get_slack_threadThe saved messages of one Slack thread, oldest first: the parent and every reply the brain holds. Only threads from channels the user switched on are stored; an empty result means the thread was not captured, and the fix is enabling that channel on /user/integrations/slack.
| Name | Required | Description | Default |
|---|---|---|---|
| thread_ts | Yes | The thread's parent ts (from a search result). | |
| channel_id | Yes | The channel id (C…/D…). |
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?
Beyond the readOnlyHint annotation, the description discloses ordering (oldest first), that only saved messages are returned, the channel enablement prerequisite, and the meaning of an empty result with a remediation path. This is strong behavioral context for a read-only tool.
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?
Every sentence carries load: the first defines the result and ordering, the second defines the availability boundary and empty-result interpretation. There is no redundant filler.
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 two-parameter read operation with no output schema, the description gives enough to call it correctly: data scope, order, and failure semantics. Nothing essential is missing.
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?
The input schema already describes both parameters with 100% coverage for channel_id and thread_ts. The description adds no extra parameter-level meaning beyond what the schema provides, so it meets the baseline but does not go further.
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 identifies the tool as reading one Slack thread's saved messages, including parent and replies, oldest first. It does not explicitly name a sibling tool to distinguish it from, though the scope and storage limitation make it distinct from generic search or messaging tools.
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?
It gives practical context: only threads from enabled channels are stored, and an empty result means the thread wasn't captured, with the fix being to enable that channel. It stops short of explicitly saying when to choose this over search_slack_messages or other Slack tools, so no exclusions are stated.
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.
Most tools are scoped to a distinct resource and action, and descriptions do a good job separating close pairs like search_connections vs ask_about_connections or get_my_linkedin_posts vs linkedin_analytics. However, the multiple deletion tools (delete_graph_object, delete_graph_objects, purge_graph_object) and the several file-reading tools are easy to confuse without reading the descriptions carefully.
The vast majority of tools follow a clear verb_noun pattern such as create_, get_, list_, search_, send_, and delete_. A handful of noun-phrase outliers like linkedin_analytics, mutual_connections, top_message_correspondents, and what_needs_attention break the pattern, so it is highly consistent but not perfect.
64 tools is an extreme count, far beyond the typical well-scoped 3-15 tool range and even beyond the 25+ threshold for 'too many'. While the server covers many integrations, this many tools creates a heavy navigation burden and would be better split into focused servers per domain.
Core graph/CRM operations and read-side integration coverage are strong, with search, get, list, and create tools across most domains. However, there are notable dead ends: no delete_calendar_event, no tracker management beyond create_tracker, and set_follow_up explicitly lacks a read-back query tool, so some natural user requests cannot be completed through the toolset.