SageMath MCP Server

Statistics Summary

statistics_summary

Idempotent

Computes descriptive statistics for a numeric list: mean, median, sample and population variance and standard deviation, min, and max.

Instructions

Descriptive statistics for a list of numbers: mean, median, population and sample variance and standard deviation, min and max. Prefer this over evaluate_sage for summary statistics.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`data`	Yes	List of numeric values
`session`	No	Workspace to use, as a name or a portable handle. Workspaces have independent variables. A name is scoped to this MCP session; a handle returned by start_sage_session (workspace_token) reaches the same workspace across reconnects and is a bearer credential -- keep it secret. Omit for 'default'.	default

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.7.0Sep 12, 2026

changedInput schema / properties / session / description

Previous value: -"Named workspace to use. Workspaces have independent variables; omit for 'default'."New value: +"Workspace to use, as a name or a portable handle. Workspaces have independent variables. A name is scoped to this MCP session; a handle returned by start_sage_session (workspace_token) reaches the same workspace across reconnects and is a bearer credential -- keep it secret. Omit for 'default'."

Changed1 schema field changedv0.5.0Aug 14, 2026

addedInput schema / properties / session

Added value: +{
+  "default": "default",
+  "description": "Named workspace to use. Workspaces have independent variables; omit for 'default'.",
+  "type": "string"
+}

First observedv0.3.1Jul 31, 2026

TDQS

A4.3/5.0

Behavior3/5

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

Annotations already provide idempotentHint=true and destructiveHint=false, covering the main behavioral concerns for a pure computation tool. The description adds no further behavioral disclosure (e.g., session effects or resource usage), but it does not contradict annotations. It is adequate but not enriched beyond the structured data.

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

Conciseness5/5

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

The description is a single, well-structured sentence that front-loads the tool's purpose, lists concrete outputs, and then gives the usage preference. No filler or redundancy; every clause earns its place.

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 simple statistics tool, the description combined with the full input schema, output schema, and annotations provides everything an agent needs to call it correctly. It identifies the domain, expected input, outputs, and relationship to a sibling tool, with no significant missing context.

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

Parameters3/5

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

Schema description coverage is 100%, so the input schema already fully documents both parameters. The description reinforces that 'data' is a list of numbers but adds no additional parameter semantics beyond the schema. Baseline 3 is appropriate.

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?

The description states a specific verb and resource ('Descriptive statistics for a list of numbers') and enumerates the exact outputs (mean, median, variance, standard deviation, min, max). It also names the sibling alternative (evaluate_sage) and explains why this tool is preferred, making it clearly distinguishable from similar math tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says to prefer this tool over evaluate_sage for summary statistics, giving an agent a direct routing rule. This is sufficient guidance for when to use this tool versus its most relevant sibling.

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