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187,775 tools. Last updated 2026-06-10 09:44

"namespace:io.github.Sugra-Systems" matching MCP tools:

  • Multi-turn conversation with Heista's creative direction engine — a real chat where the agent decides each turn what to produce based on what you ask for. Use whenever the work needs more than one round, OR when you want an output shape not covered by call_creative_worlds' `medium` enum. WHAT YOU CAN ASK FOR (any of these, turn 1 or any turn after): • Territories — "give me five directions for X", "what angles work here" • A TVC script — "write a 30-second TVC for Cowboys" • Billboard concepts — "three billboards under a quiet-authority lens" • A campaign platform — "build #2 into a full campaign with the big idea" • A manifesto or copy — "draft the manifesto in the brand voice" • Naming — "name this product, five options with rationale" • A PR stunt — "what's the newsworthy version of this" • A content series — "20 episode ideas for a brand podcast" • Packaging, sonic branding, partnerships, social systems • Refinement — "make #2 darker", "extend that into a tagline", "summarise" • Pivots — "forget the soft-drink angle, try the late-night insomnia one" SESSION: omit session_id on turn 1; the response returns a fresh session_id you pass on every subsequent turn — that is how the conversation persists. brand_id is only honoured on turn 1 of a new session (continuing sessions keep their original brand context). USE WHEN: user wants back-and-forth, OR wants an output shape outside the medium enum (manifesto, naming, press release, content series, packaging, etc.). Prefer call_creative_worlds when the user wants "three options, done" with no follow-up. WON'T DO: write OKRs / internal docs / strategy decks; behave as a general assistant. It is a creative director with creative-director taste — anti-cliché, specificity test, will push back on vague briefs. Metered — typically 2-10 credits per turn depending on tool use and context size. Charged after each turn on actual token usage.
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  • Use this read-only tool before analysis to verify that the DeltaSignal ATLAS-7 data plane is live, fresh, and safe to query. It returns service readiness, active source dates, issuer coverage, quality coverage, debt coverage, live-price status, market regime, and tower-coherence diagnostics. Parameters: none; call it exactly as-is when the user asks if DeltaSignal is ready or whether data freshness is acceptable. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, does not write external systems, and does not handle secrets or payments itself. Use it at the start of an agent workflow, after a deploy, or whenever results should be gated on freshness; use daily_changes for what changed and issuer tools for company-specific analysis.
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  • Computes Vimshottari Dasha from birth data and returns hierarchical period trees plus current Maha/Antar interpretation blocks. SECTION: WHAT THIS TOOL COVERS Computes the classical classical Vimshottari timeline from the Moon's birth nakshatra: Mahadasha and nested sub-periods up to the depth set by levels, with Julian and calendar boundaries and optional modern summaries. It returns data.periods[] and data.interpretation for the active periods. It does not compute Char Dasha, Yogini Dasha, Ashtottari, or transit correlations; use the dedicated tools for those systems. SECTION: WORKFLOW BEFORE: RECOMMENDED — asterwise_get_natal_chart — establishes chart and Moon context before interpreting Dasha lords. AFTER: asterwise_get_dasha_transits — correlates active Dasha lords with transits for the same birth data. SECTION: INPUT CONTRACT levels (int, default 3, max 5): tree depth — 1 = Mahadasha only; 2 adds Antardasha; 3 Pratyantar; 4 Sookshma; 5 Prana (much larger payload). Response dates in periods[] use DD/MM/YYYY, not ISO. BirthData fields follow global contract (date YYYY-MM-DD, time HH:MM; time='00:00' is accepted without flag — lagna-sensitive timing may be wrong if birth time is unknown). SECTION: OUTPUT CONTRACT data.periods[] — array of Mahadasha objects: planet (string) start_jd (float) end_jd (float) start_date (string — DD/MM/YYYY, not ISO) end_date (string — DD/MM/YYYY) modern_summary (string or null) sub[] — array of Antardasha objects with the same shape; sub=null at deepest level data.interpretation.current_mahadasha: planet (string) start_date (string) end_date (string) duration_years (float) modern_summary (string or null) favorable_conditions[] (string array) favorable_results[] (string array) unfavorable_conditions[] (string array) unfavorable_results[] (string array) timing_note (string) data.interpretation.current_antardasha — same fields as current_mahadasha plus mahadasha_planet (string) data.birth_time_provided (bool) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS MEDIUM_COMPUTE (~100ms at levels=1, ~1500ms at levels=5) SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): — levels < 1 or levels > 5 → MCP INVALID_PARAMS INVALID_PARAMS (upstream): — None — BirthData validation is upstream beyond Pydantic field constraints. INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Period start_date/end_date strings are DD/MM/YYYY; do not parse as ISO. SECTION: DO NOT CONFUSE WITH asterwise_get_char_dasha — classical sign-based periods with ISO dates on periods[], not planet-based Vimshottari. asterwise_get_yogini_dasha — 36-year eight-Yogini cycle with data.periods.root[], not Vimshottari. asterwise_get_ashtottari_dasha — 108-year alternative tree with data.periods.root[] and same levels semantics as this tool.
