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PaperGraph MCP

PaperGraph MCP

by lotchuazzz-crypto
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

workspace_export_result_reading_context

Export focused evidence context for a result's proof by supplying its result ID, so you can see the relevant theorem dependencies and verify the proof efficiently.

Instructions

Export focused evidence context for reading one result's proof.

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_export_result_reading_context"
  }
}

Input Schema

NameRequiredDescriptionDefault
result_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.9.3

TDQS

B3/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It does not state whether the operation is read-only, whether it has side effects, what the exported context contains, or what format is returned. 'Export' alone is ambiguous.

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 front-loaded sentence with no filler. Every word contributes to identifying the operation and its target, making it highly concise while remaining readable.

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 no annotations, the description should explain what is returned or exported and when to use this instead of similar export/get tools. It leaves those gaps open, so an agent does not have enough context to invoke it confidently.

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 coverage is 0% and the description only indirectly defines result_id as identifying 'one result's proof.' It does not explain what form the ID takes, how to obtain it, or how it relates to result contexts in sibling tools, so the schema gap is only minimally compensated.

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 ('Export') and a clear target: 'focused evidence context for reading one result's proof.' This distinguishes it from session-level exports like workspace_export_reading_bundle or workspace_export_reading_session_summary, though it does not explicitly name those alternatives.

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 reading one result's proof' implies the usage context, but the description gives no explicit when-to-use guidance or exclusions relative to the many sibling tools (e.g., workspace_get_evidence, workspace_export_reading_bundle). An agent must infer which tool to choose.

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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