Agent Twitter Client MCP

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Every tool has a clearly distinct purpose with no ambiguity. Each tool targets a specific Twitter action (like, retweet, quote, follow) or data retrieval operation (get followers, get tweets, search), and the descriptions make their unique functions immediately apparent. There is no overlap that would cause confusion or misselection.
Naming Consistency4/5
The naming is mostly consistent with a clear verb_noun pattern (e.g., follow_user, get_followers, like_tweet), but there are minor deviations. For example, 'grok_chat' and 'health_check' follow the pattern but stand out as non-core Twitter actions, and 'send_tweet' and 'send_tweet_with_poll' could be more aligned (e.g., 'post_tweet'). Overall, the naming is readable and predictable.
Tool Count5/5
With 14 tools, this is well-scoped for a Twitter client server, covering core social media interactions and data access. Each tool earns its place by addressing a specific need, such as posting, liking, searching, or retrieving user information, without being overly bloated or too sparse for the domain.
Completeness4/5
The tool set provides comprehensive coverage for Twitter operations, including CRUD-like actions (send, like, retweet) and data retrieval (get tweets, search, profile). Minor gaps exist, such as no tools for deleting tweets, managing lists, or handling direct messages, but agents can work around these with the available tools for core workflows.
Average 2.9/5 across 14 of 14 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 23 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on March 8, 2025
- 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.
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}
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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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but provides almost none. 'Quote a tweet' implies a write operation but doesn't disclose any behavioral traits: no mention of authentication requirements, rate limits, whether this creates a public post, what happens on success/failure, or any side effects. This is inadequate for a tool that presumably posts content to a social platform.
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 maximally concise at just three words with zero wasted language. It's front-loaded with the essential action and resource. Every word earns its place, making it immediately scannable and understandable without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a write operation with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what happens after quoting (e.g., returns a tweet ID, posts publicly), doesn't mention authentication or permission requirements, and provides no behavioral context. For a social media posting tool, this leaves critical gaps in understanding how to use it effectively.
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 fully documents all three parameters (text, quotedTweetId, media). The description adds no additional meaning about parameters beyond what's in the schema. The baseline score of 3 reflects adequate coverage through the schema alone, though the description contributes nothing extra.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Quote a tweet' clearly states the verb ('quote') and resource ('a tweet'), making the purpose immediately understandable. It distinguishes this from siblings like 'retweet' or 'send_tweet' by specifying the quote action rather than simple reposting or original posting. However, it doesn't explicitly mention what quoting entails (embedding another tweet with commentary), which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when quoting is appropriate compared to retweeting, sending a new tweet, or other sibling tools. There's no information about prerequisites, context, or exclusions for using this functionality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Get') but doesn't describe what the tool returns (e.g., list of users, pagination), any rate limits, authentication needs, or error conditions. This leaves significant gaps in understanding how the tool behaves.
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 extremely concise with just four words, front-loading the core purpose without any wasted text. It efficiently communicates the essential action, though this brevity contributes to gaps in other dimensions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool with two parameters. It doesn't explain what the tool returns (e.g., a list of follower objects), how results are structured, or any behavioral traits like pagination or error handling, leaving the agent with insufficient context.
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 ('userId' and 'count') with descriptions and constraints. The description adds no additional meaning beyond what the schema provides, such as explaining the relationship between parameters or typical use cases.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('a user's followers'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_following' or 'get_user_profile', which also retrieve user-related data, so it doesn't fully distinguish itself from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer 'get_followers' over 'get_following' or 'get_user_profile', nor does it specify any prerequisites or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It implies a read-only operation ('Get'), but doesn't specify whether it's paginated, what the return format is, or any error conditions. For a tool with zero annotation coverage, this is insufficient to inform the agent adequately.
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, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It is front-loaded and wastes no words, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool that likely returns a list of users. It doesn't explain the return structure, pagination behavior, or any constraints like rate limits. For a tool with two parameters and no structured output documentation, the description should provide more context to be fully helpful.
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?
