Skip to main content
Glama
Particle-Academy

fancy-flow-mcp-js

Describe Node Kind

describe_node_kind

Retrieve the full configuration schema, default config, and input/output ports for a specific node kind. Use this to understand node fields before configuring, avoiding guesswork.

Instructions

One kind in full — its config schema, its default config, and its input and output ports. Call this before configure_node rather than guessing field names; the fields are deliberately not on list_node_kinds, which would turn a vocabulary query into a payload nobody reads.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYesThe kind name, e.g. "llm_call" or the fully-qualified form.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are present, so the description carries the behavioral burden. It signals that this is a read-like lookup by saying what data it returns and by positioning it as a pre-configuration step, but it does not explicitly state side-effect-freedom or error behavior. For a simple describe operation, this 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.

Conciseness5/5

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

Two sentences deliver the full value: the first front-loads the exact purpose and return content, the second provides the call-order rationale and names the alternative that should not be used. There is no wasted wording.

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

Completeness5/5

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

For a single-argument, read-oriented, no-output-schema tool, the description is complete. It explains what will be returned, when to call it, why it is needed, and how it relates to list_node_kinds and configure_node. An agent has enough information to select and call it correctly.

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?

Only one parameter exists, kind, and the input schema already documents it with an example. The description does not add much parameter-level meaning beyond aligning kind with the concept of a node type, and with 100% schema coverage, the baseline score of 3 is appropriate.

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 names a concrete operation and enumerates the content it returns: config schema, default config, and input/output ports. It separates the tool from list_node_kinds by explaining that fields are deliberately omitted from that sibling, so an agent can distinguish it without needing to inspect schemas.

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

Usage Guidelines5/5

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

The description gives explicit call-order guidance: call this before configure_node rather than guessing field names. It also explains why list_node_kinds is not a substitute for looking up field names, which is clear, actionable guidance on when and why to use this tool.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Particle-Academy/fancy-flow-mcp-js'

If you have feedback or need assistance with the MCP directory API, please join our Discord server