Skip to main content
Glama
Sign In

rapp-agent-builder

by io.github.kody-w
OverviewDiscussionsAdmin

Find existing agents

find_agents
Read-only

Searches the public agent registry (about 1,700 single-file agents) for agents that already do what the user wants, for example when they ask 'is there an AI tool for...' or want a ready-made automation instead of building one. Use it before building from scratch, or when the user asks whether an agent exists for a task.

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": {
      "query": "<query>"
    },
    "name": "find_agents"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoHow many results, default 5
queryYesPlain words describing the task, e.g. 'summarize sales calls'

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly/openWorld/non-destructive, so safety is covered; the description adds genuinely useful behavioral context the annotations lack — that this is a public, single-file registry of roughly 1,700 agents and a fuzzy search rather than an exact lookup. It says nothing about result ranking, match quality, or rate limits, but for a read-only search that is a minor gap.

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?

Two sentences, front-loaded with the action and corpus before the usage condition. The trailing clause 'or when the user asks whether an agent exists for a task' largely restates the earlier example, a small redundancy that keeps it from a 5.

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?

No output schema exists, so the description reasonably does not explain return values, and it covers purpose, scope, and trigger conditions for a simple two-parameter search. It could go further by hinting at what the results are (agent names/IDs) or which sibling to call next after a match, which would make the workflow 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 both 'query' and 'limit' are already documented with an example and a default. The description adds no syntax, format, or constraint detail beyond what the schema provides, which is the baseline-3 case.

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?

States a specific verb and resource ('Searches the public agent registry') and scopes the corpus (~1,700 single-file agents) and the intent served (finding ready-made agents instead of building). An agent can distinguish this discovery tool from siblings like get_agent_code or use_agent_here, which serve later stages of the workflow.

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 when to call it: 'Use it before building from scratch, or when the user asks whether an agent exists for a task,' even quoting trigger phrasing ('is there an AI tool for...'). It does not name the downstream alternatives (e.g., use_agent_here to run a result), so it stops short of full alternative routing.

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

All rapp-agent-builder toolsInspect
Try in Browser
Claude Code, inside the Godot 4 editorFlockbay puts an AI agent in the real Godot 4 editor: it writes GDScript, runs your game and fixes what breaks. Free, or bring your own Claude Code or Codex.flockbay.comAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources