ZWING KG

endpoint

https://api.dyor.network/mcp

protocol

http-sse ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is ZWING KG live?

Yes — it answered the hub's last check (checked 2h ago). It answered 100% of checks over the last 30 days.

Is ZWING KG free to use?

Yes — the hub reached it with no key and no payment.

What tools does ZWING KG have?

20 tools: query_insights, query_sparql, get_entity_insights, get_insight, screen_fields, semantic_search, explore_entity, query_timeseries, ….

Is ZWING KG safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• list_narratives reads open 24h ago

  List the curated crypto narratives/sectors (RWA, liquid staking, DEXes, DePIN, AI agents, memecoins, ...) each with headline aggregates: combined revenue, median P/S, TVL, market cap, and member count. Use narrative_rollup for one narrative's full breakdown. Example prompt: 'which crypto sectors have the highest combined revenue?'

  mcp-tool

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  {
    "type": "object",
    "properties": {}
  }

  arguments 4 lines copy
• query_changes reads open 24h ago

  Query recent changes to the knowledge graph. Returns entity property changes (CREATE, UPDATE) from the change log. Use this to answer questions like 'what changed since yesterday', 'show me new tokens added this week', or 'what properties changed for Aave'. Parameters: since_hours: Look back this many hours (default: 72) entity_uri: Filter by specific entity URI operation: Filter by 'CREATE' or 'UPDATE' limit: Max results (default: 100, max: 100) summary: If true, return counts instead of individual changes Cost: $0.1 per call (x402 / USDC on Base)

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "limit": {
        "type": "integer",
        "default": 100
      },
      "summary": {
        "type": "boolean",
        "default": false
      },
      "operation": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null
      },
      "entity_uri": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null
      },
      "since_hours": {
        "type": "number",
        "default": 72
      }
    }
  }

  arguments 39 lines copy
• get_current_time reads open 7h ago

  Get the current UTC time. Useful for relative date queries.

  mcp-tool

  1234

  {
    "type": "object",
    "properties": {}
  }

  arguments 4 lines copy
• list_insights reads open 7h ago

  Browse entity-keyed Insights (free). Each Insight aggregates the active signal stack for one entity. Returns tagline (LLM headline), active signal types, convergence score, and a signal_summary excerpt. Use get_insight for the full thesis on a specific entity. This is the data shown on /demo/signals. Parameters: limit: Max results (default: 50, cap: ROW_CAP) min_convergence: Minimum convergence_score threshold qualification: Exact filter: 'qualified', 'monitor', 'rejected' entity: Substring match on entity URI exclude_rejected: Drop entities whose latest insight is rejected (default true)

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "limit": {
        "type": "integer",
        "default": 50
      },
      "entity": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null
      },
      "qualification": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null
      },
      "min_convergence": {
        "anyOf": [
          {
            "type": "number"
          },
          {
            "type": "null"
          }
        ],
        "default": null
      },
      "exclude_rejected": {
        "type": "boolean",
        "default": true
      }
    }
  }

  arguments 46 lines copy
• query_insights reads unknown 24h ago

  Browse insight signals from the analytics pipeline (free, limited fields). Returns signal summaries WITHOUT evidence or metadata. Use get_entity_insights ($1) for the full deep-dive on a specific entity, or get_signals_by_type ($0.10) for full signals of a specific type. Parameters: limit: Max results (default: 50, max: 100) since_hours: Look back window in hours (default: 72) signal_type: Filter by type (e.g. 'revenue_acceleration', 'yield_anomaly') min_score: Minimum score threshold qualification: 'qualified' (default; matches qualified OR screened), 'screened', 'pending', 'rejected', or 'all' entity: Filter by entity name (substring match) channel: Signal channel; default 'user'. Pass 'all' to include internal-only types.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "limit": {
        "type": "integer",
        "default": 50
      },
      "entity": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null
      },
      "channel": {
        "type": "string",
        "default": "user"
      },
      "min_score": {
        "anyOf": [
          {
            "type": "number"
          },
          {
            "type": "null"
          }
        ],
        "default": null
      },
      "signal_type": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null
      },
      "since_hours": {
        "type": "number",
        "default": 72
      },
      "qualification": {
        "type": "string",
        "default": "qualified"
      }
    }
  }

