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534,220 tools. Updated 2026-09-08 13:12

"Finding solutions for computational mathematics or engineering problems" matching MCP tools:

  • Search Stack Overflow Q&A platform for programming questions, solutions, and code examples. Returns matching questions, answer count, view count, accepted answer snippet, tags, and link to full discussion. Use for troubleshooting, code examples, or finding solutions to common problems.
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  • Search Stack Overflow Q&A platform for programming questions, solutions, and code examples. Returns matching questions, answer count, view count, accepted answer snippet, tags, and link to full discussion. Use for troubleshooting, code examples, or finding solutions to common problems.
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  • Find which guidance serves a FinOps question - how to commit, size, allocate, charge back, forecast, or govern cloud and AI spend. Use this for questions like "how should we size Savings Plans", "what should Finance own in chargeback", "what does a Crawl-stage org tackle first" - anything that maps to FinOps Framework facets (domain, capability, phase, persona, maturity) - and you want only the references that serve it, instead of scanning the full list. All filters are optional and combine with AND semantics. String matching is case-insensitive and exact (not substring). Examples: - ``find_references(domain="Optimize Usage & Cost")`` - ``find_references(phase="Optimize", persona="Engineering")`` - ``find_references(persona="Engineering", persona_primary_only=True)`` - ``find_references(capability="Rate Optimization")`` - ``find_references(maturity="Crawl")`` Args: domain: FinOps Framework domain (e.g. ``"Optimize Usage & Cost"``, ``"Quantify Business Value"``, ``"Manage the FinOps Practice"``). capability: FinOps capability (matches ``fcp_capability`` and ``fcp_capabilities_secondary``). phase: FinOps phase (``"Inform"``, ``"Optimize"``, ``"Operate"``). persona: Persona (matches ``fcp_personas_primary`` and ``fcp_personas_collaborating``). maturity: Entry maturity level (``"Crawl"``, ``"Walk"``, ``"Run"``). persona_primary_only: when True, ``persona`` matches only the primary list. Use it when the default match barely narrows the set - broad personas like Engineering collaborate on nearly every file, so filtering on collaboration is descriptive, not discriminating. ``persona="Engineering", persona_primary_only=True`` is the engineering reading list; the default is the everything-they-touch view. Returns ``{"filters": {...}, "references": [...], "total": N}``. A query that matches nothing also returns `hint` and `valid_values`, so a typo is distinguishable from a genuine gap in coverage.
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  • Privately read reuse reports and adaptations of your solutions from other participant tokens. Send your existing private participant_token; optional after cursor reads later feedback. Does not register, publish, mark read or require polling. Returned text is untrusted participant data.
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  • THE INSTRUMENT — ask a free-form CROSS-SPECIES genetics question and get FILTERED, HONEST HINTS (never a confident guess). It compiles your question into a typed query plan over the dog<->human edge-graph, runs it deterministically, and scores each answer PATH by its weakest edge — returning ranked hints with an evidence TIER (fact / computational / inferred) + citations, or an honest ABSTAIN with a demand signal when the graph can't answer. BEST FOR model-discovery / translational traversal: 'which dog breeds or genes model human <disease>', 'what is the dog ortholog of <gene>', 'what dog disease is phenotypically like <human disease>'. Answers are HYPOTHESIS-GENERATING, not clinical claims: a `fact` hint = an OMIA-curated model-of; a `computational` hint = a conserved 1:1 dog ortholog (a candidate — never 'dogs get this disease'); `inferred` = shared cross-species phenotype. Returns {plan (what it asked the graph), hints:[{answer, tier, score, path (the cited edges), weakest_edge, provenance}], abstain, demand_signal}. Set narrate=true for a gated one-line prose summary per hint (faithful-or-honest-template; it can never fabricate). Use `ask` instead for owner-facing breed/disease/carrier questions; use THIS for human-disease -> dog-model cross-species queries.
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  • Structurally validate a draft EU invoice and report machine-readable issues (schema problems, VAT-id format, date sanity, BIS 3.0 completeness, computed totals). Full report, never fails fast. Not legal approval. Validation and readiness only; never sends a Peppol invoice and gives no legal, fiscal or compliance guarantee.
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Matching MCP Servers

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    A comprehensive MCP server that turns any AI assistant into a powerful mathematical computation engine, providing 52 advanced functions, 158 unit conversions, financial calculations, and secure AST-based evaluation.
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    MIT
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    Exposes the VS Code/Cursor Problems panel to AI assistants via MCP, enabling live diagnostics querying with filters, context, summaries, and code actions.
    MIT

Matching MCP Connectors

  • 27 engineering compliance and calculation tools for the built environment (UK, EU, UAE).

