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PaperGraph MCP

PaperGraph MCP

by lotchuazzz-crypto
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

workspace_create_reading_queue

Create a persistent reading queue from a stored result, recursively organizing related theorems into a structured list for focused review.

Instructions

Create a persistent reading queue for one stored 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_create_reading_queue"
  }
}

Input Schema

NameRequiredDescriptionDefault
labelNo
recursiveNo
result_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.9.3

TDQS

C2.7/5.0
Behavior2/5

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

With no annotations provided, the description must bear the full behavioral transparency burden. It discloses that the queue is 'persistent', but does not explain side effects, whether creating multiple queues for the same result is allowed, whether the operation is idempotent, or what happens to existing queues.

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 concise sentence with no filler. It front-loads the core purpose, though the brevity crosses into under-specification for the parameters.

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 tool has no output schema and no annotations, so the description is the only source of behavioral and parameter context. It is too sparse to fully support correct invocation, especially for label and recursive, and it does not explain what the tool returns or how it fits into the reading-queue workflow.

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%, so the description must compensate. It only hints at the meaning of result_id by saying 'one stored result', but it provides no explanation of label or recursive, both of which have defaults and likely affect queue behavior.

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 states the action ('Create'), the resource ('persistent reading queue'), and the scope ('for one stored result'). It is specific enough to distinguish from similar concepts like creating a reading session, but it does not explicitly name or contrast sibling tools.

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?

The description provides no guidance on when to use this tool versus alternatives such as workspace_create_reading_session, workspace_list_reading_queues, or workspace_apply_reading_queue_to_session. There is no mention of prerequisites, expected workflow, or exclusions.

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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