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OT Intel API

Physical Invariant Check

getInvariantReachability
Read-only

Checks whether a tank/vessel's reported level change is physically consistent with its reported flow-in/flow-out via conservation of mass. Fully deterministic, no LLM, no trained model. Inspired by Barbhaya et al. 2025's physical-invariant residual check — flags inconsistency and direction, does not assert a specific cause. Price: $0.20 USDC per call via x402 on Base mainnet. An unpaid call returns an x402 PaymentRequired challenge; retry the identical call with the signed payload in _meta["x402/payment"].

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
flow_inYesMetered inflow rate
asset_idYesIdentifier for the tank/vessel being checked
flow_outYesMetered outflow rate
level_endYesLevel reading at end of interval
tank_areaYesCross-sectional area, must be > 0
dt_secondsYesInterval duration in seconds, must be > 0
level_startYesLevel reading at start of interval
noise_floorNoResidual magnitude below which reading is treated as consistent

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior5/5

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

Goes well beyond the annotations: it declares full determinism and no LLM/trained model, defines the output nature (flags inconsistency and direction, not a cause), and — most importantly — discloses the x402 payment requirement, the $0.20 USDC price, and the exact retry protocol for the PaymentRequired challenge. That is exactly the kind of auth/payment context annotations cannot carry.

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

Conciseness4/5

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

Front-loaded with the core purpose, then determinism, scope, and the payment/retry mechanics. Dense but every sentence carries weight; the payment paragraph is slightly verbose but operationally necessary for a paid tool.

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

Completeness4/5

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

With no output schema, the description steps in to characterize the return (inconsistency and direction flagged) and covers the invocation prerequisites. For a single-check tool with all params documented, this is close to complete, though the exact output shape is only roughly sketched.

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 the schema already documents all 8 parameters (including the noise_floor default and >0 constraints). The description adds no parameter-level syntax or format meaning beyond that, so the baseline 3 applies.

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

Purpose5/5

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

States a specific verb (checks) and resource (tank/vessel level change vs flow-in/flow-out via conservation of mass) with unusual precision, and the method (physical-invariant residual check) is named. An agent can immediately tell this is a physical consistency validator rather than any of the cyber/threat-intel siblings.

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

Usage Guidelines3/5

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

Implied usage is clear — use it to test physical consistency of tank readings — and the scope note (flags inconsistency and direction, does not assert a cause) usefully bounds expectations. However there is no explicit when-to-use/when-not guidance or named alternative among siblings, so guidance remains inferred rather than stated.

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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