HUMMBL MCP Server

Get Self-Dialectical AI Methodology

get_methodology

Retrieve the canonical Self-Dialectical AI Systems methodology with HUMMBL Base120 mappings for structured problem-solving.

Instructions

Retrieve the canonical Self-Dialectical AI Systems methodology with HUMMBL Base120 mappings.

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": "get_methodology"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`id`	Yes
`title`	Yes
`version`	Yes
`summary`	Yes
`documentUrl`	No
`totalPages`	No
`modelsReferenced`	Yes
`stages`	Yes
`metaModels`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv1.0.3May 4, 2026
- removedInput schema / additionalProperties
```
Removed value: -false
```
Addedv1.0.0Jan 3, 2026

TDQS

A3.8/5.0

Behavior3/5

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

With no annotations, the description carries full burden. It implies a read operation ('Retrieve') but does not disclose any behavioral traits such as auth requirements, rate limits, or whether it returns metadata. It is adequate but not comprehensive.

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, well-structured sentence that immediately conveys the tool's purpose. No redundant or extraneous information.

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?

For a simple retrieval tool with no parameters and an output schema, the description is fairly complete. It names the specific resource and mentions the mappings. However, it lacks usage context, such as when to prefer this over similar tools.

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?

The tool has 0 parameters, so schema coverage is 100%. The description adds meaning by specifying what is retrieved (the methodology and mappings), which goes beyond the empty schema.

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 'Retrieve' and specifies the resource: 'the canonical Self-Dialectical AI Systems methodology with HUMMBL Base120 mappings'. This distinguishes it from sibling tools like get_model or get_transformation, which retrieve different resources.

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 provided on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or scenarios where other tools would be more appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server