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mcp-parigp

mcp-parigp

by daedalus
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

elllocalred

Computes the local reduction type of an elliptic curve at a specified prime. Determines the reduction behavior (good, bad, additive, multiplicative) for number theory applications.

Instructions

Compute the local reduction type at prime p.

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": {
      "E": "<E>",
      "p": "<p>"
    },
    "name": "elllocalred"
  }
}

Input Schema

NameRequiredDescriptionDefault
EYesElliptic curve structure.
pYesPrime.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states the computation, without indicating whether it is read-only, requires specific permissions, or has side effects. A more detailed description should clarify these aspects.

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?

A single sentence that clearly states the tool's purpose. No wasted words. The verb is front-loaded. Ideal conciseness for a simple tool.

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?

Given the tool's simplicity, 100% schema coverage, and presence of an output schema, the description covers the essential purpose. It lacks mention of error cases or prerequisites, but these are minor given the straightforward nature.

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% (both parameters described). The description adds minimal extra meaning: it reiterates that p is a prime, which is already in the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

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 clearly states the tool computes the local reduction type at a prime p. The verb 'compute' and resource 'local reduction type' are specific and distinct from sibling tools like ellglobalred or ellap. Even without explicit sibling differentiation, the purpose is unambiguous.

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 on when or when not to use this tool versus alternatives. There is no mention of prerequisites, context, or similar functions like ellglobalred. The agent must infer usage from the description alone.

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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