reservoirs
The 45 metered reservoirs with their latest levels.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
The 45 metered reservoirs with their latest levels.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 provided, the description carries the full burden of disclosing behavior. It only states the data content and does not explicitly say the tool returns data, describe side effects, mention read-only status, pagination, or freshness/update behavior.
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 a single nine-word sentence with no filler words. The core subject, '45 metered reservoirs,' is front-loaded, and every word contributes meaning.
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 parameterless list tool, this is nearly complete: it identifies the full set of items and the key attribute returned. It could be more explicit about return format or point to the singular 'reservoir' tool for detail, but no invocation-level information is missing.
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 input schema has zero parameters, so the baseline is 4. There is nothing for the description to add about parameters, and it correctly does not invent 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 names the exact resource (45 metered reservoirs) and the data returned (latest levels), so an agent can tell this is a list/collection tool rather than a detail tool. It lacks an explicit verb like 'list' or 'get' and does not explicitly differentiate from the sibling 'reservoir' tool, so it misses a 5.
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?
No guidance is given on when to use this tool versus the singular 'reservoir' tool or any other alternative. The plural phrasing implies a collection overview, but the description never states an exclusion, prerequisite, or recommended choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target clearly distinct resources or actions: reservoirs vs reservoir, superfund_summary vs superfund_site, and the various search tools are separated by domain. A few pairs could be confused at a glance—officer_lookup vs search_officers and meeting vs search_meetings—but their descriptions remove practical ambiguity.
The naming is readable but mixes conventions: some tools use verb_search (search_meetings, search_officers), some use noun_noun (reservoir, superfund_site, trading_post_ledger), and others use a mix like officer_lookup and register_verify. There are consistent subgroups, but no overarching verb_noun pattern.
At 19 tools, the server is on the heavier side, but the breadth of the platform—meetings, reservoirs, superfund sites, officers, legacy conversion, evidence packs, and trading post—justifies most of them. Each tool names a meaningful capability, and none feels redundant enough to cut outright.
The set covers the main read/query lifecycle for its data domains: listing, searching, fetching details, and summarizing. The largest gap is that paid conversions and evidence-pack results hand off to external HTTP endpoints or email rather than being fully queryable inside the MCP, but that appears intentional.