The input schema has 100% description coverage, providing clear details for both parameters (userId and count with default). The description adds no additional parameter semantics beyond what the schema already documents. According to the rules, with high schema coverage, the baseline is 3, which is appropriate here as the description doesn't enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('users a user is following'), making the purpose immediately understandable. It distinguishes this from sibling tools like 'get_followers' by focusing on following relationships rather than followers. However, it doesn't specify the exact scope (e.g., whether it returns all following or a subset), which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'get_following' over 'get_followers' or 'get_user_profile', nor does it specify prerequisites like authentication needs or rate limits. This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe how it behaves - no information about authentication requirements, rate limits, error conditions, response format, or whether it's read-only (though implied by 'Get'). This leaves significant gaps for an agent to understand operational characteristics.
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 extremely concise - a single sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded with the essential information and wastes no space on redundant details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is insufficiently complete. While it states what the tool does, it doesn't provide enough context about what 'profile information' includes, how results are structured, or any behavioral constraints. The agent would need to guess about the response format and operational characteristics.
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?
The description doesn't add any parameter information beyond what's already in the schema. However, with 100% schema description coverage and only one well-documented parameter ('Twitter username without @'), the schema provides adequate documentation. The baseline score of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('a user's profile information'), making the purpose immediately understandable. It doesn't specifically differentiate from siblings like 'get_followers' or 'get_following', which also retrieve user-related data, but the focus on 'profile information' provides reasonable distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over other user-related tools like 'get_followers' or 'get_user_tweets', nor does it specify any prerequisites or constraints for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('fetch') but doesn't cover critical aspects like rate limits, authentication needs, pagination, error handling, or what the output looks like (e.g., tweet format, ordering). For a tool with no annotation coverage, this leaves significant gaps in understanding its 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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of fetching tweets (involving parameters like count and filters) and the lack of annotations and output schema, the description is incomplete. It doesn't address output format, error cases, or behavioral constraints, leaving the agent with insufficient context to use the tool effectively beyond basic parameter input.
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%, with clear documentation for all parameters (username, count, includeReplies, includeRetweets). The description adds no additional parameter semantics beyond what the schema provides, such as explaining interactions between parameters or usage tips. This meets the baseline of 3 since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Fetch tweets from a specific user' clearly states the verb (fetch) and resource (tweets from a user), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'search_tweets' or 'get_tweet_by_id', which also retrieve tweets but with different scopes or filters.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios like retrieving a user's timeline versus searching across users, or how it differs from 'get_user_profile' for user data. Without such context, the agent must infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Chat with' implies an interactive conversation, it doesn't disclose important behavioral traits such as authentication requirements, rate limits, whether this initiates a new conversation or continues an existing one, or what the typical response format looks like. The description is too minimal for a tool that likely involves API calls and conversation management.
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 extremely concise at just four words, with zero wasted language. It's front-loaded with the core purpose and doesn't contain any unnecessary elaboration. This is an example of efficient communication.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that this is a chat tool with 4 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what kind of responses to expect, how conversations are managed, or any behavioral characteristics. For a tool that likely involves complex interaction patterns, more context is needed.
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?
The input schema has 100% description coverage, so all parameters are documented in the schema itself. The description doesn't add any additional meaning or context about the parameters beyond what's already in the schema descriptions. This meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Chat with') and target ('Grok via Twitter'), providing a specific verb+resource combination. However, it doesn't differentiate this tool from potential sibling tools that might also involve interaction with Grok or Twitter's chat features, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when this tool is appropriate compared to other Twitter interaction tools like 'send_tweet' or 'search_tweets', nor does it specify any prerequisites or context for its use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. 'Like a tweet' implies a write operation that modifies tweet state, but it doesn't disclose behavioral traits such as authentication requirements, rate limits, idempotency, or error handling. For a mutation tool with zero annotation coverage, this is inadequate.
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 extremely concise with just three words, front-loading the core action and resource without any waste. Every word earns its place, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a mutation tool with no annotations, no output schema, and 1 parameter, the description is incomplete. It lacks crucial context like return values, error cases, or behavioral implications. For a tool that modifies data, more information is needed for safe and effective use.
