FlexSim MCP Server

by FelipeDiazAimar

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Python

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flexsim_fluid_level

Reads a FlexSim FluidTank level in read-only mode, returning numeric metrics such as content value or average as JSON.

Instructions

Read a FluidTank level, read-only (window or Python backend via run_script).

Solo lectura (ventana o Python dual via run_script, como
flexsim_get_statistics): nunca escribe. Prueba en orden, con variable
tipada (AGENTE_FLEXSIM.md sec 7: Object k = Model.find(..)):
1. k.stats.content.value, 2. k.stats.content.average. NO inventa
formulas (stats.output.value menos stats.input.value PROHIBIDO).
Propiedades verificadas en el repo (sec 6):
InterArrivalTime/ProcessTime/SetupTime/MaxContent/BatchSize; el nivel
solo se lee de vuelta (roundtrip) como numero.

Args:
    name: tanque a leer (Model.find debe resolverlo).

Returns JSON {name, metrics, note} con metrics solo con los que
resolvieron a numero (p. ej. {"content.value": 12.5}). Si ninguno
resuelve, error claro con lo intentado (tried) sin ceros inventados.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`params`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 5, 2026

TDQS

A3.9/5.0

Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so well: it states 'nunca escribe' (never writes), specifies a fallback read order, forbids a particular incorrect formula, documents a verified roundtrip behavior, and describes error handling with 'tried' values and no invented zeros.

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

Conciseness3/5

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

The description is front-loaded with the core purpose, but it is lengthy and repeats the same information in English and Spanish. The bilingual duplication and nested procedural details make it less concise than necessary, though the technical content is mostly relevant.

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?

Given no annotations, an output schema, and a single-parameter read operation, the description is nearly complete: it covers read-only behavior, backend context, fallback attempts, and error semantics. Minor gaps remain around when to prefer this over sibling tools, but the core call is well specified.

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

Parameters4/5

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

The input schema gives no description for the single 'name' parameter (0% coverage), but the prose adds meaningful semantics: 'name: tanque a leer (Model.find debe resolverlo)'. This tells the agent what the parameter represents and how it must be resolved, compensating for the schema gap.

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?

The description opens with a specific verb and resource: 'Read a FluidTank level, read-only'. It clearly separates this from write operations and mentions the dual backend, but it does not directly compare with the sibling flexsim_fluid_tank tool, so sibling differentiation is incomplete.

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?

It provides procedural guidance for reading the level (try k.stats.content.value then average, never invent formulas) and says it works via window or Python backend. However, it does not state when to choose this tool over alternatives such as flexsim_get_statistics or flexsim_fluid_tank, leaving usage context implied rather than explicit.

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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