Skip to main content
Glama
Sign In
systems-thinking-mcp

systems-thinking-mcp

by davidpanter
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Start Analysis

start_analysis

Begin a systems thinking analysis with a problem definition to get categorized model clusters for review and selection.

Instructions

Begin a systems thinking analysis session. Returns model clusters organized by category for you to review and select from.

BEFORE calling this, consider whether a predefined strategy fits your use case. Call get_strategy (with no arguments) to see available strategies.

THE EXPECTED WORKFLOW IS:

  1. start_analysis → review clusters, pick relevant models

  2. expand_selection → get full details + graph neighbors for your picks

  3. apply_lens (2-4 times) → apply your chosen lenses

  4. synthesize → integrate findings

The value is in COMPOSING multiple perspectives. Apply at least 2-3 lenses before synthesizing.

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": {
      "problem": "<problem>"
    },
    "name": "start_analysis"
  }
}

Input Schema

NameRequiredDescriptionDefault
scopeNoWhat is in or out of bounds for this analysis
contextNoSystem description, constraints, environment
problemYesWhat are we analyzing or troubleshooting?

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It states the tool 'Returns model clusters organized by category,' which indicates a read-like output. It also describes the iterative workflow and emphasizes composing multiple perspectives. However, it does not explicitly address whether the tool has side effects (e.g., creating persistent session state) or require any specific permissions, leaving a small transparency gap.

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 well-structured and front-loaded: it opens with the core purpose, then provides a decision point about get_strategy, followed by a clear numbered workflow. Every sentence contributes value, and the length is appropriate for the tool's complexity. It avoids fluff and repetition.

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?

Given the tool's role as an entry point to a multi-step analysis process, the description provides complete context: what it returns, how it fits into the larger workflow, and a best-practice recommendation (apply at least 2-3 lenses). No output schema exists, but the description gives a sufficient high-level view of the result. The workflow context makes it easy for an agent to use this tool appropriately.

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?

The input schema has 100% description coverage for all three parameters (scope, context, problem), each with a clear textual description. The tool description does not add additional semantics beyond the schema, but it also does not need to. Per the rubric, baseline 3 applies when schema coverage is high and description provides no extra parameter detail.

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's function: 'Begin a systems thinking analysis session' and specifies the immediate outcome: 'Returns model clusters organized by category.' It differentiates from siblings by positioning it as the first step in a defined workflow, distinct from expand_selection, apply_lens, synthesize, and get_strategy.

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 provided: 'BEFORE calling this, consider whether a predefined strategy fits your use case. Call get_strategy...' It also outlines the complete expected workflow with numbered steps, making it clear when to use this tool and when to use alternatives. This is strong, non-ambiguous usage direction.

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

Deploy Server

Other Tools