  arguments 54 lines copy
• query_sparql reads unknown 22h ago

  Execute a SPARQL query against the ZWING RDF knowledge graph. Returns bindings as a list of dicts. Useful for querying RDF triples that aren't in the property graph (provenance, detailed metadata). WRITING EFFICIENT QUERIES (the backend is a single read-only Oxigraph store; one costly query ties up a core, so keep queries selective): 1. ALWAYS include a LIMIT — never return an unbounded result set. 2. Anchor on a specific entity or rdf:type FIRST, then expand. Start the pattern from a bound ?s (an IRI or `?s a dk:Token`), not from `?s ?p ?o`. 3. Avoid graph-wide aggregation — COUNT / GROUP_CONCAT / GROUP BY / DISTINCT over the whole store is expensive. Scope it inside a subquery that is already filtered to a handful of subjects. 4. Avoid regex/`CONTAINS` FILTERs to find entities by name or concept — they scan every literal. Use the `semantic_search` tool to resolve a name/idea to an entity IRI, then query SPARQL from that IRI. 5. Keep OPTIONAL blocks few and each anchored to an already-bound variable; chained/unanchored OPTIONALs multiply the work. 6. There is no reasoner/inference — query asserted triples only. Common prefixes: PREFIX schema: <http://schema.org/> PREFIX dk: <https://schema.dyor.bot/crypto-kg#> PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> Example (anchored on a type, bounded): tokens with ticker symbols: PREFIX schema: <http://schema.org/> PREFIX dk: <https://schema.dyor.bot/crypto-kg#> PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> SELECT ?token ?name ?ticker WHERE { ?token rdf:type dk:Token ; schema:name ?name . OPTIONAL { ?token schema:tickerSymbol ?ticker } } LIMIT 20 Materialised RDF types (instance count in parens): dk: SmartContract (197K), Token (46K), Protocol (10K), DAO (5K), HackEvent (5K), Chain (3K), Project (1K), Address (1K), Exchange (906), FundingRound (147), Blockchain (100), Nft (100), Proxy (40) schema: Person (100K), Organization (50K), InvestmentFund (12K), Corporation (194) prov: Entity (316), Agent (169), Activity (36) ?: RiskFinding (379), RiskDimension (198), Audit (128), CollateralAsset (57), AdminRole (48), Auditor (42), KillSwitch (41) cg: TradingPair (39K), ContractDeployment (3K), Category (775), DexPool (40) cr: Activity (1K), TradingPair (998), Fund (543), Launchpad (179), Ecosystem (106), Person (50) dll: Pool (16K), Stablecoin (424) gh: Contribution (121K), GitHubUser (18K), Repository (11K) gov: GovernanceProposal (106K), GovernanceSpace (5K), GovernorContract (404) oracle: OracleIntegration (92), OracleProvider (83) sec: Filing (1K), RiskFactor (664), FundPortfolioHolding (469), InsiderTransaction (237), MaterialEvent (72) tt: ProjectContract (148K), TokenHolder (88K), TrendingContract (49K), InsiderTransaction (39K), Cohort (16K), Product (15K), Asset (1K), Metric (230) Always query in the dk: namespace — sameAs reasoning across source namespaces is materialised, so dk: subsumes cg:/cr:/dll:/tt: variants. Cost: $0.1 per call (x402 / USDC on Base)

