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papergraph-mcp

papergraph-mcp

by MahmoudSedky147
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

workspace_get_result

Fetches a specific evidence result by ID, including its supporting source spans for verification and analysis.

Instructions

Return one stored evidence result with source spans.

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_result"
  }
}

Input Schema

NameRequiredDescriptionDefault
result_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.1

TDQS

B3.1/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. 'Return ... with source spans' conveys an idempotent read and the included content, which is adequate for a simple getter, but it does not disclose not-found behavior, error cases, or whether source spans are always present.

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 sentence with no filler, front-loading the action and the key output detail. Every word contributes.

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?

For a one-parameter getter, this is close to sufficient, but without an output schema or usage guidance the agent is left without a clear return shape beyond 'source spans' or context about when this tool is preferable to its siblings.

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 loosely implies that result_id selects the stored evidence result. It does not explain the ID format, how to obtain it, or what 'source spans' contains, so the agent must infer parameter semantics from the tool name.

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 clear verb ('return') and resource ('one stored evidence result') and adds the output characteristic 'with source spans'. This distinguishes it from list_results and result_proof tools, though it does not explicitly name a sibling.

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 given about when to use this tool versus workspace_get_evidence, workspace_get_result_proof, or workspace_list_results. The use case is implied by the name but never stated, and no exclusions or alternatives are mentioned.

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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