"Utilizing Local LLMs for Query Preprocessing with Minions Framework" matching MCP tools:

• discovery_estimateA

  DiscoA

  Estimate the credits required to run a Disco analysis. Returns `required_credits` for public (always 0) and private, with private split by whether LLMs are enabled (use_llms=False is faster, use_llms=True adds smarter preprocessing, literature context and a written summary). Also returns per-visibility depth caps and accepted file formats. No authentication required — when an API key is supplied, also returns the caller's available credits. Call this before discovery_analyze whenever cost or feasibility is unclear. Args: file_size_mb: Size of the dataset in megabytes. num_columns: Number of columns in the dataset. analysis_depth: Search depth (1=fast, higher=deeper). Used to compute the private-run cost. Default 2. api_key: Disco API key (disco_...). Optional. When provided, the response includes `account.available_credits`.

  ConnectorNo auth

• generate_framework_analysisA

  jeda-aiA

  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. ALWAYS call `recommend_framework` FIRST to obtain a valid framework_id -- it returns the exact namespaced slug to pass here. Framework ids are namespaced by category (e.g. SWOT = "cms_swot-analysis", Porter's 5 Forces = "cms_porters-five-forces"); a bare slug like "swot-analysis" will 404. 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 (namespaced slug from `recommend_framework`) + user_inputs object (per-framework field set), OR a free-text `raw_prompt` fallback. Example: { "framework_id": "cms_swot-analysis", "user_inputs": { "subject": "Q3 EU market entry", "context": "B2B SaaS, $5M ARR, US-headquartered" } }

  ConnectorNo auth

• list_investment_thesesA

  crashtestyourstrategyA

  Discover the investment-thesis catalog. Each entry is a descriptive case study that pairs an economic framework with a rule-based portfolio and the synthetic + historical stress evidence for that allocation. Returns one compact summary per thesis (slug, title, one-liner, tags, risk tiers, framework summary, headline finding). Call get_investment_thesis(slug) for the full framework / portfolio / stress evidence, or read the thesis://{slug} resource. Descriptive, not advisory — the agent decides what is suitable.

  ConnectorNo auth

• submit_ai_governance_profileA

  australian-ai-governance-frameworkA

  Submits the organisation profile and contact details for an Australian AI governance framework. The profile determines which legislation the framework identifies, so the answers should reflect the organisation's actual circumstances — turnover in particular, since the Privacy Act's small business threshold sits at $3 million and several categories are caught regardless of turnover. Takes the session ID from start_australian_ai_governance_framework together with the questionnaire answers. Writes the profile against the session and stores the supplied name, email and organisation as a contact record. contact.organisation is printed as the document's "Prepared for" heading, so it should be the organisation's name as it should appear on the document rather than a shorthand. Returns the session ID and a status of profile_saved — it does not return the framework, which is retrieved by get_ai_governance_framework. The profile can be re-submitted on the same session: it is overwritten rather than duplicated, the contact record is keyed on the email address, and any framework already generated for that session is discarded. No authentication, and no charge at this step.

  Connector

  Destructive

  No auth

• get_standardsA

  TsehaA

  Returns the organization's development standards: coding conventions, project structure, and framework-specific rules. Read-only. Call it before writing or reviewing code, so the result follows this organization's rules rather than general defaults. Call it first without a section to get an index of available sections, each with a note on what it covers, then call again with one section id copied from that index; inventing a section id returns a not-found error naming that step. Request only the sections a task needs - the full content of one section can be long. The framework argument is deprecated: use section with the "framework:" prefix instead. It returns prose rules, not data - use get_style_tokens for visual values and get_component for component APIs.

  ConnectorNo auth

• recommend_frameworkA

  jeda-aiA

  Recommends business / strategy / risk frameworks for a stated problem. Powered by the Jeda.ai · Visual AI framework knowledge graph (~2,100 frameworks across 19 categories, edge-curated). Use when the user describes a business problem ("customer churn rising", "evaluating market entry", "need to assess vendor risk") rather than naming a specific framework. Returns top-N frameworks ranked by fit, each with a concrete reason citing the specific problem signals matched. Input: just the problem statement is enough. Optional faceted filters (`persona`, `regulation`, `decision_stage`) narrow the candidate set. Set `limit` between 3 and 10 for picker UIs. Pair with `generate_framework_analysis` to actually run a recommended framework against the user's inputs. Example: { "problem_statement": "We need to decide whether to enter the EU SMB market in Q3", "decision_stage": "decide", "limit": 5 }

  ConnectorNo auth

• search_case_by_signatureA

  Nalegalu MCPA

  Verify a cited case signature (sygnatura), e.g. "II CSK 448/14". Use before relying on a judgment cited by a user or another agent. Answers from a local index of all SAOS judgments first; only on a local miss does it query live SAOS. Read the verdict field: "confirmed" (matches listed with saos_id, court, date, type — different courts reuse signatures, so check the court), "not_found" (strong evidence the citation is fabricated or wrong), "outside_coverage" (administrative courts NSA/WSA are not in SAOS — verify in CBOSA instead), "unverified" (local miss and SAOS unreachable; do not treat as nonexistent). Optional court_type: COMMON, SUPREME, CONSTITUTIONAL_TRIBUNAL, NATIONAL_APPEAL_CHAMBER. has_text=false on a match means SAOS stores no text for it, so verify_quote cannot check quotes against that judgment.

