"Understanding Code Relationships or Dependencies" matching MCP tools:

• map_loinc_to_snomed

  Medical Terminologies MCP

  This tool looks up a LOINC code in NLM Clinical Tables and returns guidance on where to obtain a LOINC → SNOMED CT mapping. It does not perform the mapping. Direct LOINC → SNOMED CT mappings are not freely available via API. UMLS Metathesaurus contains the relationships but requires an individual UMLS Terminology Services license; the LOINC SNOMED CT Expression Association is published by Regenstrief Institute as part of the LOINC release and requires authenticated download from loinc.org under the LOINC license. For programmatic LOINC → SNOMED mapping, use UMLS or the LOINC Expression Association files. For interactive lookup, use the SNOMED CT browser available to your organization or the Regenstrief RELMA desktop tool. Provide a LOINC code like "2339-0" (Glucose) or "718-7" (Hemoglobin).

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• audit_dependency_tree

  Commit — Supply Chain Risk Scoring

  Map the full dependency tree of an npm package and identify CRITICAL supply chain risks at every level. Unlike auditing a flat list of packages, this tool traverses the dependency graph — showing not just your direct dependencies but also what your dependencies depend on. Hidden CRITICAL packages (sole publisher + >10M weekly downloads) often lurk 1-2 levels deep. Risk flags: - CRITICAL: single npm publisher + >10M weekly downloads — sole point of failure for a massive attack surface - HIGH: sole publisher + >1M/wk, OR new package (<1yr) with high adoption - WARN: no release in 12+ months (potential abandonware) depth=1 (default): root package + all direct dependencies depth=2: also traverses one more level for any CRITICAL/HIGH direct deps (reveals hidden exposure) Examples: - audit_dependency_tree("express") — see all of Express's deps and their risk scores - audit_dependency_tree("langchain", 2) — reveal transitive CRITICAL deps 2 levels deep - audit_dependency_tree("@anthropic-ai/sdk") — audit Anthropic SDK full tree Use this when someone asks: - "What am I really depending on?" - "Are my dependencies' dependencies safe?" - "Show me the full supply chain risk for package X"

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• swift_lookup

  banking-intelligence

  Search banks and financial institutions by name, SWIFT/BIC code, or country. Covers both SWIFT-connected banks and non-SWIFT financial institutions (e-money issuers, payment processors, MFOs, brokerages, VASPs, etc.). Returns: SWIFT/BIC code (if any), name, city, country, institution type, GPI membership, a coarse sanctions FLAG across 7 hard-sanctions watchlists (OFAC SDN, EU, UK, CA, CH, AU, NZ — see sanctions_note; this is NOT a full screen, use sanctions_screen for a compliance verdict), and enriched bank profile when available. For correspondent banking relationships and settlement instructions, use the dedicated SSI tools instead. The country parameter accepts both 2-letter ISO codes ("ID", "DE") and full English names ("Indonesia", "Germany"). Names are resolved automatically. Examples: swift_lookup("DEUTDEFF") # exact BIC lookup swift_lookup("Deutsche Bank") # search by name swift_lookup("TBC PAY") # find non-SWIFT payment processor swift_lookup("bank", country="KZ") # explore banks in a country swift_lookup("Halyk", country="KZ") # find specific bank in country swift_lookup("Bank Mandiri", country="Indonesia") # full country name OK

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• musicbrainz_get_artist

  musicbrainz-mcp-server

  Artist profile by MBID: type (person/group/…), country, life span, gender, area, aliases, tags/genres, plus the discography (release-groups) and band-membership / collaboration relationships and external links (Wikidata QID, Discogs, official site — surfaced as url-rels chainable to those servers). The 80% artist-detail call. Discography and relationships are capped at one page (25); for a prolific artist's complete release-group list, call musicbrainz_browse_entities with target_type=release-group and the artist link.

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• musicbrainz_get_recording

  musicbrainz-mcp-server

  Recording (a specific performance/track, distinct from the abstract work) by MBID: length, artist credits, ISRCs, the releases it appears on, the work(s) it performs (work-rels — chain to musicbrainz_get_work), and performance/production relationships (who played, produced, engineered, conducted — each with the role and the credited artist MBID). Relationships are capped at one page; for a heavily-covered recording call musicbrainz_browse_entities with target_type=recording and link.work.

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• get_entity_graph

  Origine Paris

  Returns the Origine Paris entity graph: the company and its founders as nodes, with the sourced edges between them (founded, chief executive officer, director, employed by). Use it when you need the relationships between entities; for one entity's own fields use get_brand_identity or get_person_profile instead, not this. Read-only and side-effect-free: it returns structured nodes and edges plus a text copy, every edge carrying its sources, with the index timestamp and the canonical URL, built from Wikidata and corroborated by the site JSON-LD; relationships absent from the sources are not asserted.

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• start_phone_verification

  agentcard

  Send (or re-send) the user's one-time funding verification code (the provider verifies the phone on the user's Agentcard identity, valid 60 days). add_funds already sends this code automatically when verification is needed — call this tool only to RE-send when the code never arrived (any unexpired code still works; sends are rate-limited). Returns the masked destination (text or email) and whether a code was sent; if the phone is already verified it says so and you go straight to add_funds. After the user reads back the code, call verify_phone.