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?
The input schema has 100% description coverage, with the 'id' parameter fully documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate when the schema does all the work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Like a tweet' clearly states the action (like) and resource (tweet), making the purpose immediately understandable. It distinguishes from siblings like 'retweet' or 'quote_tweet' by specifying a different interaction type. However, it doesn't explicitly contrast with all siblings (e.g., 'send_tweet'), keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., authentication), when not to use it, or how it differs from similar actions like 'retweet'. With multiple sibling tools for tweet interactions, this lack of context is a significant gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'retweet' implies a write/mutation operation, the description doesn't specify whether this is reversible, what permissions are needed, if there are rate limits, or what happens on success/failure. This leaves significant gaps for an agent to understand the tool's 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 a single, clear sentence with zero wasted words. It's appropriately sized for a simple tool and front-loaded with the essential action, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't address behavioral aspects like authentication needs, error conditions, or what the tool returns, which are critical for an agent to use it correctly in context with sibling tools.
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?
The input schema has 100% description coverage, with the 'id' parameter clearly documented. The description doesn't add any additional meaning beyond what the schema provides (e.g., it doesn't explain tweet ID format or constraints), so it meets the baseline score when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('retweet') and resource ('a tweet'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'quote_tweet' or 'like_tweet' which are also tweet interaction tools, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'quote_tweet' or 'like_tweet', nor does it mention any prerequisites (e.g., authentication requirements, rate limits, or whether the user can retweet their own tweets). It simply states what the tool does without contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions searching by keyword but doesn't disclose behavioral traits like rate limits, authentication requirements, pagination, result format, or whether it's read-only/destructive. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
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, efficient sentence with zero waste—'Search for tweets by keyword' is front-loaded and directly conveys the core purpose. Every word earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a search tool with potential complexity (e.g., result formatting, limits), the description is incomplete. It lacks context on authentication, rate limits, return values, or error handling, which are crucial for an AI agent to use it effectively. It's minimal but insufficient for full understanding.
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 fully documents parameters (query, count, searchMode). The description adds no additional meaning beyond implying keyword-based search, which aligns with the 'query' parameter but doesn't provide extra context like syntax examples or search scope. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search for') and target resource ('tweets'), with the specific mechanism ('by keyword'). It distinguishes from siblings like 'get_tweet_by_id' (specific ID lookup) and 'get_user_tweets' (user-specific retrieval). However, it doesn't explicitly contrast with other search-like siblings (none exist in the list), so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., authentication), when not to use it (e.g., for user-specific tweets), or compare to siblings like 'get_user_tweets' for user-focused retrieval. Usage is implied by the name but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. 'Post a new tweet' implies a write operation but reveals nothing about authentication requirements, rate limits, error conditions, or what happens when posting succeeds/fails. For a mutation tool with zero annotation coverage, this is inadequate.
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 maximally concise at just three words. Every word earns its place - 'Post' specifies the action, 'new' distinguishes from other tweet operations, and 'tweet' identifies the resource. No wasted words or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is insufficient. It doesn't address what happens after posting, what permissions are required, or how to handle errors. The combination of write operation + missing structured data demands more descriptive context than provided.
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 fully documents all three parameters. The description adds no additional parameter information beyond what's in the schema. This meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Post a new tweet' clearly states the verb ('Post') and resource ('tweet'), making the tool's purpose immediately understandable. However, it doesn't distinguish this from sibling tools like 'quote_tweet' or 'send_tweet_with_poll', which also involve posting tweets with different characteristics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There are multiple tweet-posting siblings (quote_tweet, send_tweet_with_poll) with no indication of when this basic tweet tool is preferred over those specialized versions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the basic action without disclosing behavioral traits. It doesn't mention authentication requirements, rate limits, whether the tweet is public/private, error conditions, or what happens after posting (e.g., returns tweet ID). This is inadequate for a mutation tool.
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, efficient sentence with zero waste. It's appropriately sized and front-loaded with the core functionality, though it could benefit from additional context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what happens after posting (success response, error handling), authentication needs, or platform-specific constraints (e.g., Twitter/X API limits). The 100% schema coverage helps but doesn't compensate for missing behavioral context.