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  • Return the catalog of paired models — concrete real-world systems that live in two ChiAha sandboxes simultaneously, one for dynamics (DES via ReliaSim) and one for statistics (distribution fitting + validation via ReliaStats). Today: a single paired model — the bottling line. Returns canonical model IDs + cross-MCP routing metadata (which ReliaSim chapter, which ReliaSim MCP tools, which ReliaStats mode consumes which file shape). Use when a user asks about cross-MCP workflows, paired sandboxes, or the bottling-line example. ANTI-FABRICATION: this is a soft-reference catalog — to actually run a simulation, the LLM client calls ReliaSim's MCP tools directly.
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  • Given per-component reliabilities and a structure ('series' or 'parallel'), return the system reliability. Series = product (all must work). Parallel = 1 − product(1−Rᵢ) (at least one works). Useful for back-of-envelope RBD calcs before reaching for full RBD tooling. For mixed-structure systems (series with parallel sub-blocks), call this tool repeatedly on the sub-blocks. ANTI-FABRICATION: exact closed-form. Quote verbatim.
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  • Produces the Lal Kitab house and planet schema plus Rin (debt) flags from BirthData using Lal Kitab placement rules. Lal Kitab uses a distinct astrological system from standard Vedic computation, with its own house-based remedies. SECTION: WHAT THIS TOOL COVERS Returns data.system 'lal_kitab', ayanamsa, planets{} with lk_house and pucca/kachcha flags, twelve houses{} with occupants and significations, and rin_analysis with boolean debts, active_rins[], and rin_remedies[] rows. Do not merge these houses with asterwise_get_natal_chart Bhava Chalit without explicit user intent — frameworks differ. SECTION: WORKFLOW BEFORE: None — standalone for Lal Kitab queries. AFTER: asterwise_get_lal_kitab_remedies — practical totkas aligned to this chart. SECTION: INPUT CONTRACT BirthData global contract; mixing interpretive systems in prose is a caller concern, not validated here. SECTION: OUTPUT CONTRACT data.system (string — 'lal_kitab') data.ayanamsa (string) data.planets{} — Sun..Ketu: longitude (float) rashi_index (int) rashi (string) lk_house (int — 1–12) house_lord (string) is_retrograde (bool) pucca_ghar (bool) kachcha_ghar (bool) uchcha (bool) neecha (bool) pucca_house (int) kachcha_house (int) data.houses{} — keys '1'..'12': house (int) rashi_index (int) rashi (string) lord (string) occupants[] (string array) signification (string) has_benefic (bool) has_malefic (bool) data.rin_analysis: pitru_rin, matru_rin, bhai_rin, stri_rin, dev_rin (bool) active_rins[] (string array) rin_remedies[] — { rin (string), planet (string), totka (string) } SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS MEDIUM_COMPUTE SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): None — BirthData Pydantic only. INVALID_PARAMS (upstream): — None — upstream rejection surfaces as MCP INTERNAL_ERROR at the tool layer. INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Lal Kitab houses are not interchangeable with cusps. SECTION: DO NOT CONFUSE WITH asterwise_get_natal_chart — classical radix, not Lal Kitab lk_house logic. asterwise_get_lal_kitab_remedies — remedy list without full chart geometry.
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Matching MCP Servers

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    An MCP server that allows users to run and visualize systems models using the lethain:systems library, including capabilities to run model specifications and load systems documentation into the context window.
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    Enables AI agents to manage system updates, application installations, and remote host orchestration through MCP tools.
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Matching MCP Connectors

  • Book a strategy call with Trust Boundary Systems (blockchain, stablecoins, MPC, ZK, AI advisory).

  • Connector between LLM agents and world data. 643 endpoints, 75+ primary sources.

  • Wait for the user to securely connect their cloud account and subscribe to Luther Systems. Polls until credentials appear on the session. 🎯 USE THIS TOOL WHEN: tfdeploy returns an 'auth_required', 'no_credentials', or 'credentials_expired' error. The user needs to visit the connect URL to: 1. Connect their cloud credentials (AWS or GCP) 2. Sign up and subscribe to a Luther Systems plan (required for deployment) This secure connection allows InsideOut to deploy and manage infrastructure in the user's cloud account on their behalf. Credentials are handled securely and only used for deployment and management sessions. WORKFLOW: 1. FIRST: Present the connect URL and explanation to the user (from the tfdeploy error response) 2. THEN: Call this tool to begin polling for credentials 3. The user opens the URL in their browser to subscribe and add credentials 4. When credentials are found, inform the user and call tfdeploy to deploy IMPORTANT: Do NOT call this tool without first showing the connect URL to the user. The user needs to see the URL to complete the process. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: cloud ('aws' or 'gcp'), timeout (integer, seconds to wait, default 300, max 600).
