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Glama

extract_lsreader_nodal

Extract LS-Reader nodal vectors for specified node IDs, quantities, and states, using 1-based states and true user IDs to read simulation results accurately.

Instructions

Extract LS-Reader vectors with 1-based public states and true user IDs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
unitsYes
statesYes
node_idsYes
quantityYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden, and it does disclose one genuinely non-obvious convention: states are 1-based and node IDs are true user IDs rather than internal indices. It says nothing about read-only behavior, file access requirements, cost, or what the returned vectors look like, so the disclosure is partial.

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 no filler, and the indexing convention is front-loaded rather than buried. It is arguably too terse for a five-parameter tool, but nothing in it is wasted.

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

Completeness2/5

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

Five required parameters, zero schema descriptions, no annotations, and no output schema means the description is the only carrier of meaning for a fairly complex call. One sentence cannot cover path/quantity/units semantics or the operational conditions for a successful extraction, so it is materially incomplete.

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?

Schema coverage is 0% across five required parameters, so the description must compensate and only does so for two: 'states' (1-based) and 'node_ids' (true user IDs). The remaining params — path, quantity, and especially the format-sensitive units string — are undocumented in both the schema and the description.

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 (Extract) and resource (LS-Reader vectors), which separates it from the d3plot and nodal-results siblings by source type. It does not, however, explain what an 'LS-Reader vector' is or how it differs from extract_nodal_results / extract_node_history, so the agent must infer the distinction from the name 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?

There is no when-to-use guidance, no statement of prerequisites (e.g. an LS-Reader must be initialized or a job present), and no mention of alternatives among the many extraction siblings. The agent gets no routing help at all.

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