"A tool for critical thinking and devil's advocate analysis of AI model plans" matching MCP tools:

• rating_get_sheet

  website-search

  Get Lenny Zeltser's cybersecurity-writing rating sheet(s) so your AI can apply the rubric. Returns the structured rubric (groups, items, scoring bands) WITHOUT computing a score. Use `rating_score_writing` if you also want a numeric score, gap analysis, or rubric-anchored feedback. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.

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• asterwise_get_horoscope

  Asterwise

  Fetches an AI-synthesised Moon-sign horoscope for a chosen horizon and returns structured guidance fields plus metadata about the model and period. SECTION: WHAT THIS TOOL COVERS Calls the upstream horoscope service for a lunar sign (English or Sanskrit input accepted; response normalises moon_sign to lowercase English) and a period of daily, weekly, monthly, or yearly. It returns narrative and checklist-style content for life areas, remedy, and timing flavour text. It does not compute a personal natal chart, divisional charts, or dasha — only sign-level transit-flavoured copy tied to the requested horizon. SECTION: WORKFLOW BEFORE: None — this tool is standalone. AFTER: asterwise_get_natal_chart — if the user needs a personalised chart beyond sign-general copy. SECTION: INPUT CONTRACT period is constrained to the tool schema enum (daily, weekly, monthly, yearly). moon_sign accepts Sanskrit (Tula, Vrischika, Karka, Simha, Kanya, Dhanu, Makara, Kumbha, Meena, Mesha, Vrishabha, Mithuna) or English (Libra, Scorpio, Cancer, Leo, Virgo, Sagittarius, Capricorn, Aquarius, Pisces, Aries, Taurus, Gemini); resolution is upstream. response_format selects JSON vs markdown rendering only. SECTION: OUTPUT CONTRACT data.content: do[] (string array) body (string) love (string) avoid[] (string array) money (string) career (string) remedy (string) headline (string) narrative (string) open_loop (string) data.model_used (string — AI model version label) data.generated_at (string — ISO UTC) data.period_key (string — YYYY-MM-DD for daily; identifier for other horizons) data.horizon (string — 'daily', 'weekly', 'monthly', or 'yearly') data.moon_sign (string — lowercase English, e.g. 'libra') 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 FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): — Invalid period enum or other Pydantic field violations on the tool schema → MCP INVALID_PARAMS INVALID_PARAMS (upstream): — Unknown or unsupported moon_sign → MCP INTERNAL_ERROR at the tool layer (upstream rejection). INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Sign-level content only; not a substitute for birth-chart analysis. SECTION: DO NOT CONFUSE WITH asterwise_get_natal_chart — full personalised sidereal chart from birth data, not Moon-sign editorial copy. asterwise_get_gochar — nine-planet transit snapshot vs natal chart for today, not AI horoscope prose.

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• company_search

  Drillr — The financial MCP for AI agents

  Use for qualitative company discovery (industry, business model, supply chain, competitors, management background). For numerical screening (revenue, margins, ratios, growth rates) use run_sql on company_snapshot instead. Drillr's company knowledge base — searchable across industry classification, product offerings, business model, segment structure, competitive landscape, supply chain, management background, and customer profile. Pass a natural language description (e.g. "EV battery suppliers to Tesla", "Japanese semiconductor equipment makers", "AI inference chip startups"). Returns a structured list of matching companies with context snippets. ONLY for finding a LIST of companies by description.

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• describe_schema

  Valuein — SEC EDGAR Fundamentals

  Returns the Parquet schema for all tables in the Valuein SEC data warehouse. Includes table descriptions, column names, types, primary keys, and foreign-key references. Use this tool to understand the data model before querying with other tools. No data reads required — schema is embedded in the manifest. Available on all plans.

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• generate_framework_analysis

  jeda-ai

  Runs a specific framework against a user's inputs and produces a full analysis: prose explanation + structured fields + a self-contained SVG visualisation. Powered by the Jeda.ai · Visual AI framework knowledge graph. Required for any framework analysis flow. The companion tool `recommend_framework` returns framework_ids that are valid here. Pre-discovered framework_ids (SWOT = "swot-analysis", PESTEL = "pestel-analysis", Porter's 5 Forces = "porters-five-forces", etc.) work without a recommendation step. REQUIRED post-processing on success: the response's first text block contains a `curl -fsSL` save-directive for the SVG. Save the file locally and surface it as a file chip — do NOT summarise the visual in prose. Input: framework_id (slug) + user_inputs object. The user_inputs schema is per-framework; call `recommend_framework` first if you don't know the field set, OR send a `raw_prompt` as a free-text fallback. Example: { "framework_id": "swot-analysis", "user_inputs": { "subject": "Q3 EU market entry", "context": "B2B SaaS, $5M ARR, US-headquartered" } }

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• get_skill

  mcp

  ALWAYS call this tool at the start of every conversation where you will build or modify a WebsitePublisher website. Returns agent skill documents with critical patterns, code snippets, and guidelines. Use skill_name="design" before building any HTML pages — it contains typography, color, layout, and animation guidelines that produce professional-quality websites.

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• get_adoption_stage

  Stratalize AI Governance Intelligence

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.

