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linancn

TianGong-LCA-MCP Server

by linancn

Search_Life_Cycle_Models_Tool

Search life cycle assessment (LCA) models data to find environmental impact models for sustainability analysis and decision-making.

Instructions

Search LCA life cycle models data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesQueries from user
Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It only states the basic action of searching without mentioning any behavioral traits like whether it's read-only, what format results return, if there are rate limits, authentication requirements, or how results are structured. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 extremely concise with a single sentence that directly states the tool's function. There's zero wasted language or unnecessary elaboration, making it efficiently front-loaded and appropriately sized for its limited content.

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's purpose (searching specialized LCA data), no annotations, no output schema, and sibling tools with similar search functions, the description is incomplete. It doesn't explain what 'life cycle models data' encompasses, how results are returned, or how this differs from other search tools, leaving the agent with insufficient context to use it effectively.

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 information beyond what the schema provides. With 100% schema description coverage (the single 'query' parameter is well-described in the schema), the baseline is 3. The description doesn't enhance understanding of the query parameter's purpose or usage context.

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

Purpose3/5

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

The description states the tool's purpose as searching LCA life cycle models data, which is clear but vague. It specifies the verb 'search' and resource 'LCA life cycle models data', but doesn't differentiate from sibling tools like Search_Flows_Tool or Search_Processes_Tool, leaving ambiguity about what specifically distinguishes this search function.

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. With sibling tools like Search_Flows_Tool and Search_Processes_Tool available, there's no indication of what type of data this searches or when it's the appropriate choice compared to other search tools in the server.

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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