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effytech

Freshdesk MCP server

by effytech

search_agents

Find specific agents in Freshdesk by querying their details. Enhance support operations by integrating AI models to automate and manage customer interactions effectively.

Instructions

Search for agents in Freshdesk.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes

Implementation Reference

  • The handler and registration for the 'search_agents' MCP tool. This async function queries the Freshdesk API's agent autocomplete endpoint with the provided query term and returns the list of matching agents.
    @mcp.tool()
    async def search_agents(query: str) -> list[Dict[str, Any]]:
        """Search for agents in Freshdesk."""
        url = f"https://{FRESHDESK_DOMAIN}/api/v2/agents/autocomplete?term={query}"
        headers = {
            "Authorization": f"Basic {base64.b64encode(f'{FRESHDESK_API_KEY}:X'.encode()).decode()}"
        }
        async with httpx.AsyncClient() as client:
            response = await client.get(url, headers=headers)
            return response.json()
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 mentions 'Search for agents' but doesn't specify whether this is a read-only operation, what permissions are needed, how results are returned (e.g., pagination), or any rate limits. This leaves critical behavioral traits undocumented.

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 a single, efficient sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly, though it could benefit from additional context.

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 complexity of a search operation with no annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't cover parameter details, behavioral traits, or output expectations, leaving significant gaps for an AI 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 input schema has 1 parameter with 0% description coverage, and the tool description provides no information about the 'query' parameter. It doesn't explain what the query should contain (e.g., name, email, partial matches) or its format, failing to compensate for the low schema coverage.

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 ('agents in Freshdesk'), making the purpose unambiguous. However, it doesn't distinguish from sibling tools like 'get_agents' or 'view_agent', which likely serve different purposes (e.g., listing all vs. retrieving specific agents).

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_agents' or 'view_agent'. The description lacks context about prerequisites, such as whether authentication is required or what the search scope entails, leaving the agent to infer usage from the tool 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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