Skip to main content
Glama
Sign In
research-hub

research-hub

by WenyuChiou
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

label_paper

Assign, add, or remove labels to paper notes for categorizing and organizing research.

Instructions

Set, add, or remove labels on a paper note.

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": {
      "slug": "<slug>"
    },
    "name": "label_paper"
  }
}

Input Schema

NameRequiredDescriptionDefault
slugYes
labelsNo
addNo
removeNo
fit_scoreNo
fit_reasonNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.4/5.0
Behavior2/5

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

No annotations exist, so the description must carry the full burden. It only says 'set, add, or remove labels' without detailing behavioral traits like whether setting replaces all labels, what happens with duplicates, or how fit_score/fit_reason interact. The mutation aspect is implied but not elaborated.

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

Conciseness3/5

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

The description is a single sentence, which is concise. However, it is too vague and does not earn its place by informing the agent adequately. It could be expanded to cover critical aspects without being verbose.

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?

Given the tool has 6 parameters, no annotations, and an output schema (but not referenced), the description is incomplete. It omits how fit_score/fit_reason are used, what the output contains, and any usage constraints. A minimal description for a complex label manipulation tool should do more.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must explain parameters. It mentions nothing about 'slug', 'labels', 'add', 'remove', 'fit_score', or 'fit_reason'. Parameter names provide some hint, but the description does not map them or clarify how they work together.

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 verb (set, add, remove) and the resource (labels on a paper note). It distinguishes the tool's purpose from sibling tools that deal with paper existence or clustering. However, it doesn't differentiate among the three operations or explain when to use each.

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 like 'mark_paper' or 'apply_fit_check_to_labels'. The description does not mention prerequisites, context, or when to use set vs add vs remove.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools