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relay_get_clusters

Group facts into connected clusters via graph structure to reveal reasoning threads and see how stored knowledge links together.

Instructions

Group facts into clusters using FalkorDB's connected-components algorithm. Each cluster is one connected reasoning thread, found purely from graph structure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses that clustering is derived 'purely from graph structure' via connected-components, which tells the agent the result is deterministic and not semantic. However, it never states whether the call is read-only or writes cluster nodes, and gives no cost or output-shape context.

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 tight sentences with zero filler, and the core action is front-loaded before the explanatory clause about reasoning threads.

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 zero-argument tool the description is reasonably self-contained and even sketches what a cluster represents, but with no output schema and no annotations it should say more about the return shape (cluster ids, members, sizes) and confirm the operation does not mutate the graph.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there is no parameter meaning to explain; the schema is trivially complete at 100% coverage. Baseline 4 applies for a parameterless tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific action and resource ('group facts into clusters') plus the underlying mechanism (connected-components), so the agent knows this is a structural analysis tool rather than a retrieval of stored facts. It does not explicitly contrast itself with siblings like relay_get_context or relay_get_important_facts, which also return derived views of facts, so it falls short of a 5.

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

Usage Guidelines2/5

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

There is no when-to-use statement, no prerequisite, and no named alternative. The phrase 'one connected reasoning thread' hints at the analytical purpose but leaves the agent to infer when clustering beats plain context retrieval.

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