get_messages_summaryOverview of the user's imported LinkedIn messages (DMs): how many messages and conversations they have, the date range covered, and how many they sent versus received. Use for 'how many messages do I have?' and questions about the shape of their message history.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
The annotations declare readOnlyHint=true, and the description adds meaningful behavioral context: it returns an overview/aggregate summary rather than individual messages, covers only imported LinkedIn data, and includes date-range and sent/received dimensions. This goes beyond what the annotation alone conveys.
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, front-loaded with the core value and then concrete use cases. Every sentence contributes useful information without repetition or 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 zero-parameter, read-only summary tool with no output schema, the description is complete: it enumerates the exact dimensions of the summary (message count, conversation count, date range, sent/received) and gives example queries. An agent can confidently decide to use it and predict the response shape.
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?
There are zero parameters and schema description coverage is 100%, so there is no parameter documentation burden on the description. The description still usefully implies the tool operates over the full imported message history without requiring arguments.
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 resource (the user's imported LinkedIn messages/DMs) and the specific purpose: producing a high-level summary with counts, conversation count, date range, and sent/received breakdown. This is distinct from siblings like top_message_correspondents or get_connections_summary because it focuses on aggregate shape of LinkedIn message history.
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?
The description explicitly states when to use the tool: for 'how many messages do I have?' and questions about the shape of message history. It does not explicitly list when not to use it or name alternatives, but the provided use cases are concrete enough to guide an agent.
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.