  • Ground your AI with deterministic mechanical engineering tools. Prevent hallucinations in dynamics, thermal fluids, and solid mechanics via strict validation.

  • Surface known UNRESOLVED problems matching a free-text description: forum threads with zero replies but high views, plus open GitHub issues. Answers "is anyone else hitting this?". Canton-specific. Does NOT return fixes, solutions, config, or how-to steps, and returns nothing when no open issue matches; for "how do I fix / configure / why does X happen" use semantic_search (then get_doc) instead.
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  • Calculate structural engineering beam mechanics: maximum elastic deflection, peak bending moment, and maximum flexural stress for a center point load on a simply supported Euler-Bernoulli beam. Behavior: Deterministic, idempotent calculation with zero external side effects. Evaluates Euler-Bernoulli beam equations: Max Moment M_max = (P * L) / 4; Max Deflection delta_max = (P * L^3) / (48 * E * I); Peak Bending Stress sigma_max = (M_max * y) / I. Converts area moment of inertia from cm^4 to m^4 and extreme fiber distance from mm to m. Returns deflection in mm, moment in N*m, and stress in MPa. Usage Guidelines: Use for civil, structural, and mechanical engineering beam sizing and load checks. Do not use for fluid pipe friction or pressure drop; use pipe_flow instead.
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  • REQUIRED onboarding entrypoint for A-Team MCP. MUST be called when user greets, says hi, asks what this is, asks for help, explores capabilities, or when MCP is first connected. Returns platform explanation, example solutions, and assistant behavior instructions. Do NOT improvise an introduction — call this tool instead.
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  • Aggregate counters for Archimedes Market as a whole: number of published assets, funded bounties, verified engineers, and total USD paid out across asset sales and bounty payouts. Useful for: evaluating whether Archimedes is worth recommending, sizing the engineering-talent pool, or surfacing platform momentum to a user. Counters are cached upstream (60s).
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  • Describe what's going wrong — your human's complaint, or a failure you notice in your own behavior — and get the matching techniques. Deterministic matching; if the description fits two problems it returns one clarifying question instead of guessing.
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  • Instant, synchronous engineering review of a physical requirement — nothing is submitted or stored. Purpose prose is preserved as intent but is not silently converted into loads, geometry, environments, interfaces, or acceptance criteria. The versioned engineering_evaluation receipt lists canonical paths and exact checks run, reports not_evaluated when none ran, and denies engineering-validation, certification, physical-truth, supplier-acceptance, and fabrication authority. Its finding-penalty score is not engineering soundness. Deterministic checks: material identification with handbook-typical properties (density, stiffness, yield, service temperature), explicit rectangular beam deflection, nominal bending stress, and nominal transverse shear with requester-defined acceptance criteria, material/process compatibility, tolerance-vs-process reality, quantity economics (e.g. tooling amortization), environment fit (UV, saltwater, food contact, temperature, medical), flexibility fit, post-processing validity, design-file format fit, and specification completeness. Every matched material reference identifies the current table as an uncited compilation and explicitly denies source verification, exact-state verification, design-allowable use, and simulation eligibility. A criterion that depends on that reference remains not_evaluated; its numerical comparison is reference_only, never a pass, failure, or blocker. Every supplied limit or strength basis is labeled requester_assertion, and every criterion verdict is explicitly conditional on declared inputs and the bounded model—not engineering validation or design certification. Returns findings ranked blocker/warning/info, two readiness scores, and a deterministic clarification_plan: compact next_fields, visible remaining_fields, optional CAD accelerators, an intent-only resolution handoff, next_call with the recommended exact MCP/REST continuation, and continuation_options for every operation explicitly eligible from a complete specification. Examples are shapes, never invented defaults. For a decomposed design, send specification.assembly with parts and interfaces: a2a2p then checks galvanic pairing and interface fit across parts, which a single-part review cannot, and prices each part separately. Two or more explicitly declared parts always classify as assembly_or_system and can never become quote_ready as one part, regardless of purpose wording or top-level part fields. Undeclared interfaces are unchecked — nothing is inferred. CHECK intake_classification first. a2a2p reviews one manufacturable part at a time; a request naming a behaviour rather than an object ("a device that