get_twitter_profileLive profile stats for the user's connected X (Twitter) accounts — name, @handle, follower count, following count, and total tweets, fresh from the X API. If it reports no account connected, tell the user to connect one at /user/integrations/twitter.
| Name | Required | Description | Default |
|---|---|---|---|
| handle | No | One connected @handle. Omit for all connected accounts. |
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 already mark this as read-only, and the description adds useful behavior beyond that: it returns live data from the X API and reports when no account is connected, including the exact user-facing connection path. This exceeds the minimal safety signal provided by readOnlyHint.
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 tightly written sentences: the first names the resource and data fields, and the second provides a practical fallback instruction for the no-account case. There is no filler or repetition of schema content.
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 one-optional-parameter read-only tool with no output schema, the description is complete: it enumerates all returned fields, clarifies the data source, and covers the main user-facing failure mode. Nothing essential is missing for an agent to invoke and interpret the tool.
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 description coverage is 100%, so the handle parameter is already documented as an optional connected @handle, with omission meaning all accounts. The description adds no new parameter semantics beyond this, which matches the baseline expectation for high schema coverage.
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 tool's job: retrieving live profile statistics (name, handle, follower/following counts, tweet totals) for the user's connected X accounts. It uses a specific verb and resource, but it does not explicitly differentiate itself from sibling tools such as get_my_tweets or get_twitter_following.
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 gives clear context for when to use the tool: for live profile stats of connected X accounts, with guidance to have the user reconnect when no account is found. It does not explicitly mention alternatives or exclusions, but the purpose statement is specific enough to imply appropriate usage.
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.