Single 0–100 'green score' for a zone: 100 ≈ very renewable / low carbon, 0 ≈ fossil-heavy. Use as a compact signal for ‘should I run compute now?’ when a numeric score is easier than raw carbon + mix.
| Name | Required | Description | Default |
|---|---|---|---|
| zone | Yes | Bidding zone code (e.g. DE, or FR vs DE depending on market). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It explains the return value (a single 0–100 score) and its interpretation (100 = renewable, 0 = fossil-heavy), which is sufficient for a simple read operation. It does not mention edge cases or error conditions, but these are not critical for this straightforward tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loads the core function ('Single 0–100 green score for a zone'), and provides scale explanation and usage context without unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema or annotations, the description fully explains what is returned (score and meaning), why it is useful, and when to use it. For a simple single-parameter tool, this is complete, and the sibling tool list provides additional context without needing explicit mention in the description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the 'zone' parameter, so the schema already provides full parameter documentation. The description adds no additional parameter semantics beyond the schema, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies a concrete verb ('get') and resource ('green score for a zone'), and clearly defines the output scale (0–100). It also distinguishes itself from sibling tools by positioning this as a compact signal relative to 'raw carbon + mix', making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit usage context: 'Use as a compact signal for should I run compute now?' and contrasts with 'raw carbon + mix', implying an alternative. It does not explicitly state when not to use, but the guidance is clear enough for an agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.