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list_users

Retrieve all unique participant names from indexed Slack threads to identify valid users for filtering search results.

Instructions

List all known participants across indexed Slack threads.

Scans the thread_users metadata field to return all unique participant names. Useful for discovering valid values for the user_filter parameter in search().

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function that implements the list_users MCP tool. It retrieves all metadata from the vector store, extracts unique user names from the thread_users field, and returns a sorted list of participants with a count.
    @mcp.tool()
    def list_users() -> dict:
        """List all known participants across indexed Slack threads.
    
        Scans the thread_users metadata field to return all unique participant names.
        Useful for discovering valid values for the user_filter parameter in search().
        """
        store = _get_store()
        all_docs = store.get(include=["metadatas"])
    
        users: set[str] = set()
        for meta in all_docs["metadatas"]:
            thread_users = meta.get("thread_users", "")
            if thread_users:
                for name in thread_users.split(", "):
                    name = name.strip()
                    if name:
                        users.add(name)
    
        return {"users": sorted(users), "count": len(users)}
  • server.py:299-299 (registration)
    The @mcp.tool() decorator registers the list_users function as an MCP tool, making it available to clients.
    @mcp.tool()
  • The function signature and docstring define the tool's interface: it takes no parameters and returns a dict with users list and count. The docstring serves as the tool's description for MCP clients.
    def list_users() -> dict:
        """List all known participants across indexed Slack threads.
    
        Scans the thread_users metadata field to return all unique participant names.
        Useful for discovering valid values for the user_filter parameter in search().
        """
Behavior4/5

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. It effectively describes the tool's behavior: scanning metadata to return unique names, implying a read-only operation without destructive effects. However, it lacks details on potential limitations like rate limits, error conditions, or data freshness, which would be helpful for a tool with no annotation coverage.

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 front-loaded with the core purpose in the first sentence, followed by operational details and usage guidance in two additional sentences. Every sentence earns its place by adding distinct value: listing participants, explaining the scanning method, and stating the utility for another tool, with zero wasted words.

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

Completeness4/5

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

Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is largely complete. It covers purpose, behavior, and usage context effectively. A minor gap is the lack of output format details (e.g., structure of returned names), but this is mitigated by the tool's straightforward nature and the explicit mention of 'unique participant names'.

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

Parameters4/5

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds context about the tool's output ('unique participant names') and its relationship to other parameters ('user_filter in search()'), providing value beyond the empty schema without redundancy.

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 specific action ('List all known participants') and resource ('across indexed Slack threads'), distinguishing it from siblings like 'list_channels' or 'search' by focusing on thread participants rather than channels or content. It explicitly mentions scanning 'thread_users metadata field' for 'unique participant names', providing precise operational details.

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

Usage Guidelines5/5

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

The description explicitly states when to use this tool: 'Useful for discovering valid values for the user_filter parameter in search().' This provides clear guidance on its purpose as a helper for another tool ('search'), distinguishing it from alternatives like 'get_thread' or 'collection_stats' without needing to list exclusions.

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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