eplusout-mcp

Overview Schema Source Related Servers Discussions Admin

execute_multiline_pandas_on_html_table

Execute multi-line pandas code on EnergyPlus HTML table data to analyze simulation results. Access the table as 'df' and assign output to 'result' to return values.

Instructions

Execute multi-line pandas code on HTML table data from an EnergyPlus model.

Retrieves an HTML table and executes multi-line pandas code on it. The dataframe is available as 'df' in your code. Use 'result = ...' to return values.

Args: id: The model_id of the EnergyPlus model (obtain from get_available_models). query_tuple: A tuple containing (zone/component, report_name, table_name) to identify the table. code: Multi-line Python code to execute

Returns: String representation of the result or execution status.

Examples: # Complex table analysis code = ''' # Convert numeric columns numeric_cols = df.select_dtypes(include=[np.number]).columns df_numeric = df[numeric_cols]

# Calculate totals and percentages
totals = df_numeric.sum()
percentages = (df_numeric / totals) * 100

result = percentages
'''
execute_multiline_pandas_on_html_table(model_id, query_tuple, code)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes
`code`	Yes
`query_tuple`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 21, 2026

TDQS

A3.5/5.0

Behavior3/5

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

No annotations are provided, so the description must carry full behavioral transparency. It discloses that the dataframe is available as 'df' and that you need to assign to 'result' to return values, which is helpful. However, it doesn't mention any potential side effects, execution sandboxing, or error handling behavior. It does state the return is a string representation, partially covering behavioral aspects.

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?

The description is well-structured: it starts with a one-line summary, then details the parameters, return type, and includes a relevant example. The example is not overly long but demonstrates complex usage. Some redundancy exists ('Execute multi-line pandas code' is repeated), but overall it's efficient and front-loaded with the core purpose.

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

Completeness3/5

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

The tool is moderately complex with 3 parameters and an output schema, but the description doesn't explain the output schema's contents. It does provide an example that clarifies usage. Missing details: what happens if code fails, how to handle large data, or what 'df' contains exactly. The example covers a common use case, but edge cases are not addressed.

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. It explains 'query_tuple' as a tuple of (zone/component, report_name, table_name), and 'code' is shown with examples that demonstrate usage. The 'id' parameter is only mentioned as 'model_id' but with a reference to get it from get_available_models. While not exhaustive, it provides meaningful meaning beyond the bare schema.

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 clearly states it executes multi-line pandas code on HTML table data and provides context about EnergyPlus. It differentiates from siblings by focusing on multi-line code versus single-line (execute_pandas_on_html_table) and HTML table versus timeseries (execute_multiline_pandas_on_timeseries).

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?

The description gives a specific scenario (complex table analysis) but doesn't explicitly state when not to use it or alternatives. It does mention obtaining model_id from get_available_models, which gives some usage context, but lacks exclusions like 'use execute_pandas_on_html_table for single-line code'.

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