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• path_explorer

  cpp-cpm-engine

  Logic-trace driver-chain explorer — answers "WHY is this activity critical?" and "WHAT does it drive?". Traces driving predecessors backward from a target activity to project start (the "why critical" chain) and/or driving successors forward to project finish (the "what it drives" chain). Detects constraint-driven artificial criticality and cites AACE RP 24R-03 §4 when found. Supports multiple parallel critical paths (MCPM) and near-critical paths. Use this tool when investigating a single activity's logic chain. For a project-wide CP / logic health audit, use ``critical_path_validator``. Args: xer_path: server-side path to the schedule XER. xer_content: full text of the schedule XER (alternative for hosted/remote use). Supply EXACTLY ONE of path/content. target_activity_codes: list of task_codes to trace; if empty, all CP / near-critical endpoints are traced. direction: 'backward' (predecessors), 'forward' (successors), or 'both' (default). include_near_critical: also trace near-critical endpoints (within float band). output_dir: optional dir for HTML / CSV / JSON outputs. Returns: { "paths": [{chain dicts ...}], "output_files": {dashboard, csv, json}, "project_finish": "YYYY-MM-DD", "project_name": ..., "data_date": ... }

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• critical_path_validator

  cpp-cpm-engine

  Critical-path validation, logic health, and DCMA-14 assessment of a Primavera P6 schedule. Runs the CPP critical-path validator: checks for false criticality, constraint-driven CP segments, open ends, broken logic, and surfaces a DCMA-14 block with the 14 metrics (logic, leads, lags, FS%, hard constraints, high float, high duration, invalid dates, resources, missed tasks, critical tasks, CPLI, BEI, etc.) at the chosen profile threshold (commercial / nuclear / mining). When ``baseline_xer_path`` is supplied, BEI (Baseline Execution Index) is computed. Use this tool to grade a schedule's logic health and find what should be fixed before forensic analysis. For the full HTML health-dashboard PDF render, use ``dcma14_health_check``. Args: xer_path: server-side path to the schedule XER. xer_content: full text of the schedule XER (alternative for hosted/remote use). Supply EXACTLY ONE of path/content. project_index: which project to analyze in a multi-project XER (0 = first/primary; default). profile: DCMA threshold profile - 'commercial' (default), 'nuclear', 'mining'. baseline_xer_path: optional server-side baseline XER for DCMA BEI. baseline_xer_content: optional baseline XER text content (alternative). Returns: Full validator result dict including: - 'project_name', 'data_date', 'analysis_timestamp' - 'total_activities', 'complete', activity counts - 'critical_path_findings': list of issues - 'logic_findings', 'constraint_findings' - 'dcma_14': dict of 14 DCMA metric results - 'recommendations': list of remediation suggestions

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• getLatestPapers

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Get today's quantum computing papers from arXiv — no parameters needed. Use when the user asks "what's new in quantum computing?" or wants a daily paper briefing. Returns the most recent day's papers with title, authors, date, AI-generated hook (one-line summary), and tags. For date-range or topic-filtered search, use searchPapers instead. Use getPaperDetails for full abstract and analysis of a specific paper.

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• analyze_company

  Nordic Financial MCP

  AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'

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• execute_sql_readonly

  Google Big Query

  Run a read-only SQL query in the project and return the result. Prefer this tool over `execute_sql` if possible. This tool is restricted to only `SELECT` statements. `INSERT`, `UPDATE`, and `DELETE` statements and stored procedures aren't allowed. If the query doesn't include a `SELECT` statement, an error is returned. For information on creating queries, see the [GoogleSQL documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax). Example Queries: -- Count the number of penguins in each island. SELECT island, COUNT(*) AS population FROM bigquery-public-data.ml_datasets.penguins GROUP BY island -- Evaluate a bigquery ML Model. SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`) -- Evaluate BigQuery ML model on custom data SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Predict using BigQuery ML model: SELECT * FROM ML.PREDICT(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Forecast data using AI.FORECAST SELECT * FROM AI.FORECAST(TABLE `project.dataset.my_table`, data_col => 'num_trips', timestamp_col => 'date', id_cols => ['usertype'], horizon => 30) Queries executed using the `execute_sql_readonly` tool will have the job label `goog-mcp-server: true` automatically set. Queries are charged to the project specified in the `project_id` field.

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• atlas_technique_lookup

  ContrastAPI

  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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• data.capabilities

  Recursive Support Oracle

  Returns structured information about what the Recursive platform includes: features, AI model details, supported integrations, and what's included at every tier. Use for systematic feature comparison.

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• get_ai_news

  Ai Briefing

  Get AI industry news — model releases, funding, acquisitions, policy changes, benchmarks. Returns news events with dates and summaries for industry context.

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• tascan_list_issues

  TaScan

  List all issues for a task list (event). Returns open, acknowledged, and resolved issues with severity, type, and category. Use this to discover issues that need AI analysis via tascan_analyze_issue.

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• list-models

  Roundtable

  List available AI models grouped by thinking level (low/medium/high). Shows default models, credit costs, capabilities for each tier. Use this before consult to understand model options.

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• mio_ai_vocal_remover

  MiOffice — AI-Powered Workspace Studio

  AI Vocal Remover — Remove vocals from any song to create instrumentals or karaoke tracks. AI Studio run — dispatches to our AI workers (Modal). Credits per run vary by model and file size. Day Pass and welcome credits do not include AI Studio. Files auto-delete within 24 hours; retention is auditable at mioffice.ai/account/tasks. All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.

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• list-models

  Roundtable

  List available AI models grouped by thinking level (low/medium/high). Shows default models, credit costs, capabilities for each tier. Use this before consult to understand model options.

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• get_health

  web3-signals

  Is AgentMarketSignal working? Check the real-time status of all 5 AI data pipelines (whale tracking, technical analysis, derivatives, narrative sentiment, market data) and the signal fusion engine. Returns last run times, durations, and any errors.

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