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605,063 tools. Updated 2026-09-23 22:39

"Exploring "Node" - A Versatile Term with Various Interpretations" matching MCP tools:

  • Browse Smithsonian objects within one exact category — a single museum (mode "museum"), culture, indexed date term (mode "period"), object type (mode "medium"), or subject term (mode "topic"). The value must be an exact indexed category term, not free text: resolve museum, culture, period, and topic vocabulary with smithsonian_list_terms first (object_type is not enumerable there — harvest it from smithsonian_search_objects results, and treat each casing as its own category, since a harvested object_type covers only the casing it was written in). Returns the category total count, a page of matching objects, and a museum breakdown of that page; page the full category with start and rows. For open-ended or topic discovery, start with smithsonian_search_objects instead.
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  • Search for diagram nodes by keyword across all providers and services. For targeted browsing when you know the provider, use list_providers -> list_services -> list_nodes instead. Args: query: Search term (case-insensitive substring match). Returns: List of matching nodes with keys: node, provider, service, import, alias_of (optional). Sorted by relevance: exact match first, then prefix, then substring.
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  • <summary>Edit one existing node of a campaign's flow in place, leaving the rest of the DAG untouched. Use this — not `define_sequence` — whenever the set of nodes isn't changing: to change a send step's message (`message_template`), change a decision's `rule` or `arms`, relabel any node, reword a manual step's `action_description`, or repoint a node's out-edges. Pass the node's full new definition; its `id` selects which node to replace and must already name a body node, and its `kind` must match that node's current kind. Because no node is added, dropped, or renamed, every prospect keeps its cursor and each node keeps its `on_enter` enactment (prompt, code, run history). The edited sequence is re-validated as a whole, so an edge repoint that would dangle a reference, orphan a node, or introduce a cycle is rejected rather than saved. To add or remove nodes, change a node's kind, or edit the start node, re-author the flow with `define_sequence` instead. A forked-on condition is often restated in several places — a decision's `rule`, each `arm.case`, a downstream terminal's `label`, and the node's `on_enter` prompt. When you change one, change the others in the same turn (a further `update_node` for the node fields, `update_trigger` for the prompt) so they agree.</summary> <returns> <description>Dict with `success`, `agent_id`, and `node_id`. Read the updated flow via get_campaign_flow.</description> </returns>
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  • Keyword Research (marketplace term universe): search the FULL Top Search Terms store by phrase - not anchored to any ASIN. Filters: contains (up to 5 substrings, ANDed), department, rank_max, min_volume; period WEEK or MONTH (MONTH = monthly volumes); sort rank | volume | trend. Each term: search_frequency_rank, volume (amazon_sqp = TRUE Amazon volume where any account's SQP covers the term; estimated_from_rank with volume_band otherwise), rank_change vs ~4 periods back, and the top-3 clicked ASINs with click/conversion shares (the term's competitive landscape). The estimator block reports calibration health (pairs, typical error factor, band coverage). Use keyword_finder for ASIN-anchored lookups; this tool for term-first research. For the guided process start with keyword_opportunities.
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  • Use this to find which EU instruments define a legal term, such as payment transaction or payment account, and where their definitions differ. For definition wording use get_defined_term. A listing: term, definers and divergence flags, with no definition text. Matching is over the term itself, never the definition body. Divergence is judged on the RESOLVED text as well as the verbatim one, because several instruments defining a term by pointing at the same root agree, however differently their pointers read. Pass only_divergent to find where the corpus does not agree with itself.
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  • Get one pipeline: metadata plus a summary of its node graph (node ids, types, labels). Pass includeGraph to get the complete graph — every node with position and config, and every edge — which is what update_pipeline needs as a starting point. Never write a graph reconstructed from the summary: it drops configs.
    ConnectorAPI key

Matching MCP Servers

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    A modular MCP server that provides AI agents with cognitive memory, local system access, and secure remote execution capabilities through three integrated layers (Brain, Master, Remote).
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    A persistent, self-organizing memory MCP server for AI assistants, using semantic search, knowledge graphs, and reinforcement learning to automatically manage and retrieve memories.
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    MIT

Matching MCP Connectors

  • A forum whose members are AI agents. Publish verifiable findings, enter scored challenges.

  • AI-to-AI petrol station. 56 pay-per-call endpoints covering market signals, crypto/DeFi, geopolitics, earnings, insider trades, SEC filings, sanctions screening, ArXiv research, whale tracking, and more. Micropayments in USDC on Base Mainnet via x402 protocol.

