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shaiksameer667ss-prog

Biomedical Research MCP Server

search_experiments

Filter biomedical experiments by cell type, treatment, organism, test, duration, or text query to find studies matching your criteria.

Instructions

Search experiments using:

  • general text query

  • cell type

  • treatment

  • organism

  • test

  • duration in hours

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
testNo
limitNo
queryNo
organismNo
cell_typeNo
treatmentNo
duration_hoursNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only lists search dimensions and does not explain result behavior, whether filters are ANDed, whether query is a full-text search, pagination, default limits, or any side effects. This is a meaningful gap for a tool with no annotation support.

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?

The description is appropriately sized and structured as a scannable bullet list. Every line adds a distinct filter option and there is no redundant or filler content.

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 tool with 7 parameters, no output schema, and no annotations, the description is incomplete. It lacks guidance on query semantics, filtering behavior, the purpose of the limit parameter, and how results are returned or sorted. An agent would need additional inference or trial-and-error to use the tool reliably.

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 description coverage is 0%, so the description must compensate. It does add value by listing six of the seven parameters and clarifying 'general text query' as the query parameter and 'duration in hours' as the duration filter. However, it omits the 'limit' parameter entirely and does not explain how multiple filters interact or what the default behavior is.

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 'Search' and the resource 'experiments', and enumerates the filterable fields such as cell type, treatment, organism, test, and duration in hours. However, it does not explicitly differentiate itself from sibling search tools like search_research_content or search_research_evidence, so it is clear but lacks sibling differentiation.

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?

The description gives no guidance about when to use this tool versus alternatives such as get_experiment or search_research_content. It does not mention whether filters combine, how to scope a search, or when the other search tools would be more appropriate.

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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