYYY, year<=YYYY, year=YYYY, has:abstract, has:doi, has:pdf, venue:<text>, title:<text>, doi:<text>. If you pass `engines`, it must include `openalex`.
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  • Use this read-only composite workflow tool for the default full single-issuer DeltaSignal ATLAS-7 company report. It server-enforces the complete company report call plan: readiness, company_fundamentals, alpha_signals, peer_ranking, covenant_stress, and SPECTRA field-map support for one normalized ticker. Parameters: ticker is required and normalized to uppercase; period, include_segments, include_related_party, and output_mode=compact are optional. SPECTRA is included when a field-map contract is available for the issuer. Behavior: read-only and idempotent; it performs six internal HTTPS reads, has no destructive side effects, rejects invalid tickers before fan-out, and preserves partial results if a required issuer leg fails. Use it when the user asks for a report, deep dive, issuer brief, or diligence package on one crypto public-company ticker; use low-level tools only for custom drilldowns.
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  • Use this read-only composite workflow tool for risk and stress monitoring across the current DeltaSignal issuer universe. It server-enforces the pressure-board call plan: readiness, top_stressed with limit 15, and risk_distribution. Parameters: optional output_mode=compact only; do not pass limit, offset, ticker, source_date, or issuer filters because this preset owns exact arguments internally. Behavior: read-only and idempotent; it performs three internal HTTPS reads, has no destructive side effects, never calls issuer-level tools, and preserves partial results if one internal call fails. Use it when the user asks for risk monitoring, pressure board, stress board, top stressed overview, or current risk mix.
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  • Delete a test suite on a Keploy branch — synchronous, no playbook to walk. USE THIS when: * The dev's update_test_suite call was rejected with "preserves no steps from the existing suite — that's a full rewrite, not an edit". Delete the existing suite and re-author from scratch via create_test_suite. The error message itself routes here. * The dev explicitly says "delete the suite", "remove suite X", "wipe my orderflow suite". * A genuine wholesale redesign — every step changed in shape — that the audit trail shouldn't try to reconcile as edits. DO NOT USE THIS when: * The dev wants a real edit (one assertion, one step's body). Use update_test_suite + preserve existing step IDs instead — keeps audit history intact. * The dev wants to "redo" a single failed run. Test runs are independent of suite state; just rerun via replay_test_suite. INPUT * app_id (required) — Keploy app id * suite_id (required) — UUID of the suite to delete * branch_id (required) — Keploy branch UUID. The delete creates a branch-scoped DeleteTestSuite audit event so reads on the same branch see the suite as gone. Direct main writes are blocked. OUTPUT * On success: {"deleted": true} — suite is tombstoned at the branch overlay; subsequent reads (getTestSuite / listTestSuites) on this branch return 404 / exclude it. * 404 if the suite_id doesn't exist on this app/branch (verify via getTestSuite or listTestSuites first if you're unsure). After delete, the standard re-create flow is: (1) call create_test_suite with a freshly authored steps_json. The new suite gets a fresh suite_id; the old id is tombstoned, not reusable. ═══════════════════════════════════════════════════════════════════ DISCOVERY — when the dev hands you a bare suite_id with no app_id / branch_id: ═══════════════════════════════════════════════════════════════════ Suites live on a (app_id, branch_id) tuple. A bare suite_id has no on-disk hint about which app or branch holds it; you have to RESOLVE both before calling this tool. Walk these steps in order — STOP as soon as getTestSuite returns 200: 1. Detect the dev's git branch: Bash `git rev-parse --abbrev-ref HEAD` in app_dir. If exit non-zero / output is "HEAD" → not a git repo / detached HEAD; ASK the dev for the Keploy branch name (don't invent one). 2. Resolve candidate apps via the cwd basename: Bash `basename $(pwd)` → call listApps with q=<basename>. Usually 1–2 candidates. If 0 → ASK; if >1 → walk every candidate in step 4. 3. For each candidate app, call list_branches({app_id}) and find the branch whose `name` matches the git branch from step 1. That gives you {branch_id}. If no match → not this app, try next. 4. Verify with getTestSuite({app_id, suite_id, branch_id=<from step 3>}). 200 → resolved; 404 → wrong app/branch, try next. 5. If steps 2–4 exhaust, walk every OPEN branch on each candidate app, then try main (branch_id omitted). If still nothing → ASK the dev for the {app_id, branch_id} pair. After resolving once in a session, REUSE the {app_id, branch_id} for subsequent suite-targeted calls; don't re-walk discovery for every action.
