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interview_list

Retrieve and browse all interviews in the hiring pipeline using cursor pagination to manage interview stages and track candidate progress.

Instructions

List all interviews with cursor pagination.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results per page
cursorNoCursor for next page

Implementation Reference

  • The `handle_call_tool` function is the generic handler for all MCP tool calls. It retrieves the API endpoint from `TOOL_ENDPOINT_MAP` based on the tool name and executes the request using `ashby.post`.
    @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}")]
  • Schema definition for the `interview_list` tool.
    types.Tool(
        name="interview_list",
        description="List all interviews with cursor pagination.",
        inputSchema={
            "type": "object",
            "properties": {
                "limit": {"type": "integer", "description": "Max results per page"},
                "cursor": {"type": "string", "description": "Cursor for next page"},
            },
        },
    ),
  • Mapping of `interview_list` to the `/interview.list` Ashby API endpoint in the `TOOL_ENDPOINT_MAP` dictionary.
    "interview_list": "/interview.list",
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions 'cursor pagination' which is valuable behavioral information, it doesn't address other critical aspects: whether this is a read-only operation, what permissions might be required, what format the results come in, or any rate limits. For a list operation with zero annotation coverage, this leaves significant behavioral questions unanswered.

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 - a single sentence that communicates the core functionality. Every word earns its place: 'List' (action), 'all interviews' (scope), 'with cursor pagination' (key behavioral characteristic). There's no wasted language or redundancy, making it highly efficient for an AI agent to parse.

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 that this is a list operation with 2 well-documented parameters (100% schema coverage) but no annotations and no output schema, the description provides the minimum viable information. It states what the tool does and mentions pagination behavior, but doesn't address the output format, error conditions, or how this tool relates to other listing tools in the system. For a tool with no output schema, more information about return values would be helpful.

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 description mentions 'cursor pagination' which provides context for the two parameters, but the input schema already has 100% description coverage with clear parameter documentation. The description doesn't add meaningful semantic information beyond what's already in the schema - it doesn't explain how the cursor is obtained, what typical limit values might be appropriate, or provide usage examples. With complete schema coverage, the baseline 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 interviews'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'candidate_list', 'job_list', or 'application_list' - all of which are also list operations. The description is specific about what it lists but not how it differs from other list tools in the system.

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 multiple list tools available (candidate_list, job_list, application_list, etc.), there's no indication of what distinguishes interview_list from these other listing operations. The description doesn't mention prerequisites, use cases, or when this tool would be preferred over other listing mechanisms.

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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