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hlydecker

UCSC Genome Browser MCP Server

by hlydecker
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

list_schema

Retrieve field definitions for a specific UCSC Genome Browser data track to understand its structure and available annotations.

Instructions

List the schema (field definitions) for a specified data track.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
genomeYesGenome assembly name
trackYesTrack name
hub_urlNoURL of track/assembly hub (optional)

Implementation Reference

  • ucsc-genome-mcp.py:402-409 (handler)
    Handler logic for the 'list_schema' tool: constructs parameters from arguments (genome, track, optional hubUrl) and makes an API request to the UCSC Genome Browser's /list/schema endpoint.
    elif name == "list_schema":
    params = {
        "genome": arguments["genome"],
        "track": arguments["track"],
        "hubUrl": arguments.get("hub_url")
    }
    url = build_api_url("/list/schema", params)
    result = await make_api_request(url)
  • ucsc-genome-mcp.py:214-230 (schema)
    Input schema defining the parameters for the list_schema tool: required genome and track strings, optional hub_url.
    inputSchema={
    "type": "object",
    "properties": {
        "genome": {
            "type": "string",
            "description": "Genome assembly name"
        },
        "track": {
            "type": "string",
            "description": "Track name"
        },
        "hub_url": {
            "type": "string",
            "description": "URL of track/assembly hub (optional)"
        }
    },
    "required": ["genome", "track"]
  • ucsc-genome-mcp.py:211-232 (registration)
    Registration of the 'list_schema' tool in the MCP server's list_tools() function, including name, description, and input schema.
    Tool(
    name="list_schema",
    description="List the schema (field definitions) for a specified data track.",
    inputSchema={
        "type": "object",
        "properties": {
            "genome": {
                "type": "string",
                "description": "Genome assembly name"
            },
            "track": {
                "type": "string",
                "description": "Track name"
            },
            "hub_url": {
                "type": "string",
                "description": "URL of track/assembly hub (optional)"
            }
        },
        "required": ["genome", "track"]
    }
),

Tool Definition Quality

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 carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe behavioral traits such as whether it's read-only, what the output format looks like, potential rate limits, or error conditions. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.

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 a single, efficient sentence that directly states the tool's purpose without any fluff. It's appropriately sized and front-loaded, with every word earning its place. No structural issues or unnecessary elaboration.

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?

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on output format, error handling, or usage context. Without annotations or output schema, the agent has incomplete information about what to expect from the tool.

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 schema description coverage is 100%, with all parameters documented in the schema (genome, track, hub_url). The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.

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 tool's purpose with the verb 'list' and resource 'schema (field definitions) for a specified data track.' It distinguishes from siblings like list_tracks (which lists tracks) and list_genomes (which lists genomes), but doesn't explicitly differentiate from them. The purpose is specific but lacks explicit sibling comparison.

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. It doesn't mention prerequisites, when-not-to-use scenarios, or compare with sibling tools like list_tracks or get_track_data. The agent must infer usage from the purpose 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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