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mcp-parigp

mcp-parigp

by daedalus
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

theta

Compute the theta function with complex parameter z and lattice parameter tau. Optionally specify the precision for numerical accuracy.

Instructions

Compute the theta function theta(z, tau).

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": {
      "z": "<z>",
      "tau": "<tau>"
    },
    "name": "theta"
  }
}

Input Schema

NameRequiredDescriptionDefault
zYesComplex parameter.
tauYesLattice parameter.
precisionNoOptional precision.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.5/5.0
Behavior2/5

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

With no annotations, the description bears full responsibility for behavioral disclosure. It only restates the function name and parameters, omitting critical details like domain restrictions (e.g., Im(tau)>0), error handling, or precision behavior.

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 under-specified. It could be improved by adding relevant context without increasing length significantly.

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?

The tool lacks an output schema, so the description should clarify return values, but it does not. It also fails to address computational nuances or differentiate from similar tools, leaving the agent underinformed.

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%, and the description does not add meaning beyond what the schema provides (e.g., 'Complex parameter', 'Lattice parameter'). The precision parameter is not mentioned in the description.

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 the theta function theta(z, tau).' which provides a verb and resource, but it is vague about which specific theta function (e.g., Jacobi theta) and does not distinguish from sibling tools like 'lfuntheta'.

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 given on when to use this tool versus alternatives such as 'lfuntheta', 'eta', or 'weber'. The description provides no context for 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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