Twitter MCP Server

Server Quality Checklist

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Latest release: v0.3.11
Disambiguation5/5
Each tool has a distinct purpose: text-only posting, image posting, and searching. No overlap in functionality, making it easy for an agent to select the correct tool.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern ('post_tweet', 'post_tweet_with_image', 'search_tweets'), making them predictable and easy to understand.
Tool Count4/5
With only 3 tools, the set is minimal but covers core actions for a Twitter server. The count is slightly low but reasonable for a focused implementation.
Completeness2/5
The set lacks essential operations like deleting tweets, retrieving a single tweet, or accessing timelines, which limits the agent's ability to perform common Twitter workflows.
Average 4.5/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 47 commits in the last 12 weeks
- Last stable release on July 7, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It mentions the return value (success boolean) and constrains the action parameter, but it does not discuss potential side effects, rate limits, authorization requirements, or error scenarios. Adequate but lacks depth.
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?
The description is two sentences long, with the first sentence defining the tool's actions and the second providing usage context and return info. It is front-loaded and contains no redundant or unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, the description covers purpose, usage, parameters, and return value. The presence of an output schema reduces the need to describe return details. However, it lacks guidance on error handling or prerequisites (e.g., user authentication), which slightly reduces completeness.
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 description coverage is 100%, so the schema already documents both parameters with descriptions. The description reiterates the action enum options and implies the tweet_id's role, adding minimal value beyond the schema. Baseline 3 is appropriate.
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?
The description explicitly states the action set (like, retweet, bookmark) and the target (tweet). It clearly distinguishes from sibling tools like 'post_tweet' or 'draft_quote_tweet' by focusing on engagement actions on existing tweets.
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?
The description provides clear context for when to use the tool ('when the LLM needs to interact with content on X/Twitter') and concrete examples. It does not explicitly state when not to use it, but the context is sufficient to differentiate from posting or drafting tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. It details all fetched content and input format. Lacks mention of rate limits or auth requirements, but for a read-only tool this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured and front-loaded with purpose. Slightly wordy but each sentence provides useful info. Could be tighter, but still efficient.
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?
Given single parameter and output schema, description fully covers what the tool returns and when to use. Complete for a profile lookup context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage 100% with clear description. Description adds value beyond schema by specifying no @ symbol, case-insensitivity, and examples.
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?
Description clearly states it fetches a comprehensive user profile including bio, metadata, metrics, pinned tweet, and recent tweets. Distinguishes from siblings (post_tweet, search_tweets) as a read-only profile tool.
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?
Explicitly states when to use: when LLM needs to understand a user before engaging. Provides examples like checking credibility or deciding to interact. Could mention alternatives but sufficiently clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully carries the burden. It discloses the 280-character limit, optional reply parameter, and return fields. However, it does not mention authentication requirements, rate limits, or any potential side effects beyond posting.
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?
The description is a single paragraph of five sentences, starting with the core purpose. Every sentence adds necessary information without redundancy, making it concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (2 params, 1 required, output schema exists), the description covers purpose, usage, parameters, and return values adequately. It lacks error handling or rate limit info, but for a simple post tool, this is sufficient.
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 description coverage is 100%, so the input schema already documents both parameters well. The description adds value by explaining that it's text-only and clarifying the reply function, but does not significantly augment the schema's meaning.
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?
The description clearly states it posts a text-only tweet to the authenticated account. It distinguishes itself from the sibling tool "post_tweet_with_image" by explicitly stating preference when no media attachment is needed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the tool (publish status updates, share info, announce, reply) and explicitly names an alternative (post_tweet_with_image) for when media is needed. Provides clear context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It details the required file existence, supported formats, character limit, optional reply threading, return object fields, and even mentions the underlying API fallback. Doesn't explicitly state authentication or rate limits, but overall transparent about operation.
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 sentences, each earning its place: purpose/usage, file constraints, character limit + threading option, return value and backend note. Front-loaded with the main action. No unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the moderate complexity (3 params, no nested objects, output schema present), the description covers purpose, usage, parameters, and return. Missing details on error handling or rate limits, but sufficient for an agent to invoke correctly. Output schema supplements return info.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (all 3 parameters described in schema). The description adds value by explaining image_path as a filesystem path with supported formats and existence requirement, and clarifies that reply_to_tweet_id is a numeric ID for threading. This goes beyond the schema alone.
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?
Description clearly states it posts a new tweet with an attached image, distinguishing itself from siblings like post_tweet (text-only) and search_tweets (search). The verb 'Posts' and resource 'new tweet with an attached image file' are specific and unambiguous.
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?
Explicitly states when to use ('when the LLM needs to publish a status update that includes a photo...') and implies when not by mentioning alternatives (post_tweet for text-only). Also explains optional reply_to_tweet_id for threaded context. Lacks explicit rate limits or prerequisites, but provides clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description discloses key behaviors: commentary appears above quoted tweet, character limit (280), and return values (ID, text, URL). It adds context beyond the input schema.
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?
The description is concise at 4 sentences, front-loaded with purpose, and well-structured. Every sentence adds value without redundancy.
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?
