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261,468 tools. Last updated 2026-07-05 12:39

"Tools and Management for General Testing and Prompts" matching MCP tools:

  • Extract typed fields from document text using a caller-defined schema. Uses a quality AI model with retry logic. Use when you need specific data points from a document rather than full text. For invoices with known fields, parse_invoice (prebuilt schema) may be simpler. For general summarization, use summarize_document instead. Schema format: { "field_name": "type hint or description" } — e.g. { "contract_date": "ISO date", "party_a": "string", "penalty_usd": "number" }. Returns: { data: { <field>: value }, data_cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Extract the contract date, parties, and penalty amount from this agreement." - "Pull the vendor name, PO number, and total from this document." - "Get me all named fields from this form using my custom schema."
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  • Return the full case for a given pair_id (axes 1/2/4) or id (axis 3): malign and benign task prompts, expected decisions, grounding rationale, and bypass patterns. Axis 3 cases are single (unmatched) and use an 'id' field instead of 'pair_id'. Use list_cases to browse available ids.
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  • Returns the universal context-setting primer for Hemrock models, plus an optional template-specific addendum. Always run this first before any other prompts.
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  • Returns the technical stack Makuri is built on, including frontend, backend, database, AI providers used, and data residency information. Use when the user asks how Makuri is built or which AI models it uses. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools.
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  • Browse Comic Vine's comic-book creator directory (writers, artists, inkers, letterers, colorists). Filter by name; paginate with limit/offset. NOT a general biography search — for actors use TMDb, for general bios use Wikipedia.
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  • Full-text book search across Open Library works. Supports field filters (title, author, subject, publisher, ISBN, language) and returns work-level records with edition counts, cover IDs, and reading availability. Use query for general search or combine specific field filters. Results are work-level — drill into editions via openlibrary_get_editions.
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  • Generate realistic mock data from a JSON Schema. Supports all common types (string, number, integer, boolean, array, object, null), format hints (email, date, date-time, uri, uuid), enum, const, and nested schemas. Perfect for testing MCP tools with realistic data.
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  • Search CJEU and General Court case law — judgments, orders, and Advocate General opinions — by case number, court, case type, keyword, and date range. By default only these primary records are returned; derivative judicial information notices, case abstracts, and summaries are excluded so distinct cases fill the page (set include_derivative to include them). Keyword matches English case titles (which carry party names) and CELEX strings; there is no full-text body search. Returns each case with its court, date, and type, plus — parsed from the title where present — the parties, subject matter, and case reference.
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  • Extract typed fields from document text using a caller-defined schema. Uses a quality AI model with retry logic. Use when you need specific data points from a document rather than full text. For invoices with known fields, parse_invoice (prebuilt schema) may be simpler. For general summarization, use summarize_document instead. Schema format: { "field_name": "type hint or description" } — e.g. { "contract_date": "ISO date", "party_a": "string", "penalty_usd": "number" }. Returns: { data: { <field>: value }, data_cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Extract the contract date, parties, and penalty amount from this agreement." - "Pull the vendor name, PO number, and total from this document." - "Get me all named fields from this form using my custom schema."
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  • Returns a plain-English usage guide for this server — example requests, what it asks the user for, and the available tools. Call this if the user asks how to use Abby SEO, or to orient yourself before starting. (Same content as the 'getting_started' prompt, exposed as a tool for clients that don't surface MCP prompts.) Takes no arguments.
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  • Get the coding conventions Moxie inferred for the repository. Read-only; no side effects. Returns a Markdown list grouped by category (e.g. testing, structure, docs, review); each convention has a title, summary, confidence score, agent guidance, and the source file paths that evidence it. Use this for the general rules to follow; when you already know the files you're about to edit, prefer moxie.get_doc_impact for conventions scoped to those paths.
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  • Return a short, human-readable walkthrough for testing this server: the endpoint, the tool/prompt/resource names, and ready-to-paste sample prompts. Use to give someone a guided demo. For the full machine-readable capability catalog, use list_capabilities instead.
