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t-rhex

mcp-obsidian-vault

by t-rhex

register_agent

Creates or updates an agent profile with capabilities, tags, and capacity in the Agents/ folder, enabling capability-based task routing.

Instructions

Register an agent with capabilities, tags, and capacity. Creates or updates an agent profile in the Agents/ folder. Used for capability-based task routing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoSpecialization tags (e.g. ['typescript', 'react', 'database']).
modelNoLLM model name powering this agent (e.g. 'claude-opus-4-6', 'gpt-4o').
agent_idYesUnique identifier for the agent (e.g. 'claude-code-1', 'research-agent').
descriptionNoHuman-readable description of what this agent does.
capabilitiesNoTask types this agent can handle (e.g. ['code', 'research', 'writing']).
max_concurrentNoMaximum number of parallel tasks this agent can handle. Default: 3.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations available, the description carries the full burden of behavioral disclosure. It explicitly states the key side effect: 'Creates or updates an agent profile in the Agents/ folder.' However, it does not clarify whether updates merge or replace existing fields, what permissions are needed, or what the tool returns after invocation.

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?

Three short, purposeful sentences: the first states the core action, the second adds the upsert and storage location, and the third gives the intended use. No filler or redundant phrasing.

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?

The description gives enough context for a typical registration call: what the tool does, where it stores profiles, and why it exists. It could be more complete by describing overwrite semantics or return values, but the schema covers parameters well and the stated purpose is sufficient for most invocation scenarios.

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 already documents all 6 parameters with high coverage, so the baseline is 3. The description adds minimal semantic value beyond echoing the concepts of capabilities, tags, and capacity. It correctly groups the most relevant parameters but does not explain them in more depth than 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?

The description uses a specific verb ('Register') with a clear resource ('agent profile') and names the relevant fields (capabilities, tags, capacity). It further clarifies the operation as an upsert ('Creates or updates') and distinguishes itself from siblings like list_agents or suggest_assignee by focusing on agent registration.

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 phrase 'Used for capability-based task routing' gives a clear functional context for when this tool is appropriate. It does not explicitly mention alternatives or when not to use it, but the context is strong enough for an agent to infer the primary use case.

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