Skip to main content
Glama
maflot

Allen Brain API MCP Server

by maflot

execute_rma_query

Query the Allen Brain API for arbitrary data models like Specimen or SectionDataSet using RMA criteria. Filter, include related objects, and paginate results.

Instructions

Executes a generic RMA (RESTful Model Access) query against the Allen Brain API. Useful for querying arbitrary data models like 'Specimen', 'SectionDataSet', 'Structure', etc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesThe top-level data model to query, e.g., 'Specimen', 'SectionDataSet'.
criteriaNoRMA criteria clause to filter objects, e.g., '[is_cell_specimen$eq\'true\']'.
includeNoRMA include clause to return associated objects, e.g., 'donor(organism)'.
onlyNoList of fields to include in the response (RMA only clause).
exceptNoList of fields to exclude from the response (RMA except clause).
numRowsNoMaximum number of rows to return (default is 50).
startRowNoPagination start row (default is 0).
Behavior2/5

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

No annotations provided; description lacks behavioral details like read-only nature, rate limits, or error handling, leaving the agent with incomplete info.

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?

Two sentences with no wasted words: introduces the tool and gives usage examples.

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?

Adequate for a generic query tool but lacks explanation of RMA syntax, pagination behavior, or response format; sibling context suggests need for more guidance.

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?

Schema covers all 7 parameters with descriptions; description adds only the acronym expansion and model examples, which is marginal extra value.

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 clearly states it executes a generic RMA query against the Allen Brain API and gives examples of data models, differentiating it from specific sibling tools.

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

Usage Guidelines3/5

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

Implied usage for arbitrary models but no explicit when-to-use or when-not-to-use versus the many specific sibling tools like get_cell_specimens.

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/maflot/allenbrain-mcp'

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