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Glama

Get tweets

get_my_tweets
Read-only

The user's own tweets, newest first, from their connected X (Twitter) accounts — text, engagement metrics (likes, replies, retweets, impressions), date, and link. Reads the synced archive, so it's fast; a brand-new connection may not have synced yet. query filters tweet text. If it reports no account connected, tell the user to connect one at /user/integrations/twitter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax tweets to return (default 20, max 100).
queryNoCase-insensitive substring to filter tweet text.
handleNoOne connected @handle to read. Omit to read all connected accounts.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint=true annotation, the description discloses the ordering guarantee, the archive-based data source with its freshness caveat for new connections, and a concrete failure mode ('reports no account connected') with the prescribed remediation. This is exactly the kind of behavioral context annotations cannot convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four tight sentences, front-loaded with the core purpose and return fields. The sync caveat and error-recovery instruction each earn their place in a single clause; 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.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only tool with three optional parameters and no output schema, the description covers the return fields, ordering, data freshness limitation, and the no-account failure path with the follow-up action. An agent has everything needed to select, invoke, and handle failures correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description mentions that `query` filters tweet text, but the schema already specifies a case-insensitive substring filter in equal or greater detail, and `limit` and `handle` are fully documented in the schema. The description adds no meaning beyond the structured definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('user's own tweets, newest first, from their connected X (Twitter) accounts') and enumerates the return fields (text, engagement metrics, date, link). It is readily distinguishable from sibling tools get_twitter_profile and get_twitter_following, which cover profile data and follow relationships rather than the user's own posts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear operational context: data comes from a synced archive so it is fast but may lag for brand-new connections, and it prescribes the exact recovery action if no account is connected ('tell the user to connect one at /user/integrations/twitter'). It does not explicitly name sibling alternatives or state when-not-to-use, which keeps it from a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0
Disambiguation5/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness4/5

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.