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trippborstel-hub

northwood-carbon MCP server

list_initiatives

Retrieve decarbonization initiatives filtered by portfolio company and status for cost-curve analysis, payback screening, and status roll-ups.

Instructions

List decarbonization initiatives with estimated reduction, capex, and payback.

Args: portco: Optional. Filter to a single portco. If omitted, returns all portfolio initiatives. status: Optional. One of 'planned', 'in_progress', 'complete'.

Returns: List of initiative dicts: {portco, id, name, category, status, est_reduction_tco2e, capex_k, start_date}.

Useful for cost-curve analysis, payback screening, and status roll-ups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
portcoNo
statusNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Handler function that lists decarbonization initiatives. Accepts optional portco and status filters. Iterates over INITIATIVES data, filtering by portco slug and initiative status, and returns a list of dicts with id, name, category, status, est_reduction, capex, and start_date.
    @mcp.tool()
    def list_initiatives(
        portco: str | None = None, status: str | None = None
    ) -> list[dict[str, Any]]:
        """
        List decarbonization initiatives with estimated reduction, capex, and payback.
    
        Args:
            portco: Optional. Filter to a single portco. If omitted, returns all portfolio initiatives.
            status: Optional. One of 'planned', 'in_progress', 'complete'.
    
        Returns:
            List of initiative dicts: {portco, id, name, category, status, est_reduction_tco2e,
            capex_k, start_date}.
    
        Useful for cost-curve analysis, payback screening, and status roll-ups.
        """
        rows: list[dict[str, Any]] = []
        slugs = [_resolve_portco(portco)] if portco else list(INITIATIVES.keys())
        for slug in slugs:
            for init in INITIATIVES[slug]:
                if status and init["status"] != status:
                    continue
                rows.append(
                    {
                        "portco": PORTCOS[slug]["name"],
                        "slug": slug,
                        "id": init["id"],
                        "name": init["name"],
                        "category": init["category"],
                        "status": init["status"],
                        "est_reduction_tco2e": init["estReduction"],
                        "capex_k": init["capex"],
                        "start_date": init["startDate"],
                    }
                )
        return rows
  • server.py:195-195 (registration)
    Tool registration via the @mcp.tool() decorator, which registers list_initiatives as an MCP tool with the FastMCP server.
    @mcp.tool()
Behavior3/5

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

With no annotations, the description carries the full burden of disclosing behavior. It explains the filtering options and return structure, which is sufficient for a read-only list tool. However, it does not mention any potential side effects, authorization requirements, rate limits, or error handling. A higher score would require more detailed behavioral context, such as pagination or data freshness.

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 the main purpose stated in the first sentence. The Args and Returns sections are clearly formatted and front-loaded. Every line serves a purpose with no redundancy or fluff. It is well-structured and easy to parse quickly.

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 that the tool has an output schema (though not provided in the input), the description adequately documents the return format with field names and descriptions. The two optional parameters are fully explained. For a straightforward list tool, the description covers all essential information (purpose, parameters, returns, use cases). It only lacks advanced context like pagination or sorting, which would be nice but not necessary.

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?

The input schema has zero description coverage, but the description's Args section explicitly explains each parameter's purpose and values. For status, it enumerates the valid options ('planned', 'in_progress', 'complete'), which adds semantic value beyond the schema's bare type definitions. The description also clarifies that omitting portco returns all portfolio initiatives, providing default behavior context.

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 begins with 'List decarbonization initiatives with estimated reduction, capex, and payback,' which is a specific verb-resource combination. It clearly distinguishes this tool from its siblings: list_portcos lists companies, gap_to_target and simulate_reduction are analytical, and get_portco_emissions retrieves emission data. The purpose is unambiguous and well-scoped.

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 states 'Useful for cost-curve analysis, payback screening, and status roll-ups,' which provides clear context on when to use the tool. However, it does not explicitly exclude scenarios where alternatives might be better, nor does it mention prerequisites or when not to use it. The sibling tools are not directly compared, but the use cases are well defined.

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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