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  • Conceptual / semantic passage search across the whole library. Use when the modern term won't literally appear in historical texts — e.g. "distributed cognition" maps to passages about active intellect, art of memory, wax tablet metaphors; "social contract" maps to pre-Hobbesian discussions of consent and authority. Ranks passages by cosine similarity on Gemini embeddings (768d), so paraphrases and conceptually adjacent phrasings match even when no keyword overlaps. ORIENTATION HINT: if the user named a specific author or work, prefer get_book (returns the book's AI summary + chapter outline) — semantic search is expensive and best reserved for cross-corpus discovery. Prefer search_translations for literal phrases or distinctive single terms; use search_concept when the concept matters more than the wording. Similarity calibration: 0.70+ is a strong match, 0.55–0.70 is worth reading but verify, below 0.55 is mostly conceptual drift. Set max_per_book to diversify results across many books rather than cluster on one source. Each passage carries a snippet_type — quote only "translation" snippets, never "summary". Cross-cultural tip: for pre-modern or non-Western topics, also try source-tradition vocabulary — e.g. for seminal economy try "jing preservation" or "bindu yoga" or "istimnāʾ"; for masturbation try "mollities" (Latin) or "hastamaithuna" (Sanskrit) or "shouyin" (Chinese). The corpus is indexed via period translations that use tradition-internal terminology, so adjacent/euphemistic terms often surface material that modern English keywords miss.
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  • Edit an existing test suite — change one or more step bodies, assertions, headers, or remove/add steps. Returns a playbook that delegates to `keploy update-test-suite`, which validates the new state (static structural checks + 2 live runs for idempotency + GET-coupling check) and snapshot-replaces the suite via api-server. POST-EDIT BEHAVIOUR: any structural change here (step method/url/body/headers/extract/assert, or add/delete steps) AUTOMATICALLY clears the suite's sandbox test server-side — the suite comes back as linked=false. Call record_sandbox_test on the updated suite before any sandbox replay; otherwise replay_sandbox_test will 400 with "no sandboxed tests". Cosmetic-only edits (name, description, labels) preserve the sandbox test. ═══════════════════════════════════════════════════════════════════ FETCH-FIRST RULE — required for the edit to be accepted: ═══════════════════════════════════════════════════════════════════ The api-server's replace handler rejects updates that preserve ZERO step IDs from the existing suite ("full rewrite, not an edit"). To make a real edit: 1. Call getTestSuite first (or use download_recording / get_app_testing_context if you already have the suite). Capture each existing step's "id" field. 2. Compose your new steps_json INCLUDING the existing "id" on every step you want to KEEP or EDIT. Omit "id" only on steps you're ADDING. Drop a step entirely from steps_json to DELETE it. 3. Call this tool with that merged steps_json. If you author a fresh JSON without the existing step IDs, the server rejects it with "preserves no steps from the existing suite". When that happens, your two options are: (a) re-author with IDs preserved (preferred — keeps history), or (b) call delete_test_suite then create_test_suite (loses history, fresh suite_id). ═══════════════════════════════════════════════════════════════════ DISCOVERY — when the dev hands you a bare suite_id with no app_id / branch_id: ═══════════════════════════════════════════════════════════════════ Suites live on a (app_id, branch_id) tuple. A bare suite_id has no on-disk hint about which app or branch holds it; you have to RESOLVE both before calling this tool. Walk these steps in order — STOP as soon as getTestSuite returns 200: 1. Detect the dev's git branch: Bash `git rev-parse --abbrev-ref HEAD` in app_dir. If exit non-zero / output is "HEAD" → not a git repo / detached HEAD; ASK the dev for the Keploy branch name. 2. Resolve candidate apps via the cwd basename: Bash `basename $(pwd)` → call listApps with q=<basename>. Usually 1–2 candidates. If 0 → ASK; if >1 → walk every candidate in step 4. 3. For each candidate app, call list_branches({app_id}) and find the branch whose `name` matches the git branch from step 1. That gives you {branch_id}. If no match → not this app, try next. 4. Verify with getTestSuite({app_id, suite_id, branch_id=<from step 3>}). 200 → resolved; 404 → wrong app/branch, try next. 5. If steps 2–4 exhaust, walk every OPEN branch on each candidate app, then try main (branch_id omitted). If still nothing → ASK the dev for the {app_id, branch_id} pair. The getTestSuite call in step 4 is the one whose response you also use to capture every step's existing "id" for the FETCH-FIRST RULE above — so step 4 is actually a 2-for-1: discovery AND fetch-first happen on the same call. After resolving once in a session, REUSE the {app_id, branch_id} for subsequent suite-targeted calls; don't re-walk discovery for every action. ═══════════════════════════════════════════════════════════════════ INPUTS ═══════════════════════════════════════════════════════════════════ * app_id (required) — Keploy app id * suite_id (required) — UUID of the suite to update * branch_id (required) — Keploy branch UUID (resolve via the two-step flow before calling) * steps_json (required) — JSON array of the FULL desired step list. Each kept step MUST carry the existing "id". Same step shape as create_test_suite (response, extract, assert, etc — all static structural checks apply). * name / description / labels (optional) — overrides for top-level suite metadata * app_url (required) — base URL of the dev's running local app, e.g. http://localhost:8080. The CLI fires the new state TWICE against this for the idempotency check + GET-coupling check. * app_dir (optional) — repo root the CLI cd's into; defaults to "." ═══════════════════════════════════════════════════════════════════ HOW THIS TOOL WORKS ═══════════════════════════════════════════════════════════════════ This tool DOES NOT call api-server itself. It returns a 3-step playbook for you (Claude) to walk via Bash — same shape as create_test_suite: 1. Write merged JSON to a temp file. 2. Run `keploy update-test-suite --suite-id <id> --file <path> --branch-id <uuid> --base-url <url>` — runs every static structural check, fires the new state twice locally, applies the GET-coupling check, then POSTs the snapshot-replace. 3. Cleanup the temp file. Walk the playbook in order. If step 2 exits non-zero, surface stdout to the dev — it has the rule violation / failure detail. OUTCOMES the AI should recognize: * Exit 0 + stdout has "✓ suite updated:" + "View:" line → success. Surface the View URL to the dev. * Exit 1 + "preserves no steps from the existing suite" → fetch-first rule was missed. Re-author with step IDs preserved (or call delete_test_suite + create_test_suite as the documented escape hatch). * Exit 1 + structural-check violations → fix the suite per the violation messages, then REWRITE the suite file via Bash and RE-RUN this CLI command directly. DO NOT call update_test_suite again to retry — the playbook + file path are already valid; only the JSON content needs revision. The validator output includes a canonical step skeleton on structural failures. * Exit 2 + "couldn't reach the dev's app" → ensure the app is up at app_url and retry. PREREQUISITES the playbook assumes: * The dev's app is up and reachable at app_url. * `keploy` binary is on PATH. If missing, install before calling this tool: `curl --silent -O -L https://keploy.io/install.sh && source install.sh`. * Either ~/.keploy/cred.yaml exists or KEPLOY_API_KEY is exported.
