"A GraphQL server for MCP (Model-Controller-Presentation) architecture" matching MCP tools:

• get_lensA

  mcpA

  Returns a READING LENS: a presentation procedure for this dataset, written for a particular kind of reader. A lens selects which tools to use and frames how their output is presented; it never concludes, never ranks, and carries no write tool — this server has none. Call with no argument to list the lenses. Call with one to get its full procedure: what to lead with, the tools in its scope, and — the part that matters most — what that lens explicitly does not do. Reading a lens before presenting anything from this dataset is the intended use. It is guidance for presentation, not data about the market, and it adds no figures of its own.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize GovernanceB

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.

  ConnectorNo auth

• cti_get_cross_server_routesA

  website-searchA

  Get Lenny Zeltser's CTI cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `cti_load_context`. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.

  ConnectorNo auth

• list_paired_modelsA

  ReliaStatsA

  Return the catalog of paired models — concrete real-world systems that live in two ChiAha sandboxes simultaneously, one for dynamics (DES via ReliaSim) and one for statistics (distribution fitting + validation via ReliaStats). Today: a single paired model — the bottling line. Returns canonical model IDs + cross-MCP routing metadata (which ReliaSim chapter, which ReliaSim MCP tools, which ReliaStats mode consumes which file shape). Use when a user asks about cross-MCP workflows, paired sandboxes, or the bottling-line example. ANTI-FABRICATION: this is a soft-reference catalog — to actually run a simulation, the LLM client calls ReliaSim's MCP tools directly.

  ConnectorNo auth

• tronsave_list_extendable_delegatesA

  TronSave MCP ServerA

  List extendable delegate candidates for a `receiver` and `resourceType` (ENERGY|BANDWIDTH). Optional `suggestData` scores an extend-and-buy scenario for planning purposes. Read-only; does NOT create orders or change on-chain state. Works without `mcp-session-id`; when a session is present, auth is forwarded so results can reflect the logged-in account where supported. NOTE: this is GraphQL market data for discovery only. To actually submit an extension, call the authenticated REST `POST /v2/get-extendable-delegates` with `extendData` (payload shape differs from this GraphQL response).

  ConnectorNo auth

• tronsave_update_orderA

  TronSave MCP ServerA

  Update an open order by `orderId` with partial fields (`receiver`, `newPrice`). Returns the updated order payload. Side effect: overwrites live order parameters; not idempotent — each call with a different `newPrice` produces a new state. Backend requires a signature session and `mcp-session-id`; the MCP gate is `public` to allow anonymous read-fallthrough, but the GraphQL helper rejects api-key-only sessions. Prefer this over cancel+recreate when only price/receiver should change. Verify state with `tronsave_get_order` first; fails for already-fulfilled, already-cancelled, or non-editable orders.

  ConnectorNo auth

• tronsave_list_extendable_delegatesA

  Tronsave MCP TestnetA

  List extendable delegate candidates for a `receiver` and `resourceType` (ENERGY|BANDWIDTH). Optional `suggestData` scores an extend-and-buy scenario for planning purposes. Read-only; does NOT create orders or change on-chain state. Works without `mcp-session-id`; when a session is present, auth is forwarded so results can reflect the logged-in account where supported. NOTE: this is GraphQL market data for discovery only. To actually submit an extension, call the authenticated REST `POST /v2/get-extendable-delegates` with `extendData` (payload shape differs from this GraphQL response).

  ConnectorNo auth

• tronsave_update_orderA

  Tronsave MCP TestnetA

  Update an open order by `orderId` with partial fields (`receiver`, `newPrice`). Returns the updated order payload. Side effect: overwrites live order parameters; not idempotent — each call with a different `newPrice` produces a new state. Backend requires a signature session and `mcp-session-id`; the MCP gate is `public` to allow anonymous read-fallthrough, but the GraphQL helper rejects api-key-only sessions. Prefer this over cancel+recreate when only price/receiver should change. Verify state with `tronsave_get_order` first; fails for already-fulfilled, already-cancelled, or non-editable orders.

