elicit_polars 0.11.1

Elicitation-enabled polars shadow crate — MCP tools for DataFrame, Expr, pipeline, and SQL
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elicit_polars

MCP shadow crate for polars 0.53 — provides ~75 Model Context Protocol tools across four plugins for DataFrame operations, Expr composition, pipeline code generation, and SQL execution.

Plugins

PolarsExprPlugin — polars_expr__* (~37 tools)

Composes polars Expr values server-side. Every tool stores a real Expr and its Rust source string; emit returns the DSL code for downstream code generation.

Tool Params Description
col name col("name")
lit_int value: i64 lit(v)
lit_float value: f64 lit(v)
lit_str value lit("v")
lit_bool value lit(true/false)
all_columns — polars::prelude::all()
first_col — col("*").first()
last_col — col("*").last()
count_all — polars::prelude::len()
eq/neq/gt/lt/gte/lte left_id, right_id Binary comparisons
and/or left_id, right_id Logical ops
not expr_id Negation
sum/mean/min/max/count/n_unique/median expr_id Aggregations
std/var expr_id, ddof Std dev / variance
first_val/last_val expr_id First/last value
str_contains expr_id, pattern .str().contains(lit(p), false)
str_starts_with/ends_with expr_id, pattern String prefix/suffix
str_to_lowercase/uppercase expr_id Case conversion
str_replace expr_id, pattern, replacement String replace
dt_year/month/day/hour expr_id Temporal extraction
alias expr_id, name .alias("name")
cast expr_id, dtype .cast(DataType::...)
fill_null_with_zero expr_id .fill_null(lit(0))
sort_expr expr_id, descending .sort(SortOptions)
is_null/is_not_null expr_id Null checks
describe expr_id Show stored code
emit expr_id Return Rust DSL source string
list — List all stored exprs

PolarsDataFramePlugin — polars_df__* (~23 tools)

Stores live DataFrame values by UUID. I/O ops work with files; the shared SharedExprRegistry (from PolarsExprPlugin) powers filter, select, etc.

Tool Params Description
read_csv path, has_header Read CSV to DataFrame
read_parquet path Read Parquet
read_json path Read JSON/NDJSON
from_json_string json Deserialise from JSON string
schema df_id Column names and types
shape df_id {rows, cols}
head df_id, n? First N rows as JSON array
to_json_string df_id, n? Serialise to JSON
select df_id, columns Keep named columns
filter df_id, expr_id Filter via stored Expr
with_columns df_id, expr_ids Compute new columns
sort df_id, by, descending Sort multi-column
group_by_agg df_id, by_ids, agg_ids GroupBy + aggregate
join df_id, right_id, left_on_ids, right_on_ids, how Join
unique df_id, subset? Deduplicate rows
drop_nulls df_id, subset? Drop null rows
rename_column df_id, old_name, new_name Rename column
drop_column df_id, name Drop column
write_csv df_id, path Write CSV
write_parquet df_id, path Write Parquet
write_json df_id, path Write JSON
write_ipc df_id, path Write Arrow IPC
list — List stored DataFrame UUIDs

PolarsPipelinePlugin — polars_pipeline__* (~7 tools)

Pure code generation — no live polars data. Stores PolarsPipelineDescriptor values and emits idiomatic Rust main.rs source from them.

Tool Params Description
new name Create pipeline descriptor
add_step pipeline_id, op Append a PolarsPipelineOp
remove_step pipeline_id, step_id Remove a step
clear pipeline_id Remove all steps
describe pipeline_id Inspect pipeline
emit_main pipeline_id Generate fn main() Rust source
list — List all pipeline UUIDs

PolarsSqlPlugin — polars_sql__* (~5 tools)

Wraps polars SQLContext. The shared SharedDfRegistry allows registering DataFrames created by PolarsDataFramePlugin as SQL tables.

Tool Params Description
new_context — Create a new SQLContext
register ctx_id, table_name, df_id Register a DataFrame as a table
execute ctx_id, query Run SQL, store result as DataFrame
describe ctx_id List registered table names
list — List all context UUIDs

Usage

use elicit_polars::{PolarsExprPlugin, PolarsDataFramePlugin, PolarsPipelinePlugin, PolarsSqlPlugin};

// Wire up shared registries
let expr_plugin = PolarsExprPlugin::new();
let df_plugin = PolarsDataFramePlugin::new(expr_plugin.registry());
let sql_plugin = PolarsSqlPlugin::new(df_plugin.df_registry());
let pipeline_plugin = PolarsPipelinePlugin::new();

Register all four with your MCP server, then agents can compose polars pipelines through tool calls.

Architecture

  • Shared Expr registry (SharedExprRegistry = Arc<Mutex<HashMap<Uuid, (Expr, String)>>>): Both PolarsExprPlugin and PolarsDataFramePlugin hold a clone, so DataFrame tools can look up stored expressions.
  • Shared DF registry (SharedDfRegistry = Arc<Mutex<HashMap<Uuid, DataFrame>>>): Both PolarsDataFramePlugin and PolarsSqlPlugin hold a clone, so SQL tools can access stored DataFrames.
  • Code tracking: Every PolarsExprPlugin tool stores both the live Expr (for runtime) and a Rust code string (for emit). No reflection over the Expr AST is needed.
  • Pipeline code gen: PolarsPipelinePlugin is pure descriptor storage with an emit_main tool that walks PolarsPipelineOp variants and produces a complete fn main().

Cargo features

Enable the following polars features in your project's Cargo.toml:

polars = { version = "0.53", features = [
    "lazy", "serde", "serde-lazy",
    "csv", "parquet", "json", "ipc",
    "dtype-full", "strings", "temporal", "sql",
] }

The elicit_polars crate enables all of the above through its own polars dependency.