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  {
    "type": "object",
    "required": [
      "query"
    ],
    "properties": {
      "query": {
        "type": "string"
      }
    }
  }

  arguments 11 lines copy
• get_entity_insights reads unknown 16h ago

  Get all insights for a specific entity — full signals with evidence + full narrative bodies. Browse free listings with query_insights and get_narratives first to find entities worth investigating. Default lookback is 7 days (168 hours). Parameters: entity_name: Entity name, fragment (e.g. 'token/aave'), or full URI (e.g. 'https://schema.dyor.bot/stablecoin/315') since_hours: Look back window in hours (default: 168) Cost: $1.5 per call (x402 / USDC on Base)

  mcp-tool

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  {
    "type": "object",
    "required": [
      "entity_name"
    ],
    "properties": {
      "entity_name": {
        "type": "string"
      },
      "since_hours": {
        "type": "number",
        "default": 168
      }
    }
  }

  arguments 15 lines copy
• get_insight reads unknown 16h ago

  Get a single Insight (free). Returns tagline, signal_summary_md, thesis_md, diff_md, active signal types, and active+closed episode snapshots. Same data shown on /demo/insights/<slug>. Parameters: entity: an insight id (e.g. 'ins_1788764887_5a1bf26e') to fetch that EXACT insight, or an entity slug/URI substring (e.g. '1inch' or 'token/aave') to fetch that entity's latest insight.

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  {
    "type": "object",
    "required": [
      "entity"
    ],
    "properties": {
      "entity": {
        "type": "string"
      }
    }
  }

  arguments 11 lines copy
• screen_fields reads unknown never probed

  The field catalog for screen_entities: every screenable field grouped by domain (market, fundamentals, tvl, unlocks, signals) with its unit, source, and the operators it supports, plus the live list of active signal types. Call this before screen_entities to construct valid filters.

  mcp-tool

  1234

  {
    "type": "object",
    "properties": {}
  }

  arguments 4 lines copy
• semantic_search reads unknown 22h ago

  Search for entities in the knowledge graph by name or semantic meaning. Combines exact/partial name matching with vector similarity (embeddings). Name matches are prioritized over vector results. Returns matching entities with URIs, names, labels, and similarity scores. Use this tool FIRST to find exact entity names before querying with query_sparql or explore_entity. Use `entity_types` to filter results (e.g., ['Token'], ['Protocol', 'Chain']). Cost: $0.1 per call (x402 / USDC on Base)

  mcp-tool

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  {
    "type": "object",
    "required": [
      "query"
    ],
    "properties": {
      "limit": {
        "type": "integer",
        "default": 10
      },
      "query": {
        "type": "string"
      },
      "entity_types": {
        "anyOf": [
          {
            "type": "array",
            "items": {
              "type": "string"
            }
          },
          {
            "type": "null"
          }
        ],
        "default": null
      }
    }
  }

  arguments 29 lines copy
• explore_entity reads unknown never probed

  Explore an entity's relationships and context in the knowledge graph. Given an entity name (and optional type), discovers its relationships across multiple categories. Returns structured data with surprise scores indicating unexpected/valuable cross-source connections. Valid `focus` categories (pass as list to limit exploration): identity — Cross-source entity equivalence (dk:canonicalizedFrom) investment — Investment and funding relationships lineage — Technical lineage, forks, and protocol relationships defi — DeFi relationships (yield pools, tokens) ecosystem — Chain and ecosystem context financial_metrics — TokenTerminal financial metrics (revenue, fees, users, TVL) insider_activity — TokenTerminal insider transactions and token holders market — Market presence, exchanges, and categories airdrops — Airdrop and drophunting activities github — Source repositories, their contributors and popularity Tip: Use semantic_search first to find the exact entity name, then pass it to explore_entity for detailed context. Cost: $0.1 per call (x402 / USDC on Base)

