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interview_plan_list

Retrieve all interview plans from the Ashby hiring pipeline to coordinate candidate evaluation stages and manage the recruitment process.

Instructions

List all interview plans.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
includeArchivedNoInclude archived plans

Implementation Reference

  • The tool logic is handled dynamically by a generic `handle_call_tool` function that maps tool names to Ashby API endpoints via a mapping table. The tool `interview_plan_list` is mapped to `/interviewPlan.list`.
    @server.call_tool()
    async def handle_call_tool(name: str, arguments: dict[str, Any]) -> list[types.TextContent]:
        """Route tool calls to the correct Ashby endpoint, passing arguments directly."""
        endpoint = TOOL_ENDPOINT_MAP.get(name)
        if not endpoint:
            return [types.TextContent(type="text", text=f"Unknown tool: {name}")]
    
        try:
            # Pass arguments straight through -- tool schemas already use Ashby's
            # camelCase param names so no translation is needed.
            response = ashby.post(endpoint, data=arguments if arguments else None)
            return [types.TextContent(type="text", text=json.dumps(response, indent=2))]
        except requests.exceptions.HTTPError as e:
            error_body = ""
            if e.response is not None:
                try:
                    error_body = e.response.text
                except Exception:
                    pass
            return [
                types.TextContent(
                    type="text",
                    text=f"Ashby API error on {endpoint}: {e}\n{error_body}",
                )
            ]
        except Exception as e:
            return [types.TextContent(type="text", text=f"Error calling {endpoint}: {e}")]
  • Tool definition for `interview_plan_list` in the `server.py` file.
    types.Tool(
        name="interview_plan_list",
        description="List all interview plans.",
        inputSchema={
            "type": "object",
            "properties": {
                "includeArchived": {"type": "boolean", "description": "Include archived plans"},
            },
        },
    ),
Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. While 'List' implies a read operation, it doesn't mention any behavioral traits like pagination, sorting, default ordering, rate limits, authentication requirements, or what happens when no plans exist. For a list tool with zero annotation coverage, this is insufficient.

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 with zero wasted words. It's perfectly front-loaded with the core action and resource, making it immediately scannable and understandable.

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 no annotations and no output schema, the description is incomplete for a list operation. It doesn't explain what an 'interview plan' is, what fields are returned, whether results are paginated, or how this differs from similar list tools. The agent would need to guess about these important contextual details.

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 100%, so the schema fully documents the single parameter 'includeArchived'. The description doesn't add any parameter semantics beyond what's in the schema, but since the schema coverage is complete, 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 verb ('List') and resource ('all interview plans'), making the purpose immediately understandable. However, it doesn't differentiate this tool from other list tools like 'application_list', 'candidate_list', or 'interview_list', which would require mentioning what specifically distinguishes interview plans from those other entities.

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. With siblings like 'interview_list' and 'interview_stage_list', there's no indication of how interview plans relate to or differ from interviews or interview stages, leaving the agent to guess about appropriate usage contexts.

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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