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by WenyuChiou
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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list_papers_by_label

Retrieve papers from a research cluster, optionally filtered by label. Use to find papers with specific tags or statuses.

Instructions

Return paper states for the cluster, optionally filtered by label.

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": {
      "cluster_slug": "<cluster_slug>"
    },
    "name": "list_papers_by_label"
  }
}

Input Schema

NameRequiredDescriptionDefault
cluster_slugYes
labelNo
label_notNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.7/5.0
Behavior2/5

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

No annotations provided, so description must disclose behavior. It fails to mention important traits like read-only nature, permission requirements, pagination, or what exactly 'paper states' means.

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?

Single sentence, no wasted words. However, it is too brief and lacks critical detail, making it less useful despite conciseness.

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?

With 3 parameters (no schema descriptions), no annotations, but an output schema, the description leaves many unknowns (e.g., what 'paper states' are, how filters combine, output format). Insufficient for effective tool selection and use.

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%, so description should compensate. It only mentions 'filtered by label' but doesn't clarify the difference between 'label' and 'label_not' or explain 'cluster_slug'.

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 the tool returns paper states for a cluster with optional label filtering, which is clear. However, 'paper states' is somewhat vague and doesn't distinguish from sibling tools like 'list_clusters' or 'search_papers'.

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 use this tool versus alternatives. There are many sibling tools for listing, filtering, or labeling papers, but the description provides no selection criteria.

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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