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lmwharton

lmwharton/sieve-mcp

sieve_deals

Read-only

Retrieve deals from your Sieve pipeline. Search by company name to find specific deals or list all to get deal metadata including Sieve scores.

Instructions

List deals in your Sieve pipeline.

Search by company name or list all deals. Returns deal metadata including Sieve scores for screened deals.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchNoSearch by company name (partial match). Empty returns all.
limitNoMaximum results to return (1-100, default 20).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler function that defines and exposes the sieve_deals tool. It accepts optional search (string) and limit (int, default 20) parameters, and delegates to client.deals().
    async def sieve_deals(search: str = "", limit: int = 20) -> dict:
        """List deals in your Sieve pipeline.
    
        Search by company name or list all deals. Returns deal metadata
        including Sieve scores for screened deals.
    
        Args:
            search: Search by company name (partial match). Empty returns all.
            limit: Maximum results to return (1-100, default 20).
        """
        return await client.deals(search=search, limit=limit)
  • The @mcp.tool decorator registering sieve_deals as an MCP tool with readOnlyHint=True, marking it as a non-destructive, read-only operation.
    @mcp.tool(
        annotations={
            "readOnlyHint": True,
            "destructiveHint": False,
            "openWorldHint": True,
        }
    )
  • The client.deals() helper function that constructs a GET request to /deals with optional search and limit query parameters, then calls the underlying _request() HTTP helper.
    async def deals(search: str = "", limit: int = 20) -> dict[str, Any]:
        """List/search deals in pipeline."""
        params = []
        if search:
            params.append(f"search={search}")
        if limit != 20:
            params.append(f"limit={limit}")
        query = f"?{'&'.join(params)}" if params else ""
        return await _request("GET", f"/deals{query}")
  • Docstring defining the input schema for sieve_deals: search (string for partial company name match, empty returns all) and limit (int, 1-100, default 20).
    Args:
        search: Search by company name (partial match). Empty returns all.
        limit: Maximum results to return (1-100, default 20).
    """
Behavior4/5

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

Annotations already note readOnly and non-destructive; description adds that it returns deal metadata with Sieve scores, which is useful beyond schema. No contradictions.

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 purpose, no filler. 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?

Simple list tool with 2 params, rich annotations, and output schema present. Description covers use and return content adequately.

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 covers both parameters with full descriptions (100% coverage). Description reiterates search behavior (partial match, empty returns all) but adds no new detail beyond schema.

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?

Clearly states 'List deals in your Sieve pipeline,' specifies optional company name search, and distinguishes from sibling tools like sieve_dataroom, sieve_memo, etc. Describes returns including Sieve scores.

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?

Indicates when to use (list/search deals) and scope (company name or all), but lacks explicit when-not or alternatives relative to siblings.

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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