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execute_pandas_on_timeseries

Run pandas queries on EnergyPlus timeseries data by providing model_id, RDD IDs, and a pandas expression. Analyze the dataframe (df) to compute stats, groupby, or find peak values.

Instructions

Execute pandas operations on timeseries data from an EnergyPlus model.

Retrieves timeseries data for a specific variable and executes pandas operations on it. The dataframe is available as 'df' in your query.

Args: model_id: The model_id of the EnergyPlus model (obtain from get_available_models). rddid: A list of RDD IDs for the desired variable (obtain from get_sql_available_hourlies). query: Pandas query to execute (e.g., "df.describe()", "df['Value'].mean()")

Returns: String representation of the query result with formatted output.

Examples: # Get basic statistics execute_pandas_on_timeseries(model_id, rddid, "df.describe()")

# Get hourly averages by month
execute_pandas_on_timeseries(model_id, rddid, "df.groupby(df['dt'].dt.month)['Value'].mean()")

# Find peak values
execute_pandas_on_timeseries(model_id, rddid, "df.loc[df['Value'].idxmax()]")

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	Yes
`rddid`	Yes
`model_id`	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

A3.8/5.0

Behavior3/5

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

With no annotations provided, the description carries the behavioral transparency burden. It discloses that the tool retrieves timeseries data, executes a pandas query, provides 'df' inside the query, and returns a string representation. It does not mention side effects, safety implications of arbitrary pandas execution, or error behavior, but it does describe the core runtime behavior.

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 organized into summary, args, returns, and examples. It is slightly redundant in the first two sentences, but the structure is front-loaded and the examples are useful rather than filler.

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?

For a tool of moderate complexity with 0% schema coverage, the description covers prerequisites, dataframe naming, query expectations, return format, and usage examples. It is complete enough for an agent to call the tool correctly, though it could strengthen sibling differentiation and mention any limitations on acceptable pandas expressions.

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

Parameters5/5

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

Schema description coverage is 0%, so the description fully compensates by explaining every parameter: model_id's source, rddid's meaning and source, and query's syntax with multiple concrete examples. The examples show exactly how the query string is expected to reference 'df', 'Value', and 'dt', adding substantial semantic value beyond the raw 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 a specific verb and resource: executing pandas operations on EnergyPlus timeseries data. It makes the data available as 'df', which gives concrete operational meaning. It doesn't explicitly distinguish itself from sibling tools like execute_multiline_pandas_on_timeseries, so it misses the full sibling-differentiation bar.

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 implies when to use the tool by showing pandas-style query examples and explains prerequisites (obtain model_id from get_available_models, rddid from get_sql_available_hourlies). However, it does not explicitly state when to prefer this over alternatives such as execute_multiline_pandas_on_timeseries or get_timeseries_report_by_rddid_list, so usage guidance is present but not decisive.

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