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  {
    "type": "object",
    "required": [
      "entity_name"
    ],
    "properties": {
      "focus": {
        "anyOf": [
          {
            "type": "array",
            "items": {
              "type": "string"
            }
          },
          {
            "type": "null"
          }
        ],
        "default": null
      },
      "entity_name": {
        "type": "string"
      },
      "entity_type": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null
      }
    }
  }

  arguments 36 lines copy
• query_timeseries reads unknown 22h ago

  Execute a read-only SQL query against PostgreSQL time-series tables. Query ts_* tables directly by name. Results limited to 500 rows. Only SELECT queries are allowed. Standard PostgreSQL SQL syntax. All tables have a `date` column (DATE) and an entity identifier column (VARCHAR). IMPORTANT: Column names with camelCase MUST be double-quoted in SQL. Example: SELECT "currentPrice", "marketCap" FROM ts_coingecko_token Example: Top tokens by market cap: SELECT entity_id, "currentPrice", "marketCap" FROM ts_coingecko_token WHERE date = CURRENT_DATE ORDER BY "marketCap" DESC LIMIT 10 Tip: Use CURRENT_DATE for today, CURRENT_DATE - INTERVAL '7 days' for a week ago. Use get_current_time() if you need the exact current timestamp. Cost: $0.1 per call (x402 / USDC on Base)

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  {
    "type": "object",
    "required": [
      "sql_query"
    ],
    "properties": {
      "sql_query": {
        "type": "string"
      }
    }
  }

  arguments 11 lines copy
• get_narratives reads unknown 7h ago

  Browse LLM-generated narratives about crypto entities (free, limited fields). Returns narrative headlines and scores WITHOUT full body text. Use get_entity_insights ($1) for the complete narrative on a specific entity. Parameters: since_hours: Look back window in hours (default: 72) limit: Max results (default: 50) entity: Filter by entity name (substring match)

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "limit": {
        "type": "integer",
        "default": 50
      },
      "entity": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null
      },
      "since_hours": {
        "type": "number",
        "default": 72
      }
    }
  }

  arguments 24 lines copy
• screen_entities reads unknown never probed

  Cross-domain screener: filter crypto entities across market, fundamentals, TVL, unlocks, and live signals in ONE query — the screen no single data source can run. Call screen_fields first to get the exact field names, units, and operators, then pass filters here (all filters are ANDed). Parameters: filters: list of {field, op, value}, e.g. [{"field":"revenue_30d_annualized","op":">","value":1000000},{"field":"ps_circulating","op":"<","value":30}] sort_field: field to sort by (default: market_cap) sort_dir: 'desc' (default) or 'asc' limit: max rows (default 50) Example prompt: 'screen for protocols with revenue over $1M, P/S under 30, and an unlock under 5% of supply in the next 30 days.'

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  {
    "type": "object",
    "required": [
      "filters"
    ],
    "properties": {
      "limit": {
        "type": "integer",
        "default": 50
      },
      "filters": {
        "type": "array",
        "items": {
          "type": "object",
          "additionalProperties": true
        }
      },
      "sort_dir": {
        "type": "string",
        "default": "desc"
      },
      "sort_field": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null
      }
    }
  }

  arguments 34 lines copy
• narrative_rollup reads unknown never probed

  Full cross-domain rollup for ONE narrative/sector: combined revenue, earnings, median valuation, TVL, market cap; funding into the sector + most-active investors; unlock overhang; live convergences; top movers; and the member roster. The Σ-revenue + median-P/S headline is computable by no single source. Parameters: slug: narrative slug from list_narratives (e.g. 'real-world-assets', 'liquid-staking', 'dexes', 'depin', 'ai-agents') Example prompt: 'what's the state of the RWA narrative — revenue, TVL, funding?'

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  {
    "type": "object",
    "required": [
      "slug"
    ],
    "properties": {
      "slug": {
        "type": "string"
      }
    }
  }

  arguments 11 lines copy
• calendar_events reads unknown never probed

  Forward-looking cross-domain calendar: upcoming token unlocks, recent funding rounds, and governance votes on one timeline, each event annotated with who's holding, its valuation, and active signals — not just a bare date. Parameters: kind: 'all' (default), 'unlocks', 'funding', or 'governance' from_date / to_date: YYYY-MM-DD window (optional; sensible default window) entity: scope to one entity slug, e.g. 'token/aptos' (optional) Example prompt: 'what token unlocks are coming in the next two weeks, and who's holding the unlocking supply?'

