Skip to main content
Glama
Sign In
Plant Simulation Research Bridge

Plant Simulation Research Bridge

by AmaneSuzuha000
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

experiment_summary

Aggregates stored experiment run records into status and policy counts, metric summaries, and validity failure totals for quick analysis.

Instructions

Aggregate stored run records (counts by status/policy, metric aggregates, validity failures).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "experiment_summary"
  }
}

Input Schema

NameRequiredDescriptionDefault
work_dirNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior3/5

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 does disclose the shape of the result (status/policy counts, metric aggregates, validity failures), which is meaningful behavioral context, but it never says where 'stored run records' come from, whether the operation is read-only, or how missing/empty data is handled.

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?

A single dense sentence with the core verb front-loaded and no filler. The parenthetical enumeration is efficient, though it packs several distinct concepts without structural separation.

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?

With no output schema and no annotations, the description does the minimum by listing what the summary contains. It leaves out the scope question (which runs are aggregated, role of work_dir) and any indication of return format, which matters for a tool whose whole output is an aggregate report.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

One parameter, work_dir, has 0% schema description coverage and the description does not mention it at all. The agent must guess whether work_dir selects the scope of aggregated runs, is optional, or defaults to a current workspace.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('aggregate stored run records') and enumerates the aggregate kinds produced (counts by status/policy, metric aggregates, validity failures). It does not distinguish itself from siblings like list_runs or compare_policies, which also operate on run data, so an agent cannot fully disambiguate from the description alone.

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

Usage Guidelines2/5

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

No explicit when-to-use guidance and no alternatives named. The word 'aggregate' weakly implies a summarized view rather than raw run records, but the agent must infer that list_runs is for detail and compare_policies is for policy comparison.

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

Deploy Server

Other Tools