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papergraph-mcp

papergraph-mcp

by MahmoudSedky147
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

where_used

Find all theorems that reference a given theorem by providing its ID, revealing dependency relationships in mathematical papers.

Instructions

Return theorem-like nodes that reference the given theorem.

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": "where_used"
  }
}

Input Schema

NameRequiredDescriptionDefault
theorem_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.1

TDQS

B3.1/5.0
Behavior2/5

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

There are no annotations, and the description only says that the tool returns referencing nodes. It does not disclose traversal depth, directness of references, or any side effects, though it is implied to be a read-only query.

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 the verb and object front-loaded. There is no filler or redundant content.

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 description is adequate for a simple tool, especially since an output schema exists. However, it lacks contextual details about reference scope (direct vs. transitive) and how this relates to dependency/citation tools, which would help an agent select it correctly.

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?

The single parameter theorem_id is named clearly and referred to as 'the given theorem', but the description adds little beyond the schema. It does not clarify whether the ID is a database key, external identifier, or how it should be formatted.

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?

Clearly states the operation ('return') and target ('theorem-like nodes that reference the given theorem'), which distinguishes it from forward-dependency tools. However, 'theorem-like nodes' is somewhat vague and could be more explicit.

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 sibling tools such as get_dependencies or workspace_get_citations. It does not mention alternatives, limitations, or whether references are direct or transitive.

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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