Symbolic Algebra MCP Server

intro_many

Define and store multiple symbolic variables with positive and negative assumptions in one call for consistent symbolic algebra setup.

Instructions

Introduces multiple sympy variables with specified assumptions and stores them.

Takes a list of VariableDefinition objects for the 'variables' parameter.
Each object in the list specifies:
- var_name: The name of the variable (string).
- pos_assumptions: A list of positive assumption strings (e.g., ["real", "positive"]).
- neg_assumptions: A list of negative assumption strings (e.g., ["complex"]).

The JSON payload for the 'variables' argument should be a direct list of these objects, for example:
```json
[
    {
        "var_name": "x",
        "pos_assumptions": ["real", "positive"],
        "neg_assumptions": ["complex"]
    },
    {
        "var_name": "y",
        "pos_assumptions": [],
        "neg_assumptions": ["commutative"]
    }
]
```

The assumptions must be consistent, so a real number is not allowed to be non-commutative.

Prefer this over intro() for multiple variables because it's more efficient.

Input Schema

Name	Required	Description	Default
`variables`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 29, 2026

TDQS

A4.4/5.0

Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It discloses that variables are stored and that assumptions must be consistent, which adds useful context beyond the schema, but it does not say what happens with duplicate variable names, conflicting existing assumptions, or failed validation.

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 front-loaded with the core action and then expands into the nested parameter shape, which is justified by the zero schema description coverage. The JSON example is somewhat repetitive after the bullet explanations, but it is useful for correct invocation and not excessive.

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?

The output schema exists, so return values need not be explained. The description covers the nested input shape well and states the key consistency constraint, though it leaves some mutation behavior unclear, such as overwrite semantics for existing variables.

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 must fully explain the parameter. It does this thoroughly by describing the `variables` list, each `VariableDefinition` field, the meaning of positive and negative assumption strings, and a complete JSON example.

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: it introduces multiple sympy variables with specified assumptions and stores them. It also explicitly distinguishes itself from the sibling `intro()` by saying it should be preferred for multiple variables, so an agent can select it without confusion.

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 gives an explicit alternative: `intro()` should be used for single variables, while this tool should be preferred for multiple variables because it is more efficient. This is exactly the when-to-use guidance an agent needs for selection.

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