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papergraph-mcp

papergraph-mcp

by MahmoudSedky147
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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workspace_get_proof_dependencies

Fetch proof dependency evidence for a stored result to see which theorems and proofs support it. Use it to explore how mathematical claims interconnect within a paper's dependency graph.

Instructions

Return proof dependency evidence for one stored evidence result.

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": {
      "result_id": "<result_id>"
    },
    "name": "workspace_get_proof_dependencies"
  }
}

Input Schema

NameRequiredDescriptionDefault
recursiveNo
result_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.1

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are present, so the description carries the full burden. It signals a read operation via 'Return' but does not disclose behavior such as whether dependencies are direct or recursive, performance implications, or output format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single efficient sentence with no filler, and the core action is front-loaded. It sacrifices helpful detail but is not padded.

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?

For a tool with no output schema and a very close sibling (workspace_get_dependencies), the description is too thin: it omits recursive behavior, result_id provenance, output shape, and when to use alternatives.

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 only implies that result_id identifies a stored evidence result. It does not explain the meaning or effect of the recursive parameter (default false), nor what an evidence result ID looks like.

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?

States a specific action ('Return') and resource ('proof dependency evidence for one stored evidence result'), which distinguishes it from generic dependency tools in the sibling list. However, it does not explain what 'proof dependency evidence' means or how this differs from workspace_get_dependencies.

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 on when to choose this tool over siblings such as workspace_get_dependencies or get_dependencies. The description only states what it returns, leaving selection to inference.

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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