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GSEP-MCP

GSEP-MCP

Official
by gsepcore
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TypeScript
Hybrid

gsep_get_status

Obtain genome health, fitness, drift, evolution, and security stats. Optionally, omit genome_id to list all active genomes.

Instructions

Get genome health, fitness scores, drift status, evolution generation, and security stats. Omit genome_id to list all active genomes.

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": "gsep_get_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
genome_idNoGenome ID to inspect. If omitted, returns all active genomes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.9

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses what the tool returns (health, fitness, drift, generation, security stats) and the two modes of operation (specific genome or all). However, it does not mention whether the tool is read-only, side-effect-free, or any other behavioral constraints 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 concise sentences: the first lists the data fields retrieved, the second explains the parameter behavior. No redundant words; information is front-loaded and easy to parse.

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 tool with one optional parameter and no output schema, the description adequately covers the return fields and usage modes. It could be improved by clarifying the response structure (e.g., single object vs. list) or error scenarios, but it is largely sufficient.

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 description coverage is 100% and already fully describes the single parameter, including the behavior when omitted. The tool description echoes this without adding new semantic meaning, so it meets the baseline but does not exceed.

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 explicitly states the verb 'Get' and the resource 'genome status' with specific fields (health, fitness scores, drift status, evolution generation, security stats). It distinguishes itself from sibling tools like gsep_scan_input or gsep_chat by focusing on status retrieval.

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

Usage Guidelines4/5

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

The description provides clear guidance on when to use the optional genome_id parameter: omit to list all active genomes, include for a specific genome. It implies the tool is for inspection purposes, but does not explicitly state when not to use it or mention alternatives.

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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