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raqueljezweb

AnythingLLM MCP Server

by raqueljezweb

chat_with_workspace

Send chat messages to specific workspaces for interactive conversations or information queries within the AnythingLLM MCP Server.

Instructions

Send a chat message to a workspace

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesThe workspace slug/identifier
messageYesThe message to send
modeNoChat mode (chat or query)chat
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal information. It states the action ('send a chat message') which implies a write operation, but doesn't disclose whether this requires authentication, what happens to the message after sending, whether there are rate limits, or what the expected response format might be. For a communication tool with zero annotation coverage, this leaves significant behavioral questions unanswered.

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 maximally concise with a single, clear sentence that front-loads the essential action. Every word earns its place with no redundancy or unnecessary elaboration. The structure immediately communicates the core functionality without requiring the agent to parse through verbose text.

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?

For a communication tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what happens after message sending, whether there's confirmation or response, how errors are handled, or any system constraints. The agent lacks crucial context about the tool's behavior and outcomes despite having full parameter documentation in the schema.

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 description adds no parameter-specific information beyond what the schema already provides. With 100% schema description coverage that documents all three parameters (slug, message, mode with enum values), the baseline score of 3 is appropriate. The description doesn't explain what a 'workspace slug' represents, how messages are formatted, or the practical difference between 'chat' and 'query' modes.

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 action ('Send a chat message') and target resource ('to a workspace'), making the purpose immediately understandable. It distinguishes this from sibling tools like 'clear_chat_history' or 'search_workspace' by focusing on message sending rather than history management or search. However, it doesn't specify whether this is for human-to-workspace or agent-to-workspace communication, which prevents a perfect score.

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 doesn't mention when to choose 'chat' vs 'query' mode, how this differs from 'invoke_agent' for agent-based interactions, or any prerequisites like workspace availability. The agent must infer usage entirely from the tool name and parameters without explicit direction.

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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