GSEP-MCP

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gsep_record_feedback

Record user satisfaction feedback for a genome to reinforce optimal gene configurations or trigger evolution when dissatisfaction is detected.

Instructions

Record user satisfaction feedback for a genome. Positive signals reinforce current gene configuration. Negative signals trigger evolution on the next cycle.

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": {
      "genome_id": "<genome_id>",
      "satisfied": "<satisfied>"
    },
    "name": "gsep_record_feedback"
  }
}

Input Schema

Name	Required	Description	Default
`note`	No	Optional feedback note
`quality`	No	Quality score 0-1 (optional, derived from satisfied if omitted)
`user_id`	No	User identifier	mcp-user
`genome_id`	Yes	Genome ID to record feedback for
`satisfied`	Yes	Whether the user was satisfied with the last response

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.9Jul 28, 2026

TDQS

A4/5.0

Behavior4/5

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

Discloses key behavioral traits: positive signals reinforce configuration, negative signals trigger evolution. With no annotations, the description carries the full burden; though missing details like reversibility or permissions, the core side effects are explained.

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?

Two front-loaded sentences: first states purpose, second explains behavioral consequences. No redundancy or unnecessary words.

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?

Explains the outcome of feedback (reinforcement/evolution) which is essential context. Lacks mention of return value or success/failure behavior, but given no output schema and clear schema descriptions, it is reasonably complete.

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%, so baseline is 3. The description adds no extra meaning beyond the schema for parameters like 'note', 'quality', or 'user_id'. The 'satisfied' parameter is tied to 'positive/negative signals' but that is already clear from the 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?

Clearly states the tool records satisfaction feedback for a genome, with specific verb 'record' and resource 'genome'. Distinguishes from sibling tools (e.g., scanning, chat) via unique purpose.

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

Usage Guidelines3/5

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

Implies usage context (after a response, to provide feedback) and explains consequences (reinforcement or evolution), but lacks explicit when-to-use or when-not-to-use guidance compared to 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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