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mcp-parigp

mcp-parigp

by daedalus
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

polsubcyclo

Compute sub-cyclotomic polynomials for a given cyclotomic field order and subfield degree. Specify n and d to obtain the polynomial.

Instructions

Compute sub-cyclotomic polynomials.

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": {
      "n": "<n>",
      "d": "<d>"
    },
    "name": "polsubcyclo"
  }
}

Input Schema

NameRequiredDescriptionDefault
dYesDegree of subfield.
nYesCyclotomic field order.
vNoVariable name.x

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.2/5.0
Behavior1/5

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It gives no information about side effects, permissions, output format, or constraints (e.g., valid ranges for n and d). This is a critical omission for a computation tool.

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

Conciseness3/5

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

The description is a single sentence, which is concise, but it contributes minimal information beyond the tool name. It is not structured to be maximally informative 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 lacks essential context such as the mathematical relationship between n and d, valid ranges, or examples. It is insufficient for an AI to correctly select and invoke the tool in diverse scenarios.

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 coverage is 100%, so the input schema already describes the parameters (n, d, v) and their meaning. The description adds no additional semantic value beyond what the schema provides, earning the baseline score.

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

Purpose3/5

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

The description states 'Compute sub-cyclotomic polynomials,' which clearly specifies the verb and resource. However, it fails to distinguish this tool from the sibling 'polcyclo' (which computes cyclotomic polynomials), and the term 'sub-cyclotomic' is not explained, leaving ambiguity for an AI agent.

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 guidance is provided on when to use this tool versus alternatives like 'polcyclo' or 'polchebyshev'. There is no mention of prerequisites, exclusions, or context-specific usage.

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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