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papergraph-mcp

papergraph-mcp

by MahmoudSedky147
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_theorems

List theorem-like environments in the currently loaded paper, optionally filtering by kind, to quickly locate and explore mathematical statements.

Instructions

List theorem-like environments in the currently loaded paper.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_theorems"
  }
}

Input Schema

NameRequiredDescriptionDefault
kindNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.1

TDQS

C2.4/5.0
Behavior1/5

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

There are no annotations and the description gives no information about side effects, read-only behavior, permissions, or any impact on the workspace. The user is left to infer that listing is non-destructive.

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 a single, concise sentence with no redundancy or unnecessary detail. It is well-structured and easy to read.

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?

The description is too brief to provide complete context. It does not clarify what 'theorem-like environments' includes (e.g., theorems, lemmas, corollaries) or how the 'kind' parameter influences output, leaving significant gaps for the agent.

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

Parameters1/5

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

The schema has a single optional parameter 'kind' with no description, and the tool description does not mention it at all. There is no explanation of what values it accepts or how it affects the results.

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 states a clear action ('List') and a specific resource ('theorem-like environments in the currently loaded paper'). It distinguishes from sibling tools like get_theorem or workspace_search_theorems by focusing on the current paper, though 'theorem-like' is somewhat vague.

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 provided on when to use this tool versus alternatives such as workspace_search_theorems or get_theorem. The description does not mention any conditions or preferred scenarios.

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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