slack__message_search
[slack · risk:low] Search recent messages across Slack channels
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query string |
[slack · risk:low] Search recent messages across Slack channels
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query string |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description carries the full burden. It only states 'risk:low' and 'Search recent messages'. It does not disclose what 'recent' means (e.g., time range), whether it searches all accessible channels, rate limits, or whether results are sorted/relevant. Behavioral traits are under-specified.
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?
One clear, efficient sentence with a front-loaded risk indicator. No redundant information. Every word contributes meaning.
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?
For a simple search tool with one parameter, the description is nearly adequate but lacks details on result format, pagination, or error handling. The ambiguity of 'recent' leaves some completeness gaps. Output schema is absent, so description could do more.
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 the parameter description is minimal ('Search query string'). The main description adds context that the search is over 'recent messages' in Slack channels, which is slightly more than the schema. However, it does not explain query syntax (e.g., boolean operators or date filters). Baseline 3 is appropriate.
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?
The description clearly states the verb ('Search') and resource ('recent messages across Slack channels'). It is sufficiently specific to understand the tool's function, but does not explicitly differentiate from other message search tools like gmail__mail_search or twitter__tweets_search. The prefix 'slack__' provides contextual distinction.
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?
No guidance on when to use this tool versus alternatives (e.g., other search tools). There is no mention of prerequisites, time windows for 'recent', or exclusions (e.g., channel scope). The description lacks usage context.
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.
Each tool is prefixed with its service name, and within each service, tools have distinct actions (e.g., mail_read vs. availability_find). The duvera tools cover different subdomains like dev, finance, and food with no overlap, making selection unambiguous.
All tools follow the pattern service__action_object or service__category_action, using lowercase with underscores. The order of verb and noun varies slightly (e.g., package_track vs. boardingpass_show), but the naming is highly predictable and readable.
With 52 tools, the server is large but justified as a gateway aggregating many external services. Each tool corresponds to a common task for its service, so no tool feels extraneous, though the total number is high.
The tool surface covers a wide array of services but only provides one or two basic operations per service (mostly read-only). While this suits a quick-lookup gateway, deeper workflows (e.g., creating or updating resources) are missing, leaving gaps for many use cases.