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  • Use this read-only composite workflow tool for opportunity and alpha screening across the current DeltaSignal issuer universe. It server-enforces the alpha-sweep call plan: readiness, alpha_opportunities with limit 15, and daily_changes; alpha_opportunities defaults to operating-company issuers. Parameters: optional output_mode=compact only; do not pass limit, offset, ticker, source_date, or issuer filters because this preset owns exact arguments internally. Behavior: read-only and idempotent; it performs three internal HTTPS reads, has no destructive side effects, never calls issuer-level tools, and preserves partial results if one internal call fails. Use it when the user asks for alpha opportunities, opportunity sweep, clean alpha board, or names worth follow-up research; treat the result as a screen requiring issuer drilldown.
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  • Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet (crypto price / Fed rate / geopolitical / sports / corporate / drug approval / election / other), fans out to the right packs (e.g. crypto+fred+gdelt for a BTC bet, fred+bls for a Fed bet, gdelt+acled+comtrade for Strait of Hormuz), and returns an evidence packet plus a simple market-vs-model comparison so the caller can see where the implied probability disagrees with the data. Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?". This is the core demo product — agents that get bet-relevant context here convert better than ones that have to discover the packs themselves.
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  • Find arbitrage opportunities on Polymarket by checking for monotonicity violations across related markets. TWO MODES: (1) `event` — pass a single Polymarket event slug; walks that event's child markets and checks ordering within it. (2) `topic` — pass a topic / seed question (e.g. "Strait of Hormuz traffic returns to normal"); the tool searches across separate events for related markets, groups them, then checks monotonicity. Cross-event mode catches the cases where Polymarket lists each cutoff as its own event ("…by May 31" is event A, "…by Jun 30" is event B — single-event mode misses the May≤June rule). Returns ranked opportunities with suggested trade direction + reasoning.
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  • Run Disco on tabular data to find novel, statistically validated patterns. This is NOT another data analyst — it's a discovery pipeline that systematically searches for feature interactions, subgroup effects, and conditional relationships nobody thought to look for, then validates each on hold-out data with FDR-corrected p-values and checks novelty against academic literature. This is a long-running operation. Returns a run_id immediately. Use discovery_status to poll and discovery_get_results to fetch completed results. Use this when you need to go beyond answering questions about data and start finding things nobody thought to ask. Do NOT use this for summary statistics, visualization, or SQL queries. Public runs are free but results are published. Private runs cost credits. Call discovery_estimate first to check cost. Private report URLs require sign-in — tell the user to sign in at the dashboard with the same email address used to create the account (email code, no password needed). Call discovery_upload first to upload your file, then pass the returned file_ref here. Args: target_column: The column to analyze — what drives it, beyond what's obvious. file_ref: The file reference returned by discovery_upload. analysis_depth: Search depth (1=fast, higher=deeper). Default 1. visibility: "public" (free) or "private" (costs credits). Default "public". title: Optional title for the analysis. description: Optional description of the dataset. excluded_columns: Optional JSON array of column names to exclude from analysis. column_descriptions: Optional JSON object mapping column names to descriptions. Significantly improves pattern explanations — always provide if column names are non-obvious (e.g. {"col_7": "patient age", "feat_a": "blood pressure"}). author: Optional author name for the report. source_url: Optional source URL for the dataset. use_llms: Slower and more expensive, but you get smarter pre-processing, summary page, literature context and pattern novelty assessment. Only applies to private runs — public runs always use LLMs. Default false. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.
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  • Fetch a single article from Psychiatry for Kids by slug. Returns title, body content, author, clinical reviewer, citations, and metadata.
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