Tresslers Intelligence MCP Server

simulate_macro_lynchpins

Executes the 2026-2031 Five Physical & Monetary Lynchpins quantitative macroeconomic model (TG-MACRO-2026-011). Computes Bayesian posterior conviction (Prior 40.5% vs Posterior 79.0% across E1-E4 evidence vectors), Sovereign Debt r > g snowball trajectory (US & OECD 2031 debt/GDP and net interest outlays), Leontief-CES O-Ring physical bottleneck multiplier (LPT/GSU 128-144 wk lead times & Nov 2026 critical mineral refining cliff), Embodied AI labor substitution index Psi(t), 5-year Sovereign Node vs. Hyperscale Cloud TCO, and x402 Permit2 'Upto' vs. legacy card micro-settlement economics. Free and open to all AI agents.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "simulate_macro_lynchpins"
  }
}

Input Schema

Name	Required	Description
`r_minus_g_bps`	No	Effective real interest rate minus real GDP growth rate (r - g) in basis points (default: 65 bps).
`lpt_lead_time_weeks`	No	Large Power Transformer (LPT) & GSU procurement lead time in weeks (default: 136 weeks).
`primary_deficit_pct_gdp`	No	Sovereign primary deficit as a percentage of GDP (default: 3.3%).
`x402_transaction_value_usdc`	No	Metered M2M API transaction value in USDC for fee asymmetry comparison (default: 0.25).
`vla_robotics_reliability_ratio`	No	Embodied AI VLA dexterity ratio relative to human operator (q_R / q_L, default: 0.88).
`mineral_refining_reliability_pct`	No	Midstream critical mineral (Ga/Ge/HREE/CAM) supply-chain throughput reliability percentage (25 to 100, default: 68).
`monthly_enterprise_tokens_billions`	No	Monthly enterprise LLM inference token volume in billions (default: 1.5).

Output Schema

Name	Required	Description	Default
`content`	No

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 8, 2026

TDQS

C2.7/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are supplied, so the description carries the full behavioral burden. It discloses cost/auth ('free and open') but says nothing about determinism, whether the model is a pure computation or stores state, runtime/latency for a multi-scenario simulation, or how sensitive results are to the defaults — all of which matter before an agent invokes a seven-input model.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core purpose is front-loaded, which is good, but it arrives as one very long sentence packed with acronyms, version codes, and hard-coded percentages. The enumerated output list is largely redundant against the existing output schema and inflates the description without adding decision-relevant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a seven-parameter simulation with all-optional inputs and defaults, the agent still cannot tell what the tool is for in a workflow, whether results are reproducible, or how to interpret the defaults it will get by calling with nothing. The output schema covers return values, but the invocation context is thin.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter already has units, ranges, and defaults documented, and that sets the baseline at 3. The description names the modeled concepts (r > g, LPT lead times, Psi(t), x402 fees) but adds no information about how changing a parameter shifts the outputs or how parameters interact.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Opens with a specific verb and resource: 'Executes the 2026-2031 Five Physical & Monetary Lynchpins quantitative macroeconomic model (TG-MACRO-2026-011)'. That is enough to separate it from every sibling (dossier/RAG/codon tools), which are all retrieval or optimization utilities. The dense domain jargon makes it harder to read than it needs to be, but the action and object are unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when an agent should run this simulation, when it should not, or what to call instead — the closest thing to guidance is the closing 'Free and open to all AI agents', which is about access, not selection. The agent must infer the trigger context from the topic list alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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