add_slack_reactionAdd an emoji reaction (by name, e.g. 'eyes' or 'white_check_mark') to a Slack message.
| Name | Required | Description | Default |
|---|---|---|---|
| ts | Yes | The message's ts. | |
| emoji | Yes | Emoji name without colons. | |
| team_id | No | ||
| channel_id | Yes |
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?
Annotations only signal that this is not read-only (readOnlyHint: false). The description accurately discloses the mutation and adds emoji-name format examples, but it does not mention permissions, idempotency, workspace/team disambiguation, or what happens on failure.
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?
A single sentence with a parenthetical example; every word earns its place and the core action is front-loaded. No filler or redundant restatement of the title.
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?
The tool is simple and has no output schema, so the short description is adequate for basic invocation when channel_id and ts are already known. However, it lacks guidance on how those identifiers are obtained, making it incomplete for an agent that must discover the message before reacting.
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 only 50%, but the description only adds meaning for the emoji parameter (examples of valid names). It does not explain channel_id or team_id, and ts is left as the schema's minimal 'The message's ts.'
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?
States a specific action ('Add'), a specific resource ('a Slack message'), and the object ('an emoji reaction'). It also gives concrete emoji-name examples, making it unmistakably distinct from siblings like send_slack_message or get_slack_thread.
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. It does not mention that send_slack_message should be used for sending text, or that ts/channel_id must come from an existing Slack message lookup such as get_slack_thread or search_slack_messages.
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 targets a distinct resource and action, with clear boundaries even within overlapping domains like LinkedIn (search vs. free-form query vs. profile vs. summary) and graph deletion (soft single, bulk soft, permanent single). Descriptions explicitly cross-reference related tools to prevent misselection.
The vast majority follow a consistent verb_noun pattern (get_, list_, search_, create_, delete_, etc.). A few noun-phrase exceptions like linkedin_analytics, mutual_connections, similar_objects, and what_needs_attention deviate slightly, but they are still descriptive and do not create confusion.
At 66 tools this is far beyond the 25+ threshold considered too many, even though the server covers many integration domains. Each domain has a coherent subset, but the overall surface is heavy for agents to navigate and would benefit from consolidation or namespacing.
The set provides deep read/search coverage across Gmail, Slack, Calendar, LinkedIn, HubSpot, Obsidian, Twitter, and a graph store, with core write operations for calendar, drafts, Slack, and graph objects. Minor gaps exist—notably no calendar delete, no direct Gmail send to third parties (only drafts), and no LinkedIn post/message actions—but these appear deliberate and do not block typical workflows.