# Sovereign Debt Crisis MCP (`ryanclinton/sovereign-debt-crisis-mcp`) Actor MCP intelligence server for sovereign debt crisis detection and analysis. - **URL**: https://apify.com/ryanclinton/sovereign-debt-crisis-mcp.md - **Developed by:** [ryan clinton](https://apify.com/ryanclinton) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event + usage This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Sovereign Debt Crisis MCP A Model Context Protocol server implementing an endogenous political economy sovereign default simulator. Fuses macroeconomic, trade, FX, regulatory, and geopolitical data from **15 Apify actors** into a sovereign debt network, then applies game theory, econometrics, and mechanism design to simulate defaults, contagion, and political transitions. ### Tools (8) | ## | Tool | Method | Price | |---|------|--------|-------| | 1 | `simulate_sovereign_default` | Eaton-Gersovitz (1981) value function iteration on (b,y) state space | $0.045 | | 2 | `compute_clearing_contagion` | Eisenberg-Noe (2001) + CDS circularity (Scarf-type damped iteration) | $0.045 | | 3 | `estimate_fiscal_sustainability` | Hansen (1999) panel threshold regression with bootstrap inference | $0.035 | | 4 | `design_optimal_restructuring` | Myerson mechanism design + Nash-Rubinstein alternating offers | $0.040 | | 5 | `model_speculative_attack` | Morris-Shin (2003) global games with unique switching equilibrium | $0.040 | | 6 | `detect_crisis_jumps` | Lee-Mykland (2008) non-parametric jump detection via bipower variation | $0.035 | | 7 | `forecast_multi_frequency` | MIDAS regression (Ghysels 2004) with Almon exponential lag polynomials | $0.035 | | 8 | `assess_political_transition_risk` | Acemoglu-Robinson (2006) inequality-driven regime transitions | $0.035 | ### Data Sources (15 actors across 5 categories) **Macro (6):** IMF, World Bank, FRED, BLS, OECD, Eurostat **Trade (2):** UN COMTRADE, World Bank Projects **FX (3):** Exchange Rates, Historical Exchange Rates, ECB Exchange Rates **Regulatory (2):** Federal Register, Congress Bills **Geopolitical (2):** OpenSanctions, REST Countries ### Mathematical Foundations #### Tool 1: Sovereign Default — Eaton-Gersovitz Model The canonical model of strategic sovereign default. A small open economy with income y and debt b chooses between repayment and default: - **Continuation value:** V_c(b,y) = max_{b'} u(c) + β·E[max(V_c(b',y'), V_d(y'))] where c = y - b + q(b',y)·b' - **Default value:** V_d(y) = u(y^def) + β·E[θ·V_c(0,y') + (1-θ)·V_d(y')] - **Bond price:** q(b',y) = E[1{no default}]/(1+r_f) — endogenous risk pricing The default threshold, borrowing policy, and equilibrium spreads are all simultaneously determined as a fixed point. #### Tool 2: Clearing Contagion — Eisenberg-Noe with CDS Standard Eisenberg-Noe finds clearing payments via monotone fixed-point iteration (Tarski's theorem). CDS circularity breaks monotonicity: bank A's solvency depends on bank B paying CDS protection, but B's ability to pay depends on C, which depends on A. The non-monotone mapping requires **Scarf-type damped iteration** with a convergence rate of O(1/k) instead of the geometric convergence of standard methods. #### Tool 3: Fiscal Sustainability — Hansen Threshold Regression Tests whether fiscal behavior changes regime at a debt-to-GDP threshold γ. The model y_it = α_i + β₁·x·I(q≤γ) + β₂·x·I(q>γ) + ε classifies countries into sustainable vs. unsustainable fiscal regimes. The threshold is identified by minimizing concentrated SSR over a grid, with significance assessed via **Hansen bootstrap** (asymptotic distribution is non-standard — mixture of chi-squareds). #### Tool 4: Optimal Restructuring — Myerson + Rubinstein Combines mechanism design with bargaining theory: - **Myerson revelation principle**: Debtor type θ (capacity to pay) is private info. IC constraints require monotone transfer schedules. - **Nash bargaining**: max (u_d - d_d)^α · (u_c - d_c)^(1-α) gives fair surplus split. - **Rubinstein alternating offers**: First-mover share = (1-δ_c)/(1-δ_d·δ_c) where δ are discount factors. More patient player captures more surplus. #### Tool 5: Speculative Attack — Morris-Shin Global Games Resolves the **Obstfeld (1996) indeterminacy** (multiple equilibria in currency crises) via incomplete information. Speculators receive private signals x_i = θ + σ·ε_i about fundamentals θ. With finite signal precision (σ > 0), there exists a **unique switching equilibrium** θ*: speculators attack iff x_i < θ*, and the peg collapses iff θ < θ*. This is perhaps the most elegant application of global games to macroeconomics. #### Tool 6: Crisis Jumps — Lee-Mykland Test Non-parametric detection of jumps in CDS/spread data: - **Bipower variation** σ̂² estimates continuous volatility while being robust to jumps - Test statistic L_i = |Δlog(X_i)| / σ̂_i - Under H₀: (L_i - C_n)/S_n converges to **Gumbel distribution** - Centering: C_n = √(2·log(n)) - [log(π)+log(log(n))] / [2√(2·log(n))] #### Tool 7: Multi-Frequency Forecast — MIDAS Regression Bridges the frequency gap between daily financial data and quarterly macro data: - y_t^(Q) = β₀ + β₁ · Σ_k B(k;θ)·x_{t-k}^(D) + ε_t - **Almon exponential weights**: B(k;θ₁,θ₂) = exp(θ₁k+θ₂k²) / Σexp(...) - Parsimonious: 2 parameters govern the entire high-frequency lag structure - Nowcasting: current-quarter prediction using within-quarter high-freq data #### Tool 8: Political Transition — Acemoglu-Robinson Models political transitions as inequality-driven Markov dynamics: - **Elite regime**: Concedes redistribution when revolution threat θ exceeds threshold - **Democratic regime**: Median voter taxation at τ_m - **Coup risk**: Elite military capacity × (1 - cost of coup) - **Gini threshold**: Inequality crossing critical level triggers transition - Markov chain over {democratic, hybrid, authoritarian} with endogenous transition probabilities ### Architecture ```` Client ──► Express (port 3019) ──► McpServer factory │ ┌─────────────┼─────────────┐ ▼ ▼ ▼ SSE transport Streamable HTTP Health endpoint │ │ ▼ ▼ getOrBuildNetwork() (5-min TTL cache) │ ┌───────────┼───────────┐ ▼ ▼ ▼ 15 Apify actors Graph build 8 analysis tools (parallel fetch) (fusion) (per-request) ```` ### Usage ```bash npm install npm run build npm start ```` Environment variables: - `APIFY_TOKEN` — Apify API token for actor execution - `PORT` — Server port (default: 3019) ### Endpoints - `GET /sse` — SSE transport for MCP - `POST /messages?sessionId=...` — SSE message handler - `POST /mcp` — Streamable HTTP transport - `GET /health` — Health check with network stats # Actor input Schema ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("ryanclinton/sovereign-debt-crisis-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("ryanclinton/sovereign-debt-crisis-mcp").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call ryanclinton/sovereign-debt-crisis-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ryanclinton/sovereign-debt-crisis-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Sovereign Debt Crisis MCP", "description": "MCP intelligence server for sovereign debt crisis detection and analysis.", "version": "1.0", "x-build-id": "CwhOa8i2ESXy2aURe" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ryanclinton~sovereign-debt-crisis-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ryanclinton-sovereign-debt-crisis-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ryanclinton~sovereign-debt-crisis-mcp/runs": { "post": { "operationId": "runs-sync-ryanclinton-sovereign-debt-crisis-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ryanclinton~sovereign-debt-crisis-mcp/run-sync": { "post": { "operationId": "run-sync-ryanclinton-sovereign-debt-crisis-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": {} }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```