list_policiesWaitingForPower's six bipartisan permitting-reform policy proposals, one per structural cause category — each with a summary, strengths, weaknesses, and related bills. This is the site's argued position, distinct from the neutral cause categories in list_causes.
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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?
No annotations are provided, so the description carries the behavioral disclosure burden. It discloses the nature of the output (six proposals, each with specified components) and the important framing that this is an argued position rather than neutral content. This gives the agent useful context about what kind of information to expect.
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 with no filler. The first sentence states what the tool returns and the structure of each item; the second sentence immediately clarifies the key distinction from list_causes. Every word earns its place.
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?
For a zero-parameter, no-output-schema tool, the description is fully sufficient. It states the number of proposals, their framing, the per-item fields, and how this tool differs from the most relevant sibling. An agent can confidently invoke this tool without further clarification.
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?
The tool accepts zero parameters and the schema coverage is 100%, so there are no parameter semantics to explain. The description appropriately focuses on the fixed output and contextual meaning instead of irrelevant parameter details.
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 clearly identifies a specific verb/action (list), a concrete resource (WaitingForPower's six bipartisan permitting-reform policy proposals), and the content structure (summary, strengths, weaknesses, related bills). It also explicitly distinguishes this tool from the sibling list_causes, making its purpose immediately clear.
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 clear context on what this tool offers and explicitly contrasts it with list_causes, telling the agent that list_policies returns the site's argued position while list_causes returns neutral categories. It does not discuss usage relative to other siblings, but the distinction is sufficient for the main alternative.
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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Each tool has a clearly distinct role: search, detail lookup, aggregate stats, neutral cause categories, policy proposals, and state reference data. Even the two list tools are explicitly separated as neutral versus argued content, so an agent should not confuse them.
All tool names consistently follow a verb_noun snake_case pattern: get_project, get_stats, list_causes, list_policies, list_states, search_projects. The pattern is predictable and readable.
Six tools is well-scoped for a specialized read-only energy permitting dataset. Each tool earns its place: two for project discovery/detail, one for aggregate stats, and three for supporting reference and policy content.
The surface covers the core workflows for this domain: searching projects, retrieving full details, summarizing aggregate statistics, and accessing the supporting reference data. There are no obvious dead ends or missing operations for a read-only tracker.