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effytech

Freshdesk MCP server

by effytech

search_tickets

Search for support tickets in Freshdesk using specific queries to streamline ticket management and enhance customer support operations.

Instructions

Search for tickets in Freshdesk.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes

Implementation Reference

  • The handler function for the 'search_tickets' tool. It is decorated with @mcp.tool(), which registers it as an MCP tool. The function takes a query string and performs a search on the Freshdesk API's /search/tickets endpoint, returning the JSON response.
    @mcp.tool()
    async def search_tickets(query: str) -> Dict[str, Any]:
        """Search for tickets in Freshdesk."""
        url = f"https://{FRESHDESK_DOMAIN}/api/v2/search/tickets"
        headers = {
            "Authorization": f"Basic {base64.b64encode(f'{FRESHDESK_API_KEY}:X'.encode()).decode()}"
        }
        params = {"query": query}
        async with httpx.AsyncClient() as client:
            response = await client.get(url, headers=headers, params=params)
            return response.json()
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 but offers minimal information. It mentions searching but doesn't describe what the search returns (e.g., list of tickets, pagination), performance characteristics, or any constraints like rate limits or authentication needs. This is inadequate for a tool with potential complexity.

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 with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly, though this brevity comes at the cost of detail.

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

Completeness2/5

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

Given the lack of annotations, output schema, and low schema coverage, the description is insufficiently complete. It doesn't address key aspects like return values, error handling, or how this tool differs from similar siblings, leaving significant gaps for an agent to operate effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, and the description provides no information about the single required parameter 'query'. It doesn't explain what the query should contain (e.g., keywords, filters, syntax) or how it affects results, failing to compensate for the lack of schema documentation.

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 ('Search for') and resource ('tickets in Freshdesk'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_tickets' or 'search_contacts', which would require more specificity about what makes this search distinct.

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?

No guidance is provided on when to use this tool versus alternatives like 'get_tickets' or 'search_contacts'. The description lacks context about appropriate use cases, prerequisites, or exclusions, leaving the agent to infer usage from the name alone.

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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