  ConnectorNo auth

• get_workload_logsA

  Control PlaneA

  Query workload logs from a GVC. Provide structured params (gvc, workload, container, location, filter) OR a raw LogQL `query` — a raw query REPLACES the structured params, so it must embed ALL labels itself. Available labels: gvc, workload, container, location, provider, replica, stream — replica and stream are only reachable via a raw query. `filter` is a literal substring match (|=), not regex; for regex use a raw query with |~. Cron workload? Get jobExecutions via list_deployments (with `location`), then re-query with a raw query scoping replica= plus the execution's time window — embed gvc/workload/location labels in the raw query. Returns structured JSON with timestamps, messages, and labels. Recommended reading before first use: get_cpln_skill("workload-troubleshooting") — the runbook for this tool family (read once per session).

  ConnectorOAuth

• get_discoveryA

  ikeytz – Schlüsseldienst LudwigsburgA

  WHAT: Fetch one public discovery file from www and return a line window. REQUIRED which. IDs (aliases folded): llms, llms-full, llms-index, llms-keywords, llms-serp, llms-impressum-kontakt, llms-orte-geo, llms-urheberrecht, llms-copyright, llms-mcp-server, llms-mcp-web, robots, sitemap-txt, sitemap-xml, ai-txt, ai-plugin, answer-engine, ard, ai-catalog, auth-md, mcp-readme, agent-skills. summary = the line window (this is the file body). Use offset/limit + nextOffset to page. Byte caps apply (keywords huge). Unknown which → unknown_discovery. Prefer dedicated get_llms_txt / get_sitemap_txt / get_llms_mcp_server when you know the file. Policy files say ai-train=no.

  ConnectorNo auth

• axint.registry.searchA

  AxintA

  Search the Axint Registry for already-published packages that match a natural-language query. Use this BEFORE calling axint.feature or axint.compile so the agent can install an existing package instead of regenerating Swift the community has already shipped. Use: use before generating code to find reusable packages; not for validating local Swift. Inputs: query drives ranking; kind and platform narrow results without changing the registry source. Effects: read-only local registry search using AXINT_REGISTRY_PATH or sibling checkout; no network by default.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize GovernanceA

  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.

  ConnectorNo auth

• searchA

  Melvea Local Honey DiscoveryA

  Search the Melvea local honey directory by free-text query and return matching producers as a list of results (id, title, url). Designed for ChatGPT Deep Research and Company Knowledge. Use for any local-honey discovery query that names or implies a place; the tool parses place and varietal from the query. Returns an honest empty list when nothing matches — never fabricate. Pair with fetch to retrieve full producer detail.

  ConnectorNo auth

• get_interpretationA

  corpusA

  Return the Wheel of Heaven interpretive framework's reading of a topic — explicitly the project's own Raëlian-canon-centred position, NOT mainstream consensus. Accepts a framework topic (overview, hypothesis, terminology, timeline, sources, method) for the curated narrative documents, or any other term to get the framework reading from the closest wiki entry. Use fact-layer tools (get_passage, compare_traditions) for source-grounded data without this framing.

  ConnectorNo auth

• get_data_source_schemaA

  clariBI.comA

  Fetch the column schema for a data source. Useful before asking run_analysis about specific columns. The schema is derived from the preprocessing metadata clariBI extracted when the source was last synced. Poll this after upload_data_source / ingest_url_data_source until the returned status flips to "active" — that means preprocessing has finished and run_analysis will see the data.

  ConnectorNo auth

• upload_data_sourceA

  clariBI.comA

  Create a new data source from an inline base64-encoded file (CSV, TSV, JSON, Excel, TXT, PDF). The file goes through the same validation and preprocessing as a web upload. Returns the data_source_id you can pass to run_analysis as soon as preprocessing completes (poll get_data_source_schema for readiness or pass wait_seconds to block here).

