Skip to main content
Glama
hjlrosales

EPANET MCP Server

by hjlrosales

get_link_results

Retrieve simulation results for network links such as flow, velocity, headloss, and quality, with options for specific timesteps and link IDs. Returns min/max/mean over time when timesteps are omitted.

Instructions

Query simulation results for links (flow, velocity, headloss, unitHeadloss, quality, status, setting, head). Defaults to the last timestep and includes min/max/mean over time unless timesteps are given. Link ids are capped at 250, timesteps at 100. Pass the runId returned by run_simulation to pin results to a specific run; omit for the latest run. headloss is TOTAL headloss (unit headloss x length/1000 for pipes; valves report the raw column value). unitHeadloss is the raw per-1000-unit value. Pumps report head (head added, positive when pumping normally); their headloss/unitHeadloss are null. status is a raw EPANET status code (legend in units.status); statusLabel/statusWarning are decoded per link type. Units and a qualityAnalyzed flag are included in every response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
runIdNoThe run id returned by run_simulation. Omit to use the latest run.
offsetNoZero-based index of the first link to return; combine with the cap to page through large networks.
linkIdsNoLink ids to query. Omit for all links (capped at 250 per page).
networkIdYesThe network id returned by load_network.
timestepsNoZero-based reporting timestep indices. Omit to get the last timestep plus min/max/mean over time.
propertiesNoResult properties to return. Defaults to flow.
Behavior5/5

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

With no annotations, the description carries the full burden, and it excels. It discloses defaults (last timestep, min/max/mean), caps (250 links, 100 timesteps), runId behavior, and special cases for pumps (head vs headloss), status decoding, and the qualityAnalyzed flag. This level of detail prevents misinterpretation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than typical but every sentence adds information. It's front-loaded with the core purpose and then covers defaults, limits, runId usage, and property semantics. No redundant or filler content; the density is justified by the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given there is no output schema, the description compensates by outlining return characteristics (units, qualityAnalyzed flag, statusLabel/statusWarning). It explains all edge cases and default behaviors, making it effectively complete for an agent to call correctly without external knowledge.

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?

Schema coverage is 100%, so each parameter has a basic description. The tool description adds substantial value by explaining semantic nuances: the meaning of headloss (total vs unit), pump-specific behavior, and how timesteps/runId interplay affect results. This goes beyond the schema's literal field names.

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 opens with a specific verb and resource: 'Query simulation results for links' and enumerates the exact properties available (flow, velocity, headloss, etc.). This immediately distinguishes it from sibling tools like get_links (network structure) and get_node_results. It also clarifies it's about simulation results, not static network data.

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 gives clear usage context: defaults to last timestep, explains how to pin to a specific run with runId, and mentions caps for paging. It doesn't explicitly state when not to use it or name alternatives, but the context is sufficient for an agent to select it appropriately for querying link results.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/hjlrosales/EPAnet-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server