search_knowledgeSemantic search over the workspace knowledge base. Returns the most relevant chunks (with source document title and similarity score) for a query. Only searches documents that have finished training (status READY).
| Name | Required | Description | Default |
|---|---|---|---|
| topK | No | Max number of chunks to return (default 5). | |
| query | Yes | The search query / user question. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that the tool only queries trained documents and returns specific fields. Read-only nature is implied and not contradictory.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no wasted words. First sentence states purpose, second adds a critical constraint. Information is front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but description adequately explains return format. Constraints are clearly stated. For a search tool, it covers all essential aspects and works well with sibling context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but description adds value by explaining the return format (chunks with title and similarity) which relates to parameter effects. This goes beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description states 'semantic search over the workspace knowledge base' with clear verb 'search' and resource 'knowledge base'. It specifies return format (chunks with source title and similarity score). Distinguishes from siblings like list_knowledge and add_knowledge.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides a key usage condition: only searches documents with status READY. Does not explicitly mention when not to use or compare to other search tools, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Tools are largely distinct, with clear purposes for knowledge management, conversations, FAQs, and setup. The only potential overlap is between 'search' (general help) and 'search_knowledge' (workspace KB), but descriptions clarify the context.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., add_knowledge, list_conversations, manage_faq). No mixing of conventions or vague verbs.
17 tools is well-scoped for a live-chat and AI agent workspace server. The set covers setup, knowledge base, conversations, FAQs, analytics, keywords, and embedding without being overwhelming.
The tool surface is comprehensive for core workspace management and support: setup, knowledge ingestion/search, conversation handling, FAQs, analytics, and keywords. Minor gaps like user management or advanced channel configuration, but nothing that critically hinders agent workflows.