Skip to main content
Glama

examples_show

Inspect a bundled example cluster definition by providing its ID, such as 'cs_swe', to view its sample JSON before copying.

Instructions

Return one bundled example cluster definition. Inspects sample JSON before examples_copy. When to use: before copying an example such as cs_swe. When NOT to use: to list names; use examples_list instead. Args: name: bundled example id, such as cs_swe or bio_protein. Returns: keys name, slug, field, query, definition, year_from, year_to, min_citations, sample_dois, description, error. Example: >>> examples_show("cs_swe") {"slug": "llm-agents-software-engineering"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses the return keys and that it inspects JSON, but does not detail side effects, error handling, or whether it is read-only. Adequate but not fully transparent.

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 concise yet structured with clear sections: action, usage, args, returns, example. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, output schema present), the description covers purpose, parameters, return keys, and an example. It lacks error behavior details, but overall is quite complete.

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

Parameters4/5

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

Schema coverage is 0%, but the description adds meaning by providing examples (cs_swe, bio_protein) and explaining that name is a bundled example id, which compensates well.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns a bundled example cluster definition and mentions inspecting sample JSON before examples_copy, distinguishing itself from siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use (before copying an example) and when not to use (to list names, use examples_list instead), providing clear guidance.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/WenyuChiou/research-hub'

If you have feedback or need assistance with the MCP directory API, please join our Discord server