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lmwharton

lmwharton/sieve-mcp

sieve_dataroom

Read-only

Get a list of all documents in a deal's data room, including uploaded file names and current processing status.

Instructions

List all documents in a deal's data room.

Shows what files and content have been uploaded for a deal, along with their processing status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
deal_idYesThe deal ID (from sieve_deals or sieve_dataroom_add).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The @mcp.tool decorator registers sieve_dataroom as an MCP tool with read-only annotation.
    @mcp.tool(
        annotations={
            "readOnlyHint": True,
            "destructiveHint": False,
            "openWorldHint": True,
        }
    )
  • The handler function sieve_dataroom takes a deal_id and delegates to client.dataroom().
    async def sieve_dataroom(deal_id: str) -> dict:
        """List all documents in a deal's data room.
    
        Shows what files and content have been uploaded for a deal,
        along with their processing status.
    
        Args:
            deal_id: The deal ID (from sieve_deals or sieve_dataroom_add).
        """
        return await client.dataroom(deal_id)
  • The client.dataroom() function sends a GET request to /api/v1/public/dataroom/{deal_id}.
    async def dataroom(deal_id: str) -> dict[str, Any]:
        """List data room contents for a deal."""
        return await _request("GET", f"/dataroom/{deal_id}")
  • The _request helper function makes the actual HTTP call with error handling for the Sieve Public API.
    async def _request(
        method: str,
        path: str,
        *,
        json_body: dict[str, Any] | None = None,
        timeout: float = 15.0,
    ) -> dict[str, Any]:
        """Execute an HTTP request and return the JSON response or an error dict."""
        if not SIEVE_API_KEY:
            return {
                "error": "Missing API key",
                "detail": "Set the SIEVE_API_KEY environment variable. "
                "Get your key at https://app.sieve.arceusxventures.com/settings",
            }
    
        url = f"{SIEVE_API_URL.rstrip('/')}{_BASE}{path}"
        start = time.monotonic()
        result: dict[str, Any] = {}
    
        try:
            async with httpx.AsyncClient(timeout=timeout) as client:
                response = await client.request(
                    method, url, headers=_headers(), json=json_body
                )
                response.raise_for_status()
                result = response.json()
                return result  # type: ignore[no-any-return]
    
        except httpx.HTTPStatusError as exc:
            try:
                body = exc.response.json()
            except Exception:
                body = exc.response.text
            result = {
                "error": f"HTTP {exc.response.status_code}",
                "detail": body,
            }
            return result
Behavior4/5

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

Annotations provide readOnlyHint and non-destructive nature. Description adds context that it shows processing status, which is additional behavioral info beyond annotations.

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?

Two sentences, front-loaded with purpose, no redundant words, efficiently conveys the tool's function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists and single parameter, the description covers what the tool returns (files, content, processing status) and is complete for agent use.

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 adds no new meaning beyond the schema's parameter description, which is already sufficient with 100% coverage, so baseline 3.

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 'List all documents in a deal's data room' with a specific verb and resource, and differentiates from siblings like sieve_dataroom_add (add documents).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The tool's purpose is clear (listing documents), but it lacks explicit guidance on when to avoid using it or alternatives, though it's implied by the resource context.

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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