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Graphicle-AI

Graphicle MCP Server

Official

god_nodes

Read-onlyIdempotent

Identify top connected nodes in a repository's code graph to reveal core abstractions and dependencies. Filter by project, code-graph episode, top count, and hub exclusion.

Instructions

Return the most connected nodes - the core abstractions of the knowledge graph.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repoNoThe project to read: its id or key, or with graphicle mcp a synced folder. Defaults to this connection's project.
as_ofNoThe code-graph episode to read: an episode id, a label such as a commit sha, latest or working. Defaults to the project's current code graph.
top_nNo
exclude_hubs_percentileNoSuppress nodes whose degree exceeds this percentile (0-100) of the degree distribution, matching cluster()'s hub exclusion

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds the useful framing that these are the highest-degree 'core abstractions', but it does not disclose ordering, default count, or how hub exclusion affects results.

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?

A single front-loaded sentence that defines the resource and its meaning with no waste. It is efficient, though short enough that it leaves openings unfilled.

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?

There is no output schema, so the description carries return-value burden, and it never states the shape of results, ordering, or the effect of top_n. It is adequate for a simple read tool with good annotation coverage, but leaves the agent to guess at output semantics.

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 75% schema description coverage, the schema already documents repo, as_of, and exclude_hubs_percentile, so the baseline is 3. The description adds no parameter detail at all, and top_n remains undocumented beyond its default of 10.

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?

States a specific verb and resource ('Return the most connected nodes') and clarifies the opaque term 'god nodes' as 'the core abstractions of the knowledge graph', so the agent knows what it retrieves. It does not distinguish itself from graph-analyzing siblings such as graph_stats, query_graph, or get_community, 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?

The description gives no when-to-use or when-not-to-use guidance. There is no indication of how this differs from graph_stats, query_graph, or get_community, forcing the agent to infer applicability from the name alone.

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