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tokonomix-council-mcp

tokonomix-council-mcp

Official
by TokonoMix
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

tokonomix_get_skill

Retrieve the consensus SKILL.md to learn when to use multi-model consensus vs single-model passthrough for legal, GDPR, code review, fact-check, and see available modes.

Instructions

Return the canonical Tokonomix consensus SKILL.md content for this MCP-server version. Use this on first connection, or when tokonomix_skill_version reports a version newer than your cache. The skill explains when to reach for consensus (legal, GDPR, code review, fact-check) vs single-model passthrough, plus what modes (consensus, diff, best_of, raw, full) are available right now.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "tokonomix_get_skill"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.12.0

TDQS

A4.7/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses version-specific behavior and caching implications ('for this MCP-server version', 'newer than your cache'). While it does not explicitly state that the operation is read-only or describe the exact output format, the context supplied is useful and goes beyond the schema.

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 two sentences: the first states the core function, the second provides usage guidance and a brief overview of the skill content. Every sentence earns its place, and it is front-loaded with the primary purpose.

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

Completeness5/5

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

For a zero-parameter tool with no annotations and no output schema, the description covers the purpose, when to use it, and what the returned skill explains (modes, consensus vs passthrough). It is complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With zero parameters, the baseline is 4 per the rubric. The description adds relevant context about version-specific content and cache usage, which is not represented in the schema. There are no parameters to explain, so this is appropriate.

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 returns 'canonical Tokonomix consensus SKILL.md content for this MCP-server version', which is a specific verb+resource that distinguishes it from siblings like tokonomix_consensus_ask or tokonomix_single_ask. It is unambiguous and not a tautology.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance is given: 'Use this on first connection, or when tokonomix_skill_version reports a version newer than your cache.' This directly tells when to invoke the tool and references a sibling for version checking, providing clear usage context.

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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