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cxg-census-mcp

cxg-census-mcp

by MaxMLang
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

count_cells

Count cells in the single-cell atlas using ontology-aware filters and optional grouping by criteria like cell type or tissue.

Instructions

Count cells (filters + optional group_by); tier 0.

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": {
      "filters": "<filters>"
    },
    "name": "count_cells"
  }
}

Input Schema

NameRequiredDescriptionDefault
filtersYes
group_byNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral aspects such as whether the operation is read-only, performance characteristics, or any side effects. The term 'tier 0' is cryptic and does not clarify behavior.

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

Conciseness3/5

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

The description is extremely concise, but it sacrifices necessary detail. It front-loads the core idea but omits important context, making it minimally adequate.

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 complex input schema with many nested filter objects and no output schema, the description is severely incomplete. It does not address the variety of filter options or how results are returned, leaving significant gaps for an agent to interpret correctly.

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 merely lists parameter names ('filters', 'group_by') without explaining their semantics or usage. With 0% schema description coverage, the agent gains no additional insight beyond the property names, which is insufficient for correct invocation.

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 counts cells and mentions the key parameters (filters, optional group_by). The verb 'Count' and resource 'cells' are specific, and the functionality is distinct from sibling tools like list_datasets or gene_coverage.

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, nor does it mention any prerequisites or exclusions. The agent must infer usage from the tool name 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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