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lmwharton/sieve-mcp

sieve_status

Read-only

Check the progress of a Sieve analysis by providing a deal ID. Returns completed IMPACT-X dimensions with scores, overall progress percentage, and current phase.

Instructions

Check the progress of a Sieve analysis.

Returns which IMPACT-X dimensions are complete with their scores, overall progress percentage, and current phase.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
deal_idYesThe deal ID returned by sieve_screen.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The sieve_status tool is registered as an MCP tool via the @mcp.tool decorator on the sieve_status async function. It is annotated with readOnlyHint=True.
    @mcp.tool(
        annotations={
            "readOnlyHint": True,
            "destructiveHint": False,
            "openWorldHint": True,
        }
    )
    async def sieve_status(deal_id: str) -> dict:
        """Check the progress of a Sieve analysis.
    
        Returns which IMPACT-X dimensions are complete with their scores,
        overall progress percentage, and current phase.
    
        Args:
            deal_id: The deal ID returned by sieve_screen.
        """
        return await client.status(deal_id)
  • The MCP tool handler function sieve_status(deal_id) that accepts a deal_id string and delegates to client.status(deal_id).
    async def sieve_status(deal_id: str) -> dict:
        """Check the progress of a Sieve analysis.
    
        Returns which IMPACT-X dimensions are complete with their scores,
        overall progress percentage, and current phase.
    
        Args:
            deal_id: The deal ID returned by sieve_screen.
        """
        return await client.status(deal_id)
  • The client.status(deal_id) helper function that makes a GET request to /api/v1/public/screen/{deal_id}/status via the internal _request helper.
    async def status(deal_id: str) -> dict[str, Any]:
        """Check analysis progress."""
        return await _request("GET", f"/screen/{deal_id}/status")
  • The _request helper function that executes the actual HTTP request with error handling, analytics, and response parsing.
    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
    
        except httpx.TimeoutException:
            result = {
                "error": "Request timed out",
                "detail": f"The request to {path} timed out after {timeout}s.",
            }
            return result
    
        except httpx.RequestError as exc:
            result = {
                "error": "Connection error",
                "detail": str(exc),
            }
            return result
    
        finally:
            duration_ms = round((time.monotonic() - start) * 1000)
            try:
                if _posthog is not None:
                    _posthog.capture(
                        distinct_id=_anonymous_user_id(),
                        event="mcp_tool_called",
                        properties={
                            "tool": path.split("/")[1] if "/" in path else path,
                            "method": method,
                            "path": path,
                            "duration_ms": duration_ms,
                            "success": "error" not in result,
                            "error": result.get("error"),
                        },
                    )
            except Exception:
                pass  # Never let analytics break the tool
  • The sieve_status tool accepts a single parameter deal_id (str) and returns a dict. The docstring explains it returns progress percentages, completed IMPACT-X dimensions with scores, and current phase.
    async def sieve_status(deal_id: str) -> dict:
        """Check the progress of a Sieve analysis.
    
        Returns which IMPACT-X dimensions are complete with their scores,
        overall progress percentage, and current phase.
    
        Args:
            deal_id: The deal ID returned by sieve_screen.
        """
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, indicating a safe read. The description adds behavioral insight by detailing the specific return values (dimensions, scores, progress percentage, current phase), which goes beyond the 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 the purpose, followed by specifics. No redundant information. Every sentence adds value.

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 that an output schema exists, the description does not need to detail return format. It provides a high-level summary of returns, input is clear, annotations cover safety, and sibling tools are listed. Complete for a progress-checking 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 input schema has full coverage (100%) for the single parameter deal_id, with a clear description. The tool description does not add additional meaning beyond the schema, so baseline 3 is appropriate.

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 the verb 'Check' and the resource 'progress of a Sieve analysis', and lists specific return values (dimensions, scores, percentage, phase). This distinguishes it from siblings like sieve_screen or sieve_results.

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

Usage Guidelines3/5

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

The description implies usage for checking progress after sieve_screen, but does not explicitly state when to use versus alternatives or provide exclusions. Sibling names provide indirect 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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