Skip to main content
Glama
Sign In
papergraph-mcp

papergraph-mcp

by MahmoudSedky147
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_theorem

Retrieve the full text and metadata of a specific theorem by its ID from the paper dependency graph, making its statement and properties directly accessible for analysis.

Instructions

Return the full text and metadata for one theorem-like node.

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": {
      "theorem_id": "<theorem_id>"
    },
    "name": "get_theorem"
  }
}

Input Schema

NameRequiredDescriptionDefault
theorem_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.1

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 carries the burden. 'Return' clearly signals a read-only operation and the description discloses what is returned, but it does not address behavior for missing or invalid IDs, access restrictions, or output shape beyond 'full text and metadata'.

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?

A single, front-loaded sentence with no filler or redundancy; every word adds meaning.

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?

The tool is simple and the description covers the core return value and scope, but with no output schema and no annotations it leaves unresolved how the ID is supplied and what happens on edge cases. It is minimally viable but not complete.

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 description coverage is 0% and the description never mentions theorem_id or how it identifies the node. The parameter name is fairly self-explanatory, but the description does not compensate for the lack of schema-level documentation.

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 specific verb ('Return') and resource ('one theorem-like node'), and clarifies the payload ('full text and metadata'). It is distinct from listing or searching siblings, though it does not name an alternative explicitly.

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

Usage Guidelines3/5

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

The phrase 'for one theorem-like node' implies this is for fetching a single item by ID rather than listing or searching, but there is no explicit when-to-use guidance or mention of alternatives such as list_theorems or workspace_search_theorems.

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

Deploy Server

Other Tools