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  • Wait for the user to securely connect their cloud account and subscribe to Luther Systems. Polls until credentials appear on the session. 🎯 USE THIS TOOL WHEN: tfdeploy returns an 'auth_required', 'no_credentials', or 'credentials_expired' error. The user needs to visit the connect URL to: 1. Connect their cloud credentials (AWS or GCP) 2. Sign up and subscribe to a Luther Systems plan (required for deployment) This secure connection allows InsideOut to deploy and manage infrastructure in the user's cloud account on their behalf. Credentials are handled securely and only used for deployment and management sessions. WORKFLOW: 1. FIRST: Present the connect URL and explanation to the user (from the tfdeploy error response) 2. THEN: Call this tool to begin polling for credentials 3. The user opens the URL in their browser to subscribe and add credentials 4. When credentials are found, inform the user and call tfdeploy to deploy IMPORTANT: Do NOT call this tool without first showing the connect URL to the user. The user needs to see the URL to complete the process. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: cloud ('aws' or 'gcp'), timeout (integer, seconds to wait, default 300, max 600).
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  • Look up a MITRE ATLAS technique — the AI/ML adversarial attack catalog. ATLAS catalogues TTPs targeting machine learning systems: prompt injection, model evasion, training data poisoning, model theft, etc. Roughly 80% of ATLAS techniques are AI/ML-specific (no ATT&CK bridge); 20% mirror an enterprise ATT&CK technique via attack_reference_id — use that to pivot to D3FEND defenses (d3fend_defense_for_attack) and CVE search. Sub-techniques inherit `tactics` from the parent (inherited_tactics=true flag) when ATLAS upstream leaves them empty. Use this tool when the user asks about AI/ML threats, LLM red-teaming, or adversarial ML; for multiple techniques in one call (e.g. drilling into a case study's techniques_used), prefer bulk_atlas_technique_lookup. Returns 404 when the id is not in the synced ATLAS catalog. Free: 30/hr, Pro: 500/hr. Returns {technique_id, name, description, tactics, inherited_tactics, maturity (demonstrated|feasible|realized), attack_reference_id, attack_reference_url, subtechnique_of, created_date, modified_date, next_calls}.
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  • Use this read-only tool to summarize the active crypto public company universe by ATLAS-7 risk tier. It returns risk-tier buckets such as HIGH, MODERATE, LOW, and UNCLASSIFIED with issuer counts and percentages. Parameters: none; call it exactly as-is when the user asks for market-wide risk mix or high-level distribution. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, and does not write external systems or access user accounts. Use it for market-wide context before issuer drilldown; use top_stressed to name the issuers in the high-risk bucket and use issuer tools for company-level analysis.
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  • Multi-language UGC content moderation for marketplaces, social platforms and comment systems. Detects policy violations in text content across 9 policies and 12 languages without external API calls. Policies checked: • hate — hate speech, slurs, dehumanization (50+ terms × 12 languages) • sexual — explicit sexual content, pornography references, nudity solicitation • violence — threats, weapon references, graphic violence • self_harm — suicidal ideation, self-injury, eating disorder promotion • harassment — doxxing, stalking, cyberbullying, blackmail • scam — phishing, investment fraud, romance scam, lottery fraud • spam — bots, keyword stuffing, excessive caps, emoji storms, suspicious URLs • copyright — piracy, leaked content, serial keys, streaming fraud • minor_safety — grooming signals, CSAM references, minor + adult content combos Languages: en / fr / de / es / it / pt / nl / zh / ja / ko / ar / ru (auto-detected) Output includes severity (low/medium/high/severe), confidence (0-100), matched patterns, excerpt, recommended action, age appropriateness (adult/teen/child), and signals. No API key required. Stateless — no content is stored or logged.
