ASTRA

np_query_spike_count

Read-onlyIdempotent

Query spikes per minute per electrode over a time window for a given experiment, returning only nonzero results by default.

Instructions

NeuroPlatform v2 DB — SpikeCountQuery: spikes/minute per electrode over a time window

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": "np_query_spike_count"
  }
}

Input Schema

Name	Required	Description	Default
`fsname`	No	Experiment ID	fs264
`window_sec`	No	Look back this many seconds of sim-clock
`nonzero_only`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.1.0Sep 23, 2026

TDQS

C2.7/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint=false, and destructiveHint=false, so the safety profile is fully covered. The description adds the computational semantics (per-electrode rate, time-windowed), which is useful, but says nothing about return shape, sim-clock behavior, or cost/rate limits. With annotations doing the heavy lifting, a 3 is appropriate.

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 compact, front-loaded line with no filler; the metric and scope appear immediately. Its brevity is a virtue structurally, though the space saved is spent on nothing that would aid invocation.

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?

For a query tool with three parameters, one undocumented option, and no output schema, the description is too thin: it omits output format expectations, sibling routing, and the meaning of nonzero_only. Annotations cover safety but the description leaves key invocation details unaddressed.

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 only 67%: fsname and window_sec are documented, but nonzero_only has no description anywhere. The description adds no parameter meaning of its own (no explanation of fsname values, window semantics, or the nonzero_only filter), so it fails to compensate for the gap.

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

Purpose3/5

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

The description names a specific resource and metric (spikes/minute per electrode over a time window) and identifies the platform (NeuroPlatform v2 DB). However, it does not distinguish this from closely-named siblings like np_count_spikes, np_query_spike_events, or np_query_triggers, so an agent cannot tell which to pick without opening schemas.

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 prerequisites, and no mention of the obvious alternatives (np_count_spikes, np_query_spike_events). The agent is left to infer selection from names alone.

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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