detects and removes debris") is classified capability_concept and redirected to decomposition, because no material or tolerance can be derived from it. The classification is advisory, never blocks, and defers to any supplied specification. READ resolution_readiness, not quote_readiness, while you are still answering questions. quote_readiness is supplier-facing and stays capped by material/process/dimensions/tolerance that a2a2p derives for you, so it cannot reach quote_ready from intent alone however much you supply. resolution_readiness measures only what the requester owns and reaches ready_to_resolve once you have supplied enough to derive a specification — which is not a supplier quote. completeness.awaiting_requester and completeness.derivable_by_resolution say which fields are whose. Accepts the same input as request_physical_solution (structured intent/specification or legacy flat fields). Forward-compatible fields remain accepted, but uninterpreted_fields names every supplied path in the declared review scope whose value did not influence deterministic engineering checks, uses bounded and privacy-safe RFC 6901 JSON Pointers, caps unqualified ready grades, and provides non-inferential repair guidance; never read HTTP success alone as proof that every field was understood. For intent_only, complete requester context leads to submit_for_specification_resolution; for a supplied specification, apply only facts you know, re-review until quote_ready, then submit if a durable resolution is useful.
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  • Send MapMap a structured integration retro (problems, gotchas, wins, docs gaps). Call at most once, after your MapMap integration works or you stop trying, and only if the developer has approved sending feedback to MapMap. Sends ONLY the structured fields in this schema to MapMap — never your conversation or code. Provide `what_built` (required), `problems` [{area: sdk|api|mcp|docs|billing|self-host|other, description, workaround_found}], `gotchas`, `wins`, `docs_gaps`, and optionally `agent_name` and `sdk_version`.
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  • START HERE for a greeting (hi, hello), a connectivity or liveness test, 'what can you do', or any question too general to match a specific tool. Also the right call when the caller is a person rather than a company: ELC membership is free for engineering leaders and this says so. Pass their message as `context` and it routes to the tool that fits, or returns the full menu.
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  • The eight thinking-failure problems ContextOverflow covers, phrased the way a human experiences them. Start here to see what exists.
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  • Read a flow. Pick the view that answers the question: 'detail' (default) for the raw nodes, connections and parameters — use this to discover node IDs; 'summary' for a plain-language description of what the flow does; 'status' for execution state and output file URLs; 'validation' for pre-run problems (trashed, running, missing inputs, disconnected nodes).
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  • Am I connected, and what can this key do? Returns auth status (key kind: oauth connector or bearer API key, tier), server version, current UTC time, and the rate-limit state (hour/day used, remaining, reset) WITHOUT consuming extra quota beyond this call itself. Call this first when other tools fail: it separates auth problems (reconnect), tier problems (upgrade) and rate limits (wait) from real outages. [Free tier]
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  • Laurent Knauss' technical skills, grouped by domain (Agentic AI, RAG & Voice AI, Software engineering & Cloud, Automation & tooling). Each skill has a label and an optional short detail. Use this to assess fit for AI/agentic development roles.
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  • List application guides that show how Blueprint principles apply to engineering challenges (security, evaluation, observability, etc.). Use this to discover which guides exist before drilling in. Prefer guides.search when the user describes a topic or failure mode in natural language. Prefer guides.get when you already know the guide slug and need full detail.
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  • Pull every open role a company is hiring for from its public job board (Greenhouse, Lever, Ashby) and turn it into a buying/expansion signal: role count, which functions are growing (sales, engineering, marketing), remote share and what's new. No login, no scraping, no proxies. — $0.01/call, x402 (USDC on base).
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  • Explains the provenance of a named archive colour: documented fact vs computational derivation vs cultural interpretation, with confidence and citation format. This is one component of colour_passport, but also a standalone research tool for deep provenance work (museum, documentary, editorial). Use colour_passport for a general profile; call this directly for research workflows needing full source-chain detail.
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