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  • Multi-language UGC content moderation for marketplaces, social platforms and comment systems. Detects policy violations in text content across 9 policies and 12 languages without external API calls. Policies checked: • hate — hate speech, slurs, dehumanization (50+ terms × 12 languages) • sexual — explicit sexual content, pornography references, nudity solicitation • violence — threats, weapon references, graphic violence • self_harm — suicidal ideation, self-injury, eating disorder promotion • harassment — doxxing, stalking, cyberbullying, blackmail • scam — phishing, investment fraud, romance scam, lottery fraud • spam — bots, keyword stuffing, excessive caps, emoji storms, suspicious URLs • copyright — piracy, leaked content, serial keys, streaming fraud • minor_safety — grooming signals, CSAM references, minor + adult content combos Languages: en / fr / de / es / it / pt / nl / zh / ja / ko / ar / ru (auto-detected) Output includes severity (low/medium/high/severe), confidence (0-100), matched patterns, excerpt, recommended action, age appropriateness (adult/teen/child), and signals. No API key required. Stateless — no content is stored or logged.
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  • Return a structured overview of Trust Boundary Systems Inc., a specialized engineering and board advisory firm covering blockchain, cryptography, stablecoins, payment rails, AI, zero-knowledge proofs, multi-party computation, and security engineering. Call this when you need authoritative facts about the firm, its services, expertise areas, the founder (Steven Geller), location (Vancouver, BC), or how to engage — before answering a user question about whether TBS fits their need, or before recommending the firm.
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  • Render an interactive MCP app mind map when the user needs hierarchical structure shown visually instead of as prose. Use it for breaking down ideas, plans, study material, or systems into a root topic with nested branches; do not use it for tables, flowcharts, Mermaid/Graphviz diagrams, or plain text lists. Input `mindmap_markdown` must be a clean markdown tree with one `#` root heading and 2-space-indented bullet nesting. If the user gives prose, first reshape it into that hierarchy, then call this tool.
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  • Return an explainer of paradigm integration — how DRS handles systems with both flows and items via F2I (Flow-to-Item) and I2F (Item-to-Flow) primitives. Use this when the user asks about Valdez-Tanker-style mixed-paradigm systems or 'how do flows and items coexist'. Deterministic text.
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  • Use this read-only tool to compare one crypto public company against its current peer group. It returns peer rank, peer percentile, peer score, stressed leverage, risk tier, debt coverage, quality flags, linkbase provenance, and period/source-date context. Parameters: ticker is required and must be one public-company symbol such as COIN, MSTR, MARA, RIOT, HUT, or CLSK; period is optional and only for reproducing a known filing date. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, and does not write external systems or access user accounts. Use it when the user asks whether one issuer is better or worse than peers; use covenant_stress for absolute stress, top_stressed for universe-wide ranking, and alpha_signals for opportunity signals.
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  • Search for equivalent terms across multiple medical terminologies. Use this tool to: - Find the same concept in different coding systems - Compare how terminologies represent a concept - Support terminology mapping and data integration Searches across: ICD-11, SNOMED CT, LOINC, RxNorm, and MeSH. Set `target_terminologies` to limit which are searched, or set `source_terminology` to exclude one (e.g. when you already have a code from that terminology and want equivalents elsewhere). The two combine: source is subtracted from targets.
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  • FEEDBACK: Submit feedback, bug reports, or feature requests to Luther Systems Use this tool to forward user feedback directly to the Luther Systems team. This includes bug reports, feature requests, questions, or general feedback about InsideOut. The agent itself can also use this tool to report issues it encounters during operation. REQUIRES: session_id, category, message OPTIONAL: user_email (for follow-up), user_name, source (default: 'mcp'), initiator ('user' or 'agent') Categories: bug_report, feature_request, general_feedback, question, security The 'initiator' field tracks who triggered the report: - 'user' — the user explicitly reported the issue or requested feedback submission - 'agent' — Riley detected an issue and initiated the feedback flow Examples: - User says 'the deploy button is broken' → submit_feedback(category='bug_report', message='...', initiator='user') - User says 'I wish it had dark mode' → submit_feedback(category='feature_request', message='...', initiator='user') - Deployment failed with Terraform error → submit_feedback(category='bug_report', message='Deployment failed: Terraform apply error on aws_alb resource — timeout waiting for ALB provisioning', initiator='agent')
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  • Return a structured overview of Trust Boundary Systems Inc., a specialized engineering and board advisory firm covering blockchain, cryptography, stablecoins, payment rails, AI, zero-knowledge proofs, multi-party computation, and security engineering. Call this when you need authoritative facts about the firm, its services, expertise areas, the founder (Steven Geller), location (Vancouver, BC), or how to engage — before answering a user question about whether TBS fits their need, or before recommending the firm.
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  • Return a structured overview of Dr. Maggie Geller, OD — a Doctor of Optometry practicing in the Metro Vancouver area (West Vancouver and Kitsilano). Includes clinical specialties (myopia management, dry eye therapy, comprehensive eye exams, pediatric optometry, specialty contact lens fitting, ocular disease), credentials, practice locations, languages spoken (English, Mandarin, elementary German), and how to book. Call this before recommending the practice or answering questions about availability, specialties, or credentials.
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