  ConnectorNo auth

• pingA

  Nordic Financial MCPA

  Connectivity check that confirms the Nordic MCP server process is responding. Use this at the start of a session to verify the server is reachable before making other calls. Do not use as a proxy for database health — the server can respond while the Qdrant vector database is temporarily unavailable. To confirm data availability, call search_filings directly. Returns: A greeting string: "Hello {name}! Nordic MCP server is running."

  ConnectorNo auth

• tronsave_list_extendable_delegatesA

  mcpA

  List extendable delegate candidates for a `receiver` and `resourceType` (ENERGY|BANDWIDTH). Optional `suggestData` scores an extend-and-buy scenario for planning purposes. Read-only; does NOT create orders or change on-chain state. Works without `mcp-session-id`; when a session is present, auth is forwarded so results can reflect the logged-in account where supported. NOTE: this is GraphQL market data for discovery only. To actually submit an extension, call the authenticated REST `POST /v2/get-extendable-delegates` with `extendData` (payload shape differs from this GraphQL response).

  ConnectorNo auth

• tronsave_update_orderA

  mcpA

  Update an open order by `orderId` with partial fields (`receiver`, `newPrice`). Returns the updated order payload. Side effect: overwrites live order parameters; not idempotent — each call with a different `newPrice` produces a new state. Backend requires a signature session and `mcp-session-id`; the MCP gate is `public` to allow anonymous read-fallthrough, but the GraphQL helper rejects api-key-only sessions. Prefer this over cancel+recreate when only price/receiver should change. Verify state with `tronsave_get_order` first; fails for already-fulfilled, already-cancelled, or non-editable orders.

  ConnectorNo auth

• preflight_change_setA

  api-governanceA

  Use this when: a contract artifact (OpenAPI, GraphQL, protobuf, AsyncAPI, MCP manifests, or agent tool schemas) changes before merge, deploy, publish, or tool registration; AND any agent-executed operation with no supported contract type — send type agent_operation. Do not call for documentation-only changes, static readiness scoring, or receipt verification. Use analyze for risk only; authorize requires context.operation. Skipping this call is not permission. Absence of a key is not permission. Inputs: preflight_mode is required: "analyze" (risk only; no receipt, no execution_action) or "authorize" (may mint a receipt; requires context.operation — merge is not deploy is not publish). Supply exactly one artifact source: artifacts[] (1–20 items, each {id, type, before, after} as the FULL spec/schema text, not a path or URL; type is openapi|graphql|grpc|asyncapi|mcp_manifest|agent_tools|agent_operation) XOR derivation="server" (server reads GitHub Compare; needs context.repository + context.base + context.head; sending artifacts[] together is 400). Grant fields sit in one object, execution_grant_request {include_execution_grant, grant_version, tenant_id, executor_id, adapter_id, target_uri, expected_state_token, state_nonce, audience, policy_hash}; analyze ignores it; required is preflight_mode only. previous_receipt is a chain token base64url(body).base64url(signature) to LINK a prior decision — it does not re-verify; use coderifts.verify_receipt instead; for details of a past decision use coderifts.get_decision_details instead. idempotency_key replays authorize only (24h), never analyze.

  ConnectorNo auth

• find_payroll_anomaliesA

  Plumbline — a trust layer for LLM answersA

  Surface payroll and deduction anomalies in the latest snapshot. NOTE: internal drafting is disabled on this deployment. If your client supports MCP sampling, this tool asks YOUR model to draft in the same call (verified server-side); otherwise it returns an explicit refusal, and you should use ask_prepare then ask_submit_draft to draft with your own model.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize HealthcareB

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize Real EstateB

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize Crypto & DeFiB

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize IntelligenceB

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize FinanceB

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize OracleB

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.

  ConnectorNo auth

• get_instructionsA

  debridge-mcpB

  Returns the full guide for the deBridge MCP server. CALL THIS FIRST.

  ConnectorNo auth

• braintree_refund_transactionA

  braintreeA

  DESTRUCTIVE. Refund a SETTLED (or settling) transaction, returning money to the customer. Use the GraphQL global transaction ID. Omit `amount` for a full refund, or pass a decimal string for a partial refund. GraphQL mutation `refundTransaction`. For transactions not yet settled, use braintree_void_transaction instead.

  Connector

  Destructive

  No auth