Given the tool's simplicity (2 parameters, no nested objects) and the presence of an output schema, the description covers all necessary information: usage, constraints, and return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds meaning by explaining how parameters relate (e.g., quoted tweet embedded below commentary). It reinforces the character limit and purpose of each parameter.
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?
The description clearly states the tool's action: 'Quote retweets an existing tweet with the LLM's commentary.' It distinguishes from siblings like post_tweet by specifying the quoting behavior and provides examples of when to use it.
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?
The description gives explicit when-to-use scenarios (sharing with perspective, endorsement, critique, reaction) and examples. It implicitly distinguishes from post_tweet but does not explicitly state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses the tool's behavior: it looks up the tweet, finds conversation_id, retrieves all tweets in that conversation, and returns them chronologically. It also lists included fields per tweet. No mention of error handling or rate limits, but the core behavior is clear.
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?
The description is concise, with two sentences plus a brief list of included fields. It is front-loaded with the main purpose and structured logically.
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?
Given the simple single-parameter schema and presence of an output schema, the description covers the full process and expected output. It is complete for an agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter (tweet_id) with full schema description. The description adds meaning by explaining how tweet_id is used (to find conversation_id and fetch the thread), going beyond the schema's basic definition.
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?
The description clearly states the tool retrieves the full conversation thread for a tweet, with specific steps (lookup tweet, find conversation_id, fetch all tweets chronologically). It distinguishes from siblings like search_tweets which do not provide thread context.
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?
The description explicitly guides when to use: 'when the LLM needs to understand the context of a conversation, read previous replies, or analyze the full discussion flow.' It does not mention when not to use, but the guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description details behavior: return format for photos vs videos/GIFs, mentioning bitrates and highest quality, along with returned fields (tweet ID, media array, count). Lacks mention of rate limits or auth.
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?
Description is concise, front-loaded with main action, and every sentence adds value without redundancy.
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?
Given single parameter, presence of output schema, and no nested objects, the description fully covers input, output, and behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one parameter. Description adds context by explaining the tool fetches tweet with media expansions, supplementing the schema's brief description.
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?
The description clearly states the tool extracts direct media URLs from a tweet, specifying types (images, videos, animated GIFs). It distinguishes from siblings like post_tweet or search_tweets, which have different purposes.
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?
Explicitly says when to use (e.g., to view attached image, download video) but does not mention when not to use or provide alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description discloses behavioral traits: two modes of operation, output contents (tweet text, author, timestamp, metrics), and pagination limits. It lacks explicit mention of rate limits or authentication requirements but covers the core behavior.
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?
The description is concise yet complete, organized logically: purpose, mode explanation, use cases, output details, and limits. No extraneous information.
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?
With only two parameters, full schema coverage, and an output schema, the description covers all necessary aspects: function, usage, output description, and constraints. Nothing is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description adds context beyond schema by explaining how the query parameter affects behavior and the meaning of max_results, including default and range.
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?
The description explicitly states it searches for recent tweets mentioning the authenticated user or matching a custom query, clearly distinguishing two behavioral modes. It differentiates from siblings like search_tweets by specifying scope and use cases.
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?
The description provides clear use cases such as monitoring mentions and tracking brand mentions, but does not explicitly state when not to use or recommend alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes splitting algorithm (paragraph breaks then sentence boundaries), posting sequence (reply chain), and return (thread with IDs and URL). No annotations to contradict. Full behavioral disclosure.
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?
Front-loaded with main action, then conditions and examples. Every sentence adds value. No fluff.
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?
Covers all necessary aspects: when to use, how it works, return value. Output schema exists but description also mentions thread with IDs and URL. No missing information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% with parameter description, but the tool description adds significant context: how splitting works, 280-char limit, double newline usage. Provides meaningful extra guidance beyond schema.
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?
Clearly states it splits long text into tweets and posts as a threaded reply chain. Distinguishes from siblings like post_tweet (single tweet) and draft_quote_tweet (drafting).
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?
Explicitly says when to use: when content exceeds 280-character limit (e.g., announcements, tutorials). Does not explicitly say when not to use, but context implies short content should use post_tweet. Could be improved with explicit exclusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It thoroughly discloses behavior: returns list with fields, pagination metadata, alternative backends, and count parameter effects (latency, quota). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph that front-loads the main purpose and uses efficient language. Some slight redundancy (e.g., 'Searches' and 'Returns'), but overall each sentence adds value.
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?
Given the tool's low complexity (2 params, no nested objects) and presence of output schema, the description covers purpose, usage, syntax, return fields, pagination, and backend options comprehensively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but description adds significant extra context: query parameter includes detailed advanced syntax examples, count parameter explains range and performance trade-offs. This goes well beyond schema basics.
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?
The description clearly states the tool searches for tweets matching a query, using specific verbs and resources. It distinguishes from sibling tools (post_tweet, post_tweet_with_image) by focusing on search/retrieval rather than posting.
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?
The description explicitly states when to use the tool (when needing to find tweets by keyword, hashtag, etc.) and mentions alternative backends. However, it does not provide explicit negative examples or compare to non-sibling tools.
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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