  • Retrieve proteins annotated with a functional term or descriptive text in a single species. You can query for tissues, compartments, diseases, processes, pathways, and domains. IMPORTANT: For cross-species comparisons, run this tool separately for each species. Select relevant model organisms to search or ask user to provide the selection. The results reflect annotation depth within each category; use caution when interpreting. If no results are found, try simplifying the query. For tissue queries, follow BRENDA tissue nomenclature and omit the word "tissue" (e.g. use "skin" instead of "skin tissue"). Output fields: - category: Source database of the matched functional term (e.g. GO, KEGG, Reactome, Pfam, InterPro). - term: Exact identifier for the functional term. - description: The free text description of the term. - proteinCount: Number of proteins annotated with that term - preferredNames: Full protein-name list when `detail_for_term` is set - stringIds: STRING protein identifiers when returned - preferredNames_omitted: True when a row omits the protein-name list - stringIds_omitted: True when STRING identifiers are omitted
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  • Find cross-provider equivalents for a diagram node by infrastructure role. Given a node name (e.g. 'EC2', 'Lambda', 'ComputeEngine'), returns the infrastructure role category it belongs to and the equivalent nodes from other providers. If a node name is ambiguous, use list_categories to see all mapped roles and pick a provider-specific node name. Args: node: Node class name to look up (case-insensitive, e.g. 'EC2', 'lambda'). target_provider: Optional provider to filter equivalents to (e.g. 'gcp', 'azure', 'aws'). If omitted, all equivalents across all other providers are returned. Returns: A dict with keys: category (str): Infrastructure role category name. description (str): Human-readable description of the category. source (dict): The matched node with keys node, provider, service, import. equivalents (list[dict]): Equivalent nodes, each with keys node, provider, service, import.
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  • For one world point, the contribution of EVERY light in the scene, computed from the saved scene (no render, instant): each light node in reach with its value (three's falloff and spot cone, from the node's WORLD position and aim), the studio sun or the live sky, and `ambient` = lighting.env.intensity NAMED as a term, sorted by contribution, plus a `why` on each light that does not reach (out of range, outside the cone, behind the panel). Pass `nodeId` for the surface's albedo: a pale albedo shows the ambient term 2-3x as strongly as a dark one. Use it BEFORE deleting lights to find a phantom glow: a surface no lamp reaches that still reads lit is lit by env.intensity (or the sky in time mode), and the answer is the settings op, not another lamp. Occlusion is not computed (a wall between does not reduce the number).
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  • Entropy routing over competing interpretations — the model proposes, the deterministic core disposes. YOU generate the candidate readings of the user’s message (3–7 short hypotheses covering the plausible interpretations, INCLUDING likely-typo readings, idiom-vs-literal readings, and domain senses) and pass them as hypotheses, ideally with your own likelihoods (0–1 per reading) AND a paraphrase per reading — the user’s message rewritten unambiguously under that interpretation, so the user can VERIFY intent by recognition before anything commits (one misread prompt skews a whole thread). The router computes the posterior and its normalized entropy T̂ and returns the decision: commit (one reading dominates), commit_with_note (close alternative disclosed), present_options (several readings live), or clarify (ask before acting — open-endedly when nothing discriminates). Thresholds adapt to the user’s ReceiverProfile (AR widens/narrows the commit region; high FT discloses near-ties). This tool is the commit-vs-clarify AUTHORITY in the pipeline. Omitting hypotheses falls back to a generic six-intent PRODUCT-ROUTING starter set — do not use the fallback for interpreting arbitrary sentences. Deterministic, stateless, read-only. Benchmarked: RTEB v1.1 (developer-bench grade; see docs/routing.md).
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  • Discover canonical identity from an ordinary-language query — the entry point when no canonical id is held. Ranked: identity fields and sealed claims decide candidacy, Section text only when no identity field matches; ordered by identity score, then Section score, then how many words matched, then manifest position. Node candidates carry id, status, Hook, and disclosure class; Decision and term candidates carry exactly a read address and Hook. A term's Hook is its full sealed definition. Results are paged. A miss is a successful empty payload.
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  • Cancel the drainable leads at a workflow node (WAITING, WAITING_FOR_EVENT, PENDING_REVIEW entries are marked cancelled). PROCESSING leads have a live runner job and are left to finish on their own; the response reports how many remain. Call this after campaignstack_update_workflow or a node deletion is rejected with NODE_HAS_ACTIVE_LEADS, to drain the node, then retry the graph change. It cancels queued work only, never a message already sent: there is no way to recall one. Use campaignstack_get_workflow to find node IDs.
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    Destructive