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  • Loads one supported self-assessment into the widget by slug. Use `gad7` for anxiety screening, `phq9` for depression screening, and `who5` for general well-being screening when the user wants to take one of those assessments.
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  • Returns Makuri's regulatory posture across EU AI Act, GDPR, GDPR-K (children data), COPPA, and ISO 42001 — as design intentions and operator self-assessment, NOT certified or audited compliance. No formal audit or conformity assessment has been performed. Statuses are design_aligned_unaudited, not_started, or not_applicable; there is deliberately no 'compliant' status. Use when the user asks about regulatory compliance, AI Act classification, or data protection for children — and present results as posture, not certification. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools.
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  • Get the AI Defense Matrix evaluation playbook for assessing an AI security program: per-cell prompts, gap-inventory template, and a workflow that walks each asset class first and rolls findings up to the Govern column. Supports mode='gate' for binary deployment-gate decisions (returns the deployment-gate workflow plus gate-tier prompts only) and consumerPattern for scoping to consumed-vs-built AI deployments. The AI applies these prompts against your program documentation locally, and no program details leave your client. 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.
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  • Parse a receipt or invoice document into structured fields. Uses a quality AI model for accuracy. Use when you need to extract line items, totals, and merchant info from financial documents. For general document text, use extract_text instead. Returns: { invoice: { merchant, date (YYYY-MM-DD), line_items[], subtotal, tax, total }, cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Parse this invoice and give me the line items and total." - "Extract the merchant, date, and amounts from this receipt." - "Read this scanned invoice and return structured data."
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  • List pages in Redpanda API reference documentation. Returns endpoints, schemas, and topic pages with URL, title, type, and description. SCOPING (important for accurate results): - api="all" or omit: Lists all available APIs - api="admin": Cluster management operations (brokers, partitions, configs, users) - api="cloud-controlplane": Redpanda Cloud resource management (clusters, networks, namespaces) - api="cloud-dataplane": Cloud cluster data operations (topics, ACLs, connectors) - api="http-proxy": Kafka operations over HTTP (produce, consume, offsets) - api="schema-registry": Schema management (register, retrieve, compatibility) Use this to browse API structure. For general Redpanda docs, use ask_redpanda_question instead.
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  • Search Redpanda API reference documentation by keyword. Returns up to 20 matching endpoints, schemas, or topics with URL, title, and text excerpts. SCOPING (important for accurate results): - api="all" or omit: Search across ALL APIs at once - useful when unsure which API contains the endpoint - api="admin": Search only cluster management (brokers, partitions, configs, users, maintenance) - api="cloud-controlplane": Search only Cloud resource management (clusters, networks, namespaces) - api="cloud-dataplane": Search only Cloud data operations (topics, ACLs, connectors) - api="http-proxy": Search only HTTP Proxy (produce, consume, offsets over HTTP) - api="schema-registry": Search only Schema Registry (register, retrieve, compatibility) WHEN TO USE WHICH: - User asks "broker endpoints" → api="admin" (brokers are cluster management) - User asks "create topic API" → api="all" (topics exist in admin AND cloud-dataplane) - User asks "Cloud cluster API" → api="cloud-controlplane" - User asks about Redpanda APIs generally → api="all" or omit For general Redpanda questions (not API-specific), use ask_redpanda_question instead.
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  • Get current ads scheduled for a device (for testing). WHEN TO USE: - Testing device ad delivery - Debugging which ads are being shown - Verifying ad targeting is working RETURNS: - ads: Array of advertisement objects - default_stream: Default content when no ads - schedule: Current ad schedule EXAMPLE: User: "What ads are showing on device P_abc123?" get_device_ads({ fingerprint: "P_abc123" })
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  • Send a message to Atlas Advisor for lightweight hiring advice (2 credits). Faster and cheaper than atlas_chat, no tool use -- best for general hiring questions. Returns AI response text and a conversation_id. Omit conversation_id to start a new conversation; include it to continue a thread.
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