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lalrow

AI Makerspace MCP Demo Server

by lalrow

science_term

Explain science terms using Wikipedia summaries to clarify complex concepts for educational purposes.

Instructions

Explain a science term using Wikipedia summaries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
termYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • server.py:80-91 (handler)
    The handler function for the 'science_term' tool, decorated with @mcp.tool() for registration. It fetches a Wikipedia summary for the input term and returns a formatted response or appropriate error message.
    @mcp.tool()
    def science_term(term: str) -> str:
        """Explain a science term using Wikipedia summaries."""
        try:
            url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{term}"
            r = requests.get(url)
            if r.status_code == 200:
                data = r.json()
                return f"🔬 {data.get('extract', 'No summary found.')}"
            return f"⚠️ Wikipedia error: {r.status_code}"
        except Exception as e:
            return f"❌ Something went wrong: {e}"        
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'explains' using 'Wikipedia summaries,' implying a read-only, informational operation, but doesn't detail aspects like rate limits, authentication needs, error handling, or response format. The description is minimal and lacks rich behavioral context beyond the basic action.

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, efficient sentence with zero waste: 'Explain a science term using Wikipedia summaries.' It's front-loaded with the core purpose and appropriately sized for the tool's simplicity, making it easy to parse quickly.

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?

Given the tool's low complexity (one parameter) and the presence of an output schema, the description is somewhat complete but has gaps. It covers the basic action and source, but without annotations or parameter details, it lacks behavioral and usage context. The output schema mitigates the need to explain return values, but overall completeness is minimal.

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

Parameters3/5

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

The description adds no parameter-specific information beyond what the input schema provides. Schema description coverage is 0%, and the description doesn't explain the 'term' parameter's semantics (e.g., format, examples, or constraints). However, with only one parameter, the baseline is 4, but the description fails to compensate for the lack of schema details, resulting in a score of 3.

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 tool's purpose: 'Explain a science term using Wikipedia summaries.' It specifies the verb ('explain'), resource ('science term'), and method ('using Wikipedia summaries'). However, it doesn't explicitly differentiate from sibling tools like 'animal_fact' or 'space_fact' beyond the domain focus, which prevents a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools (e.g., 'web_search' for broader queries or 'animal_fact' for biology terms) or specify contexts where it's preferred or inappropriate. Usage is implied by the domain ('science term'), but no explicit when/when-not instructions are given.

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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