  ConnectorNo auth

• batch_searchA

  AnySearchA

  This is Anysearch's parallel search tool. Parallel search — run multiple Anysearch queries in a single call. Prefer this over multiple sequential calls when you have 2–5 queries. Saves context space and returns all results at once. Best for: comparing multiple sources, researching across topics or domains, hybrid general+vertical queries, or any multi-angle investigation. ## When to use Use batch_search instead of multiple sequential search calls when you have 2–5 independent queries. 🏆 PRIMARY use case: After get_sub_domains(domains=[...]) returns sub_domains across multiple domains, use batch_search to send one query per sub_domain in parallel. This is more efficient than sequential per-domain search calls. Also useful for ambiguous / fuzzy queries within a single domain: after get_sub_domains, use batch_search to explore multiple sub_domains in parallel. ## Constraints - Maximum 5 queries per call - Each query item follows the search tool parameter structure (query is required; domain, sub_domain, sub_domain_params are optional. For general queries, omit all domain fields. For vertical queries, domain + sub_domain + sub_domain_params MUST come from get_sub_domains(domain=<domain>) output — same rules as the search tool) - Queries run in parallel; a single query failure does not block others - REQUIRED PARAMS: Same rule as search — when a required param from get_sub_domains is not applicable, pass it as an empty string (key: ""). Never skip required params. ## Examples ### Single-domain batch (multiple sub_domains) Instead of: search(query="latest TSLA earnings", domain="finance", sub_domain="finance.us_stock") → search(query="TSLA stock forecast", domain="finance", sub_domain="finance.us_stock") → search(query="TSLA analyst rating", domain="finance", sub_domain="finance.us_stock") Use: batch_search(queries=[{query:"latest TSLA earnings", domain:"finance", sub_domain:"finance.us_stock"}, {query:"TSLA stock forecast", domain:"finance", sub_domain:"finance.us_stock"}, {query:"TSLA analyst rating", domain:"finance", sub_domain:"finance.us_stock"}]) ### Multi-domain batch (after get_sub_domains with multiple domains) After: get_sub_domains(domains=["finance", "health", "legal"]) Use: batch_search(queries=[ {query:"AI regulation impact on healthcare stocks 2025", domain:"finance", sub_domain:"finance.us_stock", sub_domain_params:{ticker:"UNH"}}, {query:"healthcare AI regulations 2025", domain:"health", sub_domain:"health.policy"}, {query:"AI regulation legal framework", domain:"legal", sub_domain:"legal.legislation"}]) ### Hybrid: general + vertical in parallel (universal pattern for any borderline query) Use this whenever you are unsure if the query is pure encyclopedia or domain-specific — fire BOTH channels in batch_search: batch_search(queries=[ {query:"..."}, // general — no domain {query:"...", domain:"...", sub_domain:"..."}]) // vertical channel(s) This applies universally: classical texts, financial concepts, legal theories, historical events, scientific discoveries, medical topics — any query where domain knowledge could enrich the encyclopedia answer.

  Connector

  Destructive

  No auth

• aidefense_locate_conceptA

  website-searchA

  Reverse-lookup a single concept ID (MITRE ATLAS technique like 'AML.T0051', OWASP LLM Top 10 risk like 'LLM01', OWASP Agentic Top 10 issue like 'ASI03', or ISO 42001 Annex A clause like 'A.6') across the AI Defense Matrix. Returns which framework the concept belongs to, the asset rows whose alignment cites it, the cells whose evaluation cellPrompts cite it, and those prompts themselves. Useful when a vendor's product is defined by a specific technique ('we defend AML.T0051') and they need to find which matrix cells to claim. Recognizes only concepts with structured IDs; for prose-only frameworks (NIST IR 8596, CSA AICM, Google SAIF, OWASP AI Exchange) use aidefense_get_framework_alignment instead. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.

  ConnectorNo auth

• nausika_geocodeA

  NausikaA

  Resolve a place 'query' to coordinates (forward) or find nearest places to 'latitude'+'longitude' (reverse). Mediterranean-focused curated DB; forward falls back to OSM/Nominatim globally. Each result carries a 'source' discriminator ('local' for the curated marine DB, 'osm' for the global fallback). Returns name, type, coords, source, plus similarity (forward) or distance_m (reverse). Example forward: query="Portofino". Example reverse: latitude=44.3, longitude=9.21, radius_m=50000. Chain into nausika_marine_forecast, nausika_tides, nausika_search_places, or nausika_sea_route using the returned coords.

  ConnectorNo auth

• ourairports_get_airportA

  ourairports-mcp-serverA

  Fetch the full record for one airport resolved by ANY code — IATA (SEA), ICAO (KSEA), GPS, national/local, or the OurAirports ident — with its runways and radio frequencies inline. The single `code` param is resolved case-insensitively across all five identifier spaces (priority: ident, then ICAO, IATA, GPS, local). The response always echoes the airport's complete code set and a resolution_note naming which space matched, so a wrong resolution from an ambiguous national code is self-correcting (re-query with the IATA or ICAO code, or the ident). Absent codes are reported as null, never an error. Closed airports always resolve. OurAirports is community-edited — not authoritative for flight operations.

  ConnectorNo auth

• list_suitesA

  saferagenticai-mcpA

  List all 16 suites in the SaferAgenticAI framework (9 drivers + 7 inhibitors) with subgoal counts and titles. Call this first to orient.

  ConnectorNo auth

• colorize_imageA

  Sats4AI - Bitcoin-Powered AI ToolsA

  Colorize black-and-white or grayscale photos. DDColor (dual-decoder, ICCV 2023) — vivid, natural colorization. Impossible for text/vision LLMs. 5 sats per image, pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='colorize_image'.

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