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curate-ipsum

curate-ipsum

by egoughnour
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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compute_partitioning

Partition a project's call graph using Fiedler spectral analysis. Recursively apply the Fiedler vector to produce a partition tree with algebraic connectivity values.

Instructions

Compute Fiedler spectral partitioning of a project's call graph. Recursively bipartitions the graph using the Fiedler vector (2nd eigenvector of the graph Laplacian). Returns a partition tree with node assignments and algebraic connectivity (λ₂) values.

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": {
      "workingDirectory": "<workingDirectory>"
    },
    "name": "compute_partitioning"
  }
}

Input Schema

NameRequiredDescriptionDefault
max_depthNo
workingDirectoryYes
min_partition_sizeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.5.1

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description must carry the burden. It explains the algorithm and return values well, but does not explicitly state whether the operation is safe/read-only or if it has side effects, permissions, or performance implications.

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 dense sentences with no filler. The first sentence states the purpose immediately and the second gives method and output, making it very efficient.

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

Completeness2/5

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

Given the lack of output schema, annotations, and parameter descriptions, this tool description is incomplete. It fails to explain parameter semantics, usage context, or prerequisites, leaving significant gaps for a moderately complex tool.

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

Parameters2/5

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

Schema coverage is 0%, and the description does not explain any of the three parameters (workingDirectory, max_depth, min_partition_size). The recursive bipartition mention implies some relationship to max_depth, but it is not explicit, leaving the agent to infer meaning.

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 uses a specific verb ('Compute') and defines exact resource ('Fiedler spectral partitioning of a project's call graph'), clearly distinguishing it from sibling tools like find_function_partition by naming a specific spectral method.

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?

No guidance is given on when to use this tool versus alternatives. It does not mention prerequisites (e.g., needing an extracted call graph) or exclusions.

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

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