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darronz

Octopus Energy MCP Server

by darronz
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TypeScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools are clearly distinct: one retrieves electricity consumption data and the other retrieves gas consumption data. Their purposes do not overlap, and the descriptions specify different parameters and units, making misselection unlikely.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'get_' prefix and descriptive nouns ('electricity_consumption', 'gas_consumption'). The naming is uniform and predictable across the set.

    Tool Count2/5

    With only 2 tools, the server feels thin for an energy data domain. While it covers electricity and gas consumption, typical energy APIs might include additional operations like tariff lookup, usage analysis, or billing information, suggesting a limited scope.

    Completeness2/5

    The toolset is severely incomplete for an energy data server. It only provides consumption retrieval, lacking essential operations such as tariff queries, historical data analysis, account management, or update capabilities, which could hinder agent workflows.

  • Average 4/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden. It discloses return format (kWh with precision) and parameter fallback behavior (environment variables), but doesn't mention authentication requirements, rate limits, error conditions, or whether this is a read-only operation. The description adds some behavioral context but leaves significant gaps.

    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?

    Two well-structured sentences that efficiently convey purpose, return format, precision, and parameter behavior. Every sentence earns its place with no wasted words, and the most important information (what the tool does) is front-loaded.

    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?

    For a 7-parameter tool with no annotations and no output schema, the description provides adequate basic information about purpose and return format, but lacks details about authentication, error handling, pagination behavior (beyond page_size parameter), and what the actual response structure looks like. The schema covers parameters well, but behavioral context is incomplete.

    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?

    Schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds minimal value beyond the schema by mentioning the environment variable fallback mechanism for mpan and serial_number, but doesn't provide additional semantic context for the other parameters.

    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 the verb ('Get') and resource ('electricity consumption data from Octopus Energy'), specifying the return unit (kWh) and precision (0.045 kWh). It distinguishes from the sibling tool 'get_gas_consumption' by explicitly mentioning electricity.

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

    Usage Guidelines4/5

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

    The description provides clear context about when to use this tool (for electricity consumption data) and mentions environment variable fallbacks for parameters. However, it doesn't explicitly state when NOT to use it or provide alternatives beyond the implicit distinction from the gas consumption sibling.

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

  • Behavior4/5

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

    No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it returns consumption data in different units (kWh for SMETS1, cubic meters for SMETS2), and it explains how parameters can be sourced from environment variables. This adds useful context beyond the input schema, though it could mention rate limits or error handling for completeness.

    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 appropriately sized and front-loaded, starting with the core purpose and key details. Every sentence earns its place by providing essential information without redundancy, making it efficient and well-structured for quick understanding.

    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 complexity of 7 parameters, no annotations, and no output schema, the description does a good job by explaining return units and parameter sourcing. However, it lacks details on output format, error cases, or authentication needs, which would enhance completeness for a data retrieval tool with multiple options.

    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 schema description coverage is 100%, so the input schema already documents all parameters thoroughly. The description adds minimal value by noting that MPRN and serial number can use environment variables, but it does not provide additional meaning for other parameters like period_from or group_by. This meets the baseline of 3 when schema coverage is high.

    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 the verb ('Get') and resource ('gas consumption data from Octopus Energy'), specifying what the tool does. It distinguishes from the sibling tool 'get_electricity_consumption' by focusing on gas rather than electricity, making the purpose specific and differentiated.

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

    Usage Guidelines3/5

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

    The description implies usage by mentioning that MPRN and serial number can come from environment variables if not provided as parameters, which gives some context. However, it does not explicitly state when to use this tool versus the sibling tool or any alternatives, leaving guidance at an implied level without clear exclusions or comparisons.

    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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