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "kind": {
        "type": "string",
        "default": "all"
      },
      "entity": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null
      },
      "to_date": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null
      },
      "from_date": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null
      }
    }
  }

  arguments 42 lines copy
• compare_entities reads unknown never probed

  Compare 2-4 crypto entities side by side across every domain at once — market, fundamentals, TVL, unlocks, and signals — with the stronger value on each row flagged. Commodity metrics made cross-domain. Parameters: ids: 2-4 entity slugs or URIs, e.g. ['token/aave','token/uniswap','protocol/lido'] Example prompt: 'compare Aave, Uniswap and Lido on revenue, P/S and TVL.'

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  {
    "type": "object",
    "required": [
      "ids"
    ],
    "properties": {
      "ids": {
        "type": "array",
        "items": {
          "type": "string"
        }
      }
    }
  }

  arguments 14 lines copy
• get_signals_by_type reads unknown never probed

  Get signals of a specific type with full evidence and metadata. 28 signal types covering insider accumulation, whale activity, sector rotation, yield anomalies, utilization stress, governance risk, token unlocks, and more. Use the free query_insights tool first to browse signal summaries, then use this tool to get full evidence for a specific signal type. Parameters: signal_type: Signal type slug (e.g. 'revenue_acceleration', 'yield_anomaly') since_hours: Look back window in hours (default: 72) limit: Max results (default: 50) qualification: 'qualified' (default; matches qualified OR screened), 'screened', 'pending', 'rejected', or 'all' channel: Signal channel; default 'user'. Pass 'all' to include internal-only types. Cost: $0.2 per call (x402 / USDC on Base)

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  {
    "type": "object",
    "required": [
      "signal_type"
    ],
    "properties": {
      "limit": {
        "type": "integer",
        "default": 50
      },
      "channel": {
        "type": "string",
        "default": "user"
      },
      "signal_type": {
        "type": "string"
      },
      "since_hours": {
        "type": "number",
        "default": 72
      },
      "qualification": {
        "type": "string",
        "default": "qualified"
      }
    }
  }

  arguments 27 lines copy
• batch_entity_insights reads unknown never probed

  Portfolio-level risk analysis: get comprehensive signals and narratives for up to 10 crypto entities in a single call. Returns full evidence across all 28 signal types per entity. Cheaper than calling get_entity_insights ($1) individually for multiple entities. Parameters: entities: List of entity names to analyze (max 10) since_hours: Look back window in hours (default: 168) Cost: $7.5 per call (x402 / USDC on Base)

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  {
    "type": "object",
    "required": [
      "entities"
    ],
    "properties": {
      "entities": {
        "type": "array",
        "items": {
          "type": "string"
        }
      },
      "since_hours": {
        "type": "number",
        "default": 168
      }
    }
  }

  arguments 18 lines copy
• get_risk_profile reads unknown 16h ago

  Get the ZWING risk profile for one entity. For an ASSESSED asset: the nine-dimension assessment the app renders — per-dimension band or an explicit not_assessed_reason, control locus, rationale, human-review flag, evidence count — plus the evidence chain (integrity hash, built/retention dates, provenance sources) and the monitor view (worst band, reliability, current findings incl. supersessions). For any other entity: an explicit structured answer, never fabricated depth — status 'not_assessed' with what the graph knows about the entity, or 'unknown_entity'. Absence of an assessment implies NO risk level. Every answer names the entity it resolved to (resolution / subject_uri); a name resolves to the top-ranked search hit only. Parameters: entity_name: Entity name (e.g. 'Aave'), fragment ('token/aave'), dk: CURIE, or full URI ('https://schema.dyor.bot/token/aave') Cost: $1.5 per call (x402 / USDC on Base)

  mcp-tool

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  {
    "type": "object",
    "required": [
      "entity_name"
    ],
    "properties": {
      "entity_name": {
        "type": "string"
      }
    }
  }

  arguments 11 lines copy

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.