Allen Brain API MCP Server

get_section_data_sets_by_product

List section data sets (experiments) from Allen Institute products, filtered by product IDs and optionally including failed runs.

Instructions

List section data sets (experiments) produced as part of one or more Allen Institute products.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "productIds": "<productIds>"
    },
    "name": "get_section_data_sets_by_product"
  }
}

Input Schema

Name	Required	Description
`productIds`	Yes	Product IDs to filter by. See http://api.brain-map.org/api/v2/data/query.json?criteria=model::Product for a full list.
`includeFailed`	No	If true, include failed data sets (default: false).
`numRows`	No	Maximum number of rows to return (default is 50).

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0May 29, 2026

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the full burden. It lacks disclosure of default behaviors (e.g., numRows default 50), error handling, or the meaning of 'failed' data sets. Only the parameter names in the schema hint at these.

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

Conciseness5/5

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

One sentence, no filler, front-loaded purpose. Every word is necessary.

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?

For a listing tool with no output schema, the description is adequate but minimal. It identifies the resource but does not describe return structure or pagination. Sufficient given schema richness.

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

Parameters3/5

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

The input schema covers 100% of parameters with descriptions, so the baseline is 3. The description adds no additional parameter information beyond implying productIds is the filter.

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?

The description clearly states the verb (List) and resource (section data sets/experiments) with a specific filter (by product). It distinguishes from siblings like get_cell_specimens or download tools, but does not explicitly differentiate from get_mouse_connectivity_experiments.

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 guidance on when to use this tool versus alternatives like get_mouse_connectivity_experiments or when not to use it. The description only states what it does, not the context.

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