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mcp-parigp

mcp-parigp

by daedalus
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

dirichlet

Compute Dirichlet L-functions by providing a complex parameter and a Dirichlet character, with optional precision control.

Instructions

Compute Dirichlet L-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": {
      "s": "<s>",
      "chi": "<chi>"
    },
    "name": "dirichlet"
  }
}

Input Schema

NameRequiredDescriptionDefault
sYesComplex parameter.
chiYesDirichlet character.
precisionNoOptional precision.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description must convey behavior. It only states 'compute' with no details on side effects, return format, or performance. Essential behavioral context is missing.

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 a single sentence, concise and waste-free. However, it sacrifices completeness for brevity, which is acceptable given the schema covers parameters.

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

Completeness2/5

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

For a specialized mathematical tool without output schema, the description is incomplete. It fails to explain what the return value is (e.g., complex number) or constraints on chi and s.

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% with descriptions for all parameters (s, chi, precision). The description adds no extra meaning beyond these, so baseline score of 3 applies.

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

Purpose4/5

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

The description clearly states the tool computes Dirichlet L-functions, a specific verb-resource pair. However, it does not distinguish from sibling tools like lfun or bnrL1 that also handle L-functions.

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 to use this tool versus alternatives such as lfun or bnrL1. The description lacks context about suitable inputs or scenarios.

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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