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flexsim_table_read

Read a FlexSim GlobalTable as JSON by table name, with row and column limits, returning cells, dimensions, and truncation status for model data access.

Instructions

Read a GlobalTable as JSON {table, rows, cols, total_rows, total_cols, truncated, data}.

Reads Table("<table>")[r][c] in row batches (braces work inside executestring,
AGENTE_FLEXSIM.md sec 3c) and joins cells with "|" and rows with ";" (the same join
style flexsim_inspect_model already relies on). Numeric cells come back as numbers,
the rest as strings.

Limits (documented, no silent magic): string cells containing "|" or ";" split
ambiguously (a "warning" field is added when the split does not match the table
dims); bundle/object cells read as their text form. Tables larger than max_rows x
max_cols return the top-left window with truncated=True.

Args:
    table: GlobalTable name (Tools/GlobalTables/<table>).
    max_rows: cap 1-100 (default 100).
    max_cols: cap 1-20 (default 20).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations, the description carries full burden and delivers: row-batch reads, cell/row join delimiters, numeric vs string typing, ambiguous-split handling with a warning field, bundle/object text-form reads, and explicit truncation behavior. This is unusually rich behavioral disclosure.

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 return shape, then behavior, then limits, then args - a clear structure. It is dense but every sentence carries distinct information; nothing is filler.

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

Completeness5/5

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

For a read tool with an output schema, the description still explains the truncated flag, the join semantics, and edge cases, so an agent understands exactly what comes back and when data is partial. Nothing needed to call it correctly is missing.

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?

Schema coverage is 0%, so the description must compensate, and it does: table is defined as a GlobalTable resolved at Tools/GlobalTables/<table>, and max_rows/max_cols are given caps and defaults. The schema already supplies the min/max/default constraints, so this adds the missing semantic layer without redundancy.

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 ("Read") and resource ("GlobalTable") plus the exact JSON shape returned. An agent can distinguish it from flexsim_table_write and flexsim_table_from_snapshot without opening any schema.

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?

Usage is implied by the resource (reads a named GlobalTable) and it references flexsim_inspect_model's join style as prior art, but it never states when to prefer this tool over siblings like flexsim_table_write or flexsim_table_from_snapshot, nor any prerequisites. Adequate but with clear gaps.

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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