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  • Execute a workflow. Run validate_workflow first to catch build errors. SIDE EFFECTS: this really runs every node — sends, external writes, payments, deletes. Do NOT run write/send/payment nodes without explicit user approval. Returns {execution_id, status, node_results, node_states (terminal status/error per node)}; fetch full authoritative outputs with get_node_output, inspect agent-node actions with list_tool_calls. Pass inputs={...} to simulate a form submission / webhook body (injected as the trigger node's output). Set return_output=true for full node outputs instead of truncated previews.
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    Destructive
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  • Walk a US medical code system's hierarchy for discovery without a search term. With no `node`, returns the top-level entries (ICD-10-CM categories, HCPCS range buckets, or ICD-10-PCS first-axis values). With a `node`, returns its immediate children. ICD-10-CM and HCPCS use a prefix hierarchy (a shorter code is the parent of a longer one); ICD-10-PCS is axis-based — each of its 7 characters is an independent axis (section, body system, root operation, body part, approach, device, qualifier), but only the top-level Section axis is browsable (omit `node`): positions 2–7 are context-dependent on the preceding axis path and are not enumerable from a flat partial code. Lets an agent orient in an unfamiliar system or enumerate a category's specific codes. A large child set paginates: when the response carries a `nextCursor`, pass it back as `cursor` to fetch the next page.
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  • Walk and verify a stamp's full provenance lineage by merkle_root. Use this when a stamp was created with derived_from links, to map what an output was built from. Returns a MAP of the graph (not a yes/no verdict): each node carries hash_binds and anchored, each edge carries edge_verified, and `valid` is true only if every node and edge checks out. A node whose payload was never published is an unresolved frontier (resolved:false). Read-only. Provenance, not truth.
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  • Return content for one or more navigation nodes in a filing. Pass one node via node_id or several via node_ids, using the node ids from TOC lines (the same values shown as NODE_ID lines in this tool's output). Returns plain MCP text: an ARTIFACT_DOCUMENT_ID header, then per-node blocks with NODE_ID, TITLE, CITATION_URL, CITATION_MARKDOWN, CONTENT_START … CONTENT_END. Each block's CITATION_URL links to the reader location for that node; CITATION_MARKDOWN is the same link as ready-to-paste markdown [TITLE](CITATION_URL). A requested TOC node may expand into more granular child NODE_ID blocks, each with its own TITLE, CITATION_URL, and CITATION_MARKDOWN. Responses that include citations end with a CITATIONS_IN_THIS_RESPONSE list.
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  • Reject a proposed process landscape node while it is still a proposal, removing it and any proposed descendants. Real processes nested underneath survive and move back to Unsorted. Only proposals can be declined — an accepted node is removed with deleteClarityLandscapeNode.
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  • Calculation, not advice. Verify with a professional before acting. Deterministic mortgage refinance break-even analysis. Given your current loan (balance, rate, remaining term) and a refinance offer (new rate, new term, closing costs, optional points), computes: monthly P&I savings; the cash-flow break-even month (total refinance cost divided by monthly savings, CFPB convention); the lifetime interest delta over your remaining-term horizon; a term-matched scenario that isolates the rate cut from a term reset; a term-reset-trap flag (lower payment but higher lifetime interest from extending the term); and the economic break-even (net-worth crossover) month using an equal-outflow invest-the-savings model. Rate-and-term refis only (cash-out and tax effects are out of scope). Pick this when refinancing your existing mortgage into a new rate and term is the question; pick `compare_mortgage_terms` when comparing two mortgage structures on a purchase you have not yet taken out. All defaults cite primary sources (LodeStar/ALTA closing-cost data, CFPB break-even convention). Scalar output, no chart series.
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  • Create a workspace node of any type One call creates any of the 11 node types with its type-specific payload: `fields` for a Base, `body` for a Doc, `files` for a Skill/Drive/AirApp, `assetId` for a File. Review is permission-aware, decided server-side: this merges immediately when you already have write access on the parent node, and proposes a ChangeRequest for a human otherwise. Pass requireReview to always propose instead of merging. Before you finish: if the person will come back to this node to do the same job again, pass agentPrompts so the node opens with THEIR job on it instead of the node type's generic list.
    ConnectorAPI key
  • Look up the WEO methodology definition for any platform-specific term, field, or concept (e.g. "CC-V", "T2", "PAA", "ENT-1", "PIET"). Returns the term's definition, its section anchor, a deep link to that section of the published methodology, and the methodology version. Use to resolve any vocabulary the other tools return. Pass `term`; matching is exact-first, then substring, and an unknown term returns a sample of available terms.
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  • Returns all published Arco Lexicon terms grouped by pillar, each with its slug and canonical short definition. Accepts an optional pillar filter. Use this tool first when you do not know which term to look up — it gives you the full vocabulary to orient from. Use lookup_term once you have identified the term you need.
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