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• create_npc_mnemons

  argo-mcp

  Create NPC mnemons (FACTION or INDIVIDUAL). npcType is REQUIRED on each item. Use memberNpcEntryIds (on FACTIONs) and affiliationEntryIds (on INDIVIDUALs) to wire membership; the server projects into MEMBER relationships. Players may not call this — GM/co-GM only.

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• duvera__dev_npm_package

  gateway

  [duvera · risk:low] Look up the latest version, description, license, and dependencies of an npm package. Works for scoped packages too (e.g. "@types/node"). No account required.

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• get_categories

  Radix Wiki

  Get the wiki tag hierarchy with page counts per category. Useful for understanding what content exists, and for finding a valid tagPath before writing.

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• web_snapshot

  Robot Actions — Remote Device Control

  Accessibility tree of the DESKTOP grid browser page (by pageId), as text — for finding elements and understanding layout. Not a device: the equivalent for a phone or tablet is webpage_snapshot (by udid).

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• check_geometry

  DaedalMap Geocoding and Reverse Geocoding (loc_id)

  Fast shape-only preflight for one loc_id or a bounded loc_id list. Reports whether each exact identity has reusable geometry and its geometry vintage; it does not resolve points or explain identity relationships. Use before get_geometry or an export. No payment required.

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• check_geometry

  DaedalMap Disaster and Geospatial Data

  Fast shape-only preflight for one loc_id or a bounded loc_id list. Reports whether each exact identity has reusable geometry and its geometry vintage; it does not resolve points or explain identity relationships. Use before get_geometry or an export. No payment required.

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• get_locality_schema

  DataGraph

  Get the graph schema for a locality (node types, relationships, sample queries). Call list_datasets first to get locality codes.

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• map_business_process

  ALM X++ MCP Server

  WHEN: mapping the technical D365 objects behind a business process, or understanding which tables/forms implement a flow. Triggers: 'processus métier', 'Order-to-Cash', 'Procure-to-Pay', 'Record-to-Report', 'business process flow', 'qui est impliqué dans', 'map the process', 'flux du processus', 'quels objets dans le flux'. Map a D365 F&O business process to its complete object chain. For known processes (Order-to-Cash, Procure-to-Pay, Record-to-Report, Plan-to-Produce, Inventory-Management, Hire-to-Retire, Project-Accounting, Asset-Lifecycle): shows every step with forms, tables, classes, entities, reports, and security roles involved. For any other object name: traces all dependencies (tables, classes, forms, entities) from that entry point. Produces a Mermaid process flow diagram. Use 'list' to see all known process mappings. NOT for a single object's FK relations only -- use `find_related_objects` for that (faster and more precise).

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• get_trident_spec

  trident

  Returns the complete Trident 2D specification including grammar, syntax rules, coordinate system, containers, nodes, connections, shapes, and icon reference. Use this when you need deep understanding of the Trident DSL.

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• homology

  String Db

  Retrieve STRING-DB homology mappings for a list of protein identifiers (gene symbols or accessions) within a given NCBI taxonomy species (default 9606=human), returning cross-species homolog relationships.

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• list_categories_api_agents_categories_get

  Agentry — The Trust Layer for the Agent Economy

  List Categories List all agent categories with counts. Returns every category in the directory along with the number of agents in each. Useful for building category filters or understanding the directory's coverage areas. ### Responses: **200**: Successful Response (Success Response) Content-Type: application/json **Example Response:** ```json [ { "category": "Category", "count": 1 } ] ```

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• get_mission_stats

  mcp-server

  Get aggregate statistics about missions on the HomeVisto platform. Returns total counts, status breakdown, and average bounty information. Useful for understanding platform activity.

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• cross_signal_correlate

  Trillboards DOOH Advertising

  Discover correlations between different signal types. Example: relationship between ad fill rate and audience attention for QSR venues. Queries the cross_signal_insights table for pre-computed correlations, or computes ad-hoc correlations from the observation_stream when no pre-computed insight exists. WHEN TO USE: - Understanding relationships between different sensing signals - Finding which audience behaviors correlate with business outcomes - Discovering hidden patterns (e.g., crowd_energy vs purchase_intent) - Validating hypotheses about audience-venue-time relationships RETURNS: - data: Correlation analysis with: - signal_a, signal_b: The two signals being correlated - correlation_r: Pearson correlation coefficient (-1 to +1) - correlation_r2: R-squared (proportion of variance explained) - p_value: Statistical significance - sample_count: Number of data points used - effect_size: Cohen's d effect size - confidence_interval_lower, confidence_interval_upper: 95% CI bounds - insight_summary: Human-readable interpretation - metadata: { computation_method, window, filters_applied } - suggested_next_queries: Related correlation analyses to explore EXAMPLE: User: "Is there a correlation between audience attention and ad fill rate at QSR venues?" cross_signal_correlate({ signal_a: "attention_score", signal_b: "ad_fill_rate", filters: { venue_type: "restaurant_qsr" } }) User: "How does crowd energy relate to purchase intent during lunch hours?" cross_signal_correlate({ signal_a: "crowd_energy", signal_b: "purchase_intent", filters: { daypart: "lunch" } })

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