Skip to main content
Glama
barvhaim

Israeli Land Authority MCP Server

by barvhaim

get_recent_results

Retrieve completed Israeli land tenders with published results from recent days for market analysis and trend monitoring.

Instructions

Get tenders with results from recent days

Find completed tenders with published results for market analysis and trend monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
argsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main MCP tool handler function 'get_recent_results' decorated with @mcp.tool(). It takes RecentResultsArgs, calls the api_client helper, and returns formatted success/error response.
    @mcp.tool()
    def get_recent_results(args: RecentResultsArgs) -> Dict[str, Any]:
        """
        Get tenders with results from recent days
    
        Find completed tenders with published results for market analysis
        and trend monitoring.
        """
        try:
            results = api_client.get_recent_results(days=args.days)
    
            if isinstance(results, list):
                tender_list = results
            else:
                tender_list = results.get("results", results)
    
            return {
                "success": True,
                "count": len(tender_list),
                "days_back": args.days,
                "recent_results": tender_list,
            }
        except Exception as e:
            return {"success": False, "error": str(e), "days_back": args.days}
  • Pydantic model RecentResultsArgs that defines the input schema for the tool, with a single 'days' parameter (int, default=30).
    class RecentResultsArgs(BaseModel):
        """Arguments for recent results query"""
    
        days: int = Field(30, description="Number of days to look back for results")
  • Core API client method implementing get_recent_results by calling search_tenders with has_results=True and a date filter for the last N days.
    def get_recent_results(self, days: int = 30) -> List[Dict[str, Any]]:
        """
        Get tenders with results from the last N days
    
        Args:
            days: Number of days to look back
    
        Returns:
            List of tenders with recent results
        """
        date_from = datetime.now() - timedelta(days=days)
        return self.search_tenders(
            has_results=True, submission_date_from=date_from, page_size=10000
        )
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It indicates the tool retrieves data ('get', 'find') and implies read-only behavior by focusing on 'completed tenders with published results,' but doesn't specify critical details like authentication requirements, rate limits, pagination, error handling, or data freshness. For a tool with no annotation coverage, this is a significant gap in transparency.

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 highly concise and well-structured, consisting of two sentences that efficiently convey the tool's purpose and use case. The first sentence states the core functionality, and the second adds context without redundancy. Every sentence earns its place, making it front-loaded and easy 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 the tool's moderate complexity (1 parameter, no annotations, but with an output schema), the description is partially complete. It covers the basic purpose and use case, but lacks details on behavior, parameters, and usage guidelines. The presence of an output schema reduces the need to explain return values, but the description doesn't fully compensate for missing annotations and low parameter coverage, leaving gaps for effective tool invocation.

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 1 parameter ('days') with 0% description coverage in the schema itself. The description adds some context by implying a time-based filter ('recent days'), but doesn't explain the parameter's semantics beyond that. It doesn't clarify the default value, valid ranges, or how 'days' interacts with the query. With low schema coverage, the description compensates minimally, meeting the baseline for adequate but incomplete parameter information.

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 tool's purpose: 'Get tenders with results from recent days' and 'Find completed tenders with published results for market analysis and trend monitoring.' It specifies the verb ('get', 'find'), resource ('tenders with results', 'completed tenders'), and scope ('recent days', 'published results'). However, it doesn't explicitly differentiate from sibling tools like 'get_active_tenders' or 'search_tenders', which likely have overlapping domains.

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. It mentions use cases ('market analysis and trend monitoring') but doesn't specify prerequisites, exclusions, or compare to sibling tools like 'get_active_tenders' (which might fetch ongoing tenders) or 'search_tenders' (which could have broader filtering). This leaves the agent to infer usage context without explicit direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/barvhaim/remy-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server