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nikhilxnarula

sagemath-mcp

Graph Info

graph_info

Get basic graph invariants including order, size, connectivity, girth, and edges. Use it first to understand a graph before exploring advanced properties.

Instructions

Basic invariants of a graph: order, size, connectivity, girth, edges.

Use this first when you need to know what a graph is before asking sharper questions about it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
graphYesa graph6 string such as 'Ks?GOgSOpDL?', or a registry name such as 'cubeplex'. Call list_named_graphs to see the known names.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the burden. It communicates that this is a basic informational read operation, but it does not state side effects, errors, or behavior for invalid graph strings. The mention of output schema in context signals helps, but the description itself is still light on behavioral detail.

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?

The description is two tight sentences with no filler. The most important content — what the tool returns — is front-loaded, and the usage guidance is a natural second sentence.

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

Completeness4/5

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

For a single-parameter tool with a documented schema and an output schema present, the description provides enough orientation: what it returns and when to call it. It could mention the registry-name option itself, but the schema already covers that, and the sibling list_named_graphs fills the gap.

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?

Schema description coverage is 100%, with the single 'graph' parameter already documented as accepting graph6 strings or registry names and pointing to list_named_graphs. The description adds no parameter-level detail beyond the schema, so the baseline-3 score applies.

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 clearly identifies the resource ('a graph') and the kind of result ('order, size, connectivity, girth, edges'), which distinguishes it from the more specialized property-test siblings. It lacks an explicit action verb, but 'Basic invariants' and the usage sentence make the purpose reasonably unambiguous.

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

Usage Guidelines4/5

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

'Use this first when you need to know what a graph is' provides clear placement in a workflow, and 'before asking sharper questions about it' implies the alternative class of specialized tools. It does not name specific sibling tools or give explicit when-not conditions, so it stops short of a 5.

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