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Neuron - "Synapse"

Neuron - "Synapse"

by recla93
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

skill

Fetch the complete text of a Neuron skill or playbook to access full workflow details when needed.

Instructions

Return the FULL text of a Neuron skill/playbook on demand — token-cheap, fetch it only when you need the details. Use after the compact opener to load the complete workflow or curation rules.

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": "skill"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameNoWhich skill: playbook (the full PRE/POST workflow) or curated (clean-graph patterns).playbook

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv5.4.2
    • changedInput schema / properties / name / default
      Previous value: -"auto-context"New value: +"playbook"
    • changedInput schema / properties / name / description
      Previous value: -"Which skill: auto-context (PRE/POST workflow, recommended), curated (clean-graph patterns), base/full (references)."New value: +"Which skill: playbook (the full PRE/POST workflow) or curated (clean-graph patterns)."
    • changedInput schema / properties / name / enum
      Previous value: -[
-  "auto-context",
-  "curated",
-  "base",
-  "full"
-]New value: +[
+  "playbook",
+  "curated"
+]
  2. Addedv5.0.1

TDQS

A4/5.0
Behavior3/5

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

No annotations provided, so description carries full burden. Mentions 'token-cheap' as a performance trait, but does not disclose potential errors, auth needs, or size of returned text. Adequate for a simple read operation.

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 sentences, front-loaded with purpose, no redundant information. Every word adds value.

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?

Given simple tool with one parameter, no output schema, and high schema coverage, description is nearly complete. Could mention error handling or return format, but not necessary for basic usage.

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 coverage is 100% and already describes the 'name' parameter with enum values. Description does not add extra meaning beyond what the schema provides, meeting baseline.

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 verb 'Return' and resource 'FULL text of a Neuron skill/playbook'. Distinguishes from sibling tools by specifying it's a detail-fetching operation after a compact opener.

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?

Explicitly says 'Use after the compact opener' and 'fetch it only when you need the details', providing clear context. Does not list alternatives or when not to use, but context is sufficient given no direct sibling competitors.

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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