Finds strongly connected components in a directed graph using Gabow's algorithm. Detects cyclic groups of vertices from edge pairs without recursion.
Find strongly connected components of a directed graph using Gabow's algorithm.
| Name | Required | Description | Default |
|---|---|---|---|
| edges | Yes | Directed edges as [u, v] pairs | |
| vertex_count | Yes | Number of vertices (max 50,000) |
Changes observed during successful MCP inspections.
v0.1.11Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It names the algorithm but omits output format, complexity implications, vertex numbering, and the max-vertex constraint (which is only in the schema). For a pure computation tool this is thin, though the algorithm name adds a little method transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence with no wasted words. It states the action, input, and algorithm efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of two sibling SCC tools and no output schema, the description should describe the return shape (e.g., list of components) and when this implementation is preferable. It instead provides only a one-line algorithmic summary, leaving key context missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no syntax or format details beyond what the schema already provides for 'edges' and 'vertex_count'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Find strongly connected components of a directed graph' and names the algorithm. It is clear on its own, but it does not distinguish itself from sibling SCC tools like tarjan_scc or kosaraju_scc, missing that sibling differentiation point.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus the alternative SCC algorithms (tarjan_scc, kosaraju_scc). There are no exclusions, prerequisites, or context cues; the agent must infer selection solely from the algorithm name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.