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mcp-sympy

mcp-sympy

by daedalus
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

sympy_erf

Compute the error function of a given expression using SymPy, enabling symbolic evaluation of the integral related to the normal distribution.

Instructions

Error function.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "expr": "<expr>"
    },
    "name": "sympy_erf"
  }
}

Input Schema

NameRequiredDescriptionDefault
exprYesExpression

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

D1.7/5.0
Behavior1/5

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

With no annotations, the description must fully disclose behavior, but 'Error function.' gives no details about side effects, return type, input constraints, or computational behavior. It adds no value over the tool name.

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

Conciseness2/5

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

The description is extremely concise (two words) but is under-specifying, not efficient. It fails to convey the tool's purpose or usage, making it insufficient for an AI agent.

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

Completeness1/5

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

Despite having an output schema, the description does not mention it or explain return values. The tool's complexity is low, but the description lacks essential information about what the tool returns and how to interpret results.

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

Parameters2/5

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

The sole parameter 'expr' has a generic description 'Expression' in the schema, and the tool description adds nothing to clarify its meaning (e.g., expected format, whether numeric or symbolic). Schema coverage is 100%, but the description does not improve upon the minimal schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Error function.' is a noun phrase with no verb, making it unclear whether the tool computes the error function or defines it. It does not explicitly state that it evaluates the error function for a given expression, and among many sibling tools, it lacks specificity.

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

Usage Guidelines2/5

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

No usage guidelines are provided. The description does not indicate when to use this tool instead of other related functions like sympy_erfc or sympy_fresnelc, nor does it mention any prerequisites or context.

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

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