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cafferychen777

ChatSpatial

by cafferychen777
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

find_markers

DestructiveIdempotent

Identify differentially expressed genes between cell groups or clusters in spatial transcriptomics data. Specify grouping column and statistical method to get top marker genes per group.

Instructions

Find differentially expressed genes between groups.

Args:
    data_id: Dataset ID
    params: Required - group_key and optional method, group1/group2, n_top_genes, etc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes
data_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
data_idYes
n_genesYes
warningsNoNon-fatal conditions that may affect result interpretation.
comparisonYes
top_genes_by_groupNo

Schema Changelog

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

  1. Changed3 schema fields changedv1.5.3
    • changedOutput schema / description
      Previous value: -"Result of differential expression analysis\n\nNote on serialization:\n    For consistency with other result models, the statistics dict is excluded\n    from JSON serialization. Key summary info is in explicit fields.\n\n    Fields included in MCP response:\n    - data_id, comparison (basic info)\n    - n_genes (count)\n    - top_genes (top differentially expressed genes)\n\n    Fields excluded from MCP response:\n    - statistics (detailed DE metrics per group)"New value: +"Result of differential expression analysis\n\nNote on serialization:\n    For consistency with other result models, the statistics dict is excluded\n    from JSON serialization. Key summary info is in explicit fields.\n\n    Fields included in MCP response:\n    - data_id, comparison (basic info)\n    - n_genes (count of distinct genes returned)\n    - top_genes_by_group (top genes keyed by the group they mark)\n\n    Fields excluded from MCP response:\n    - statistics (detailed DE metrics per group)\n\n    Markers are keyed by group because a flat list cannot say which group a\n    gene marks. One-vs-rest runs compare every group at once, so a flat list\n    would silently collapse to whichever group happened to come first."
    • removedOutput schema / properties / top_genes
      Removed value: -{
-  "items": {
-    "type": "string"
-  },
-  "title": "Top Genes",
-  "type": "array"
-}
    • addedOutput schema / properties / top_genes_by_group
      Added value: +{
+  "additionalProperties": {
+    "items": {
+      "type": "string"
+    },
+    "type": "array"
+  },
+  "title": "Top Genes By Group",
+  "type": "object"
+}
  2. Changed5 schema fields changedv1.3.2
    • addedInput schema / $defs / DifferentialExpressionParameters / additionalProperties
      Added value: +false
    • addedInput schema / additionalProperties
      Added value: +false
    • addedOutput schema / additionalProperties
      Added value: +false
    • removedOutput schema / properties / statistics
      Removed value: -{
-  "additionalProperties": true,
-  "title": "Statistics",
-  "type": "object"
-}
    • addedOutput schema / properties / warnings
      Added value: +{
+  "description": "Non-fatal conditions that may affect result interpretation.",
+  "items": {
+    "type": "string"
+  },
+  "title": "Warnings",
+  "type": "array"
+}
  3. First observedv1.2.7

TDQS

C2.9/5.0
Behavior2/5

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

The description adds no behavioral details beyond what annotations already specify. It does not mention potential side effects, required permissions, or impact on data, despite annotations indicating destructive and non-idempotent behavior.

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?

The description is concise with only one sentence of purpose and a brief parameter list. It is not bloated, but the 'Args' section is redundant with the schema and could be streamlined.

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?

Given the tool's complexity (nested params, multiple optional settings), the description lacks context about prerequisites, return values (though output schema exists), or typical use cases. The output schema is present, so return values are covered, but other contextual information is missing.

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?

The description's 'Args' section vaguely lists a few parameters but adds no explanatory value beyond what the schema already provides. Since schema_description_coverage is 0%, the description fails to compensate for the lack of parameter detail, leaving the agent to rely solely on the schema.

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 the tool's purpose: 'Find differentially expressed genes between groups.' This is a specific and unambiguous action, distinguishing it from sibling tools like compare_conditions or compute_embeddings.

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 provides no guidance on when to use this tool versus alternatives, prerequisites, or typical scenarios. It only states the action without context, leaving the agent to infer usage from the parameter names.

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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