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ptorsten

humaans-mcp

by ptorsten

list_spaces

Retrieve a complete list of all physical office spaces in your organization. View location details for space management.

Instructions

List all physical office spaces.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The tool handler function `list_spaces` decorated with @mcp.tool(). Calls `client().list_page('/spaces', limit=250)` to list all physical office spaces from the Humaans API.
    @mcp.tool()
    async def list_spaces() -> Any:
        """List all physical office spaces."""
        return await client().list_page("/spaces", limit=250)
  • The tool is registered via the @mcp.tool() decorator, which is a FastMCP decorator that automatically registers the function as an MCP tool.
    @mcp.tool()
  • The `list_page` method on HumaansClient, which is the underlying API call used by list_spaces. It constructs query params and calls the Humaans API.
    async def list_page(
        self,
        path: str,
        filters: dict[str, Any] | None = None,
        limit: int = 100,
        skip: int = 0,
    ) -> Any:
        params = dict(filters or {})
        params["$limit"] = limit
        params["$skip"] = skip
        return await self.get(path, params)
Behavior3/5

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

The description indicates a read operation listing all spaces, but lacks details about pagination, permissions, or other behavioral traits. Without annotations, it carries minimal extra information.

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 extremely concise (4 words) and front-loaded, with no wasted words.

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?

For a simple list tool with no parameters and no output schema, the description is adequate but does not elaborate on return values or data fields.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are no parameters, so the description need not add meaning beyond the schema. Baseline 4 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 action (list) and the resource (physical office spaces). However, it does not explicitly distinguish itself from sibling tools like list_locations, which may cause confusion.

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 any context about limitations or prerequisites.

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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