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viridis-agent-fleet

viridis-agent-fleet

by jdhart81
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

verdigraph__detect_format

Detect which agent-file format the content uses, such as verdigraph_genome or claude_project_export, to prepare for building.

Instructions

[verdigraph — verifiable cognition: deterministic content-addressed brain_id for any agent file] Detect which supported agent-file format the content is (verdigraph_genome, claude_project_export, openai_assistant, prompt_list) before building.

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": {
      "content": "<content>"
    },
    "name": "verdigraph__detect_format"
  }
}

Input Schema

NameRequiredDescriptionDefault
contentYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.11

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It indicates the tool performs detection without side effects, but does not detail output format or behavior on invalid content. This is minimal but not contradictory.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short, with two sentences. The first sentence is a lengthy prefix with branding, which could be trimmed. However, the core information is front-loaded and the description earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple detection tool with one parameter and no output schema, the description covers the purpose and basic usage. It lacks explicit mention of return value or error handling, but is adequate given the tool's simplicity.

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?

With 0% schema description coverage, the description adds meaning by explaining that 'content' is an agent-file string and listing valid formats. However, it does not specify the format of the input or how to provide it (e.g., raw text vs. path).

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 that the tool detects the format of agent-file content, listing four specific formats. It is a specific verb+resource combination that distinguishes it from siblings like verdigraph__build_brain and verdigraph__verify_brain.

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 explicitly says 'before building', implying use as a precursor to build_brain. While it doesn't mention when not to use or provide alternatives, this is adequate context for a simple detection tool.

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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