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mcp-parigp

mcp-parigp

by daedalus
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

ellap

Compute the trace of Frobenius for an elliptic curve at a given prime number.

Instructions

Compute the trace of Frobenius for an elliptic curve 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": "ellap"
  }
}

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 present, so the description carries the full burden for behavioral disclosure. It only states what the tool computes without mentioning side effects, required inputs, or potential errors. The description fails to disclose whether the tool is read-only or has other behaviors.

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, clear sentence that is well-structured and contains no unnecessary words. It adheres to the principle of being front-loaded and concise.

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 presence of an output schema and 100% schema description coverage, the description provides adequate context for the tool's core purpose. However, it lacks information about error conditions or the nature of the trace value, but this is partially compensated by the output schema.

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% with basic descriptions for both parameters. The tool description adds minimal value beyond the schema, merely restating the parameters' roles. The baseline of 3 is appropriate as the schema already provides sufficient meaning.

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 verb 'compute' and the specific resource 'trace of Frobenius for an elliptic curve at prime p'. It effectively distinguishes the tool from sibling tools like elladd, ellheight, etc., which perform other elliptic curve operations.

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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, context, or exclusions, leaving the agent without decision support for tool selection.

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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