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%, providing detailed parameter documentation. The description adds no additional parameter semantics beyond implying 'poll' is required, which is already in the schema. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Post a tweet with a poll' clearly states the action (post) and resource (tweet with poll), distinguishing it from sibling tools like 'send_tweet' (which lacks poll functionality). However, it doesn't specify the platform (e.g., Twitter/X) or fully differentiate from 'quote_tweet' which also posts tweets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'send_tweet' (for tweets without polls) or 'quote_tweet' (for quoting existing tweets). The description lacks any context about prerequisites, constraints, or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Follow a Twitter user,' which implies a mutation/write operation, but doesn't describe any behavioral traits such as authentication requirements, rate limits, error conditions (e.g., invalid username), or what happens on success (e.g., confirmation message). This leaves significant gaps for safe and effective use.
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, direct sentence: 'Follow a Twitter user.' It is front-loaded with the core action and resource, with zero wasted words. Every part of the sentence earns its place by conveying essential purpose without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a mutation tool with no annotations and no output schema), the description is incomplete. It doesn't address behavioral aspects like authentication needs, rate limits, or response format, which are crucial for a write operation. While the schema covers the single parameter well, the overall context for safe and informed use is lacking.
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?
The input schema has 100% description coverage, with the 'username' parameter clearly documented as 'Username to follow (without @).' The description doesn't add any parameter details beyond what the schema provides, but since schema coverage is high, the baseline is 3. It gets a 4 because with only one parameter, the description's lack of additional info is less critical, and the schema fully compensates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Follow') and resource ('a Twitter user'), making the purpose immediately understandable. It distinguishes from siblings like 'get_followers' or 'get_following' by being a write operation rather than a read operation. However, it doesn't explicitly differentiate from other social actions like 'like_tweet' or 'retweet' beyond the verb itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., authentication status), when not to use it (e.g., if already following), or compare it to similar tools like 'get_following' for checking follow status. The agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'fetch' implies a read operation, but doesn't cover aspects like authentication requirements, rate limits, error handling (e.g., invalid IDs), or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding its 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 a single, efficient sentence with zero wasted words. It front-loads the core purpose ('Fetch a specific tweet') and avoids redundancy, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single parameter, no output schema) and high schema coverage, the description is minimally adequate. However, it lacks context on usage guidelines and behavioral traits, which are important for an agent to invoke it correctly. Without annotations or output schema, more detail would improve 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?
The input schema has 100% description coverage, with the 'id' parameter documented as 'Tweet ID'. The description adds no additional meaning beyond this, as it only repeats 'by ID' without elaborating on format (e.g., numeric string) or constraints. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Fetch a specific tweet by ID' clearly states the action (fetch) and resource (tweet), with the qualifier 'specific' indicating it retrieves a single item. However, it doesn't explicitly differentiate from sibling tools like 'get_user_tweets' or 'search_tweets', which also fetch tweets but with different scopes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a tweet ID), exclusions (e.g., not for bulk retrieval), or comparisons to siblings like 'get_user_tweets' (for multiple tweets by a user) or 'search_tweets' (for query-based results).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe what 'health' means (server status, API availability, rate limit status), what the response format might be, or whether this has any side effects. The description is minimal beyond the basic purpose.
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, clear sentence that states the essential purpose without any unnecessary words. It's perfectly front-loaded and wastes no space, making it ideal for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter diagnostic tool with no output schema, the description provides the basic purpose but lacks important context about what 'health' entails and what information the check returns. It's minimally adequate but leaves significant gaps in understanding the tool's behavior and output.
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?
The tool has zero parameters with 100% schema description coverage, so the schema fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist, earning a baseline score of 4 for not creating confusion about non-existent parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Check') and target ('health of the Twitter MCP server'), making the purpose immediately understandable. It doesn't differentiate from siblings, but that's reasonable since this is a unique administrative tool among Twitter API functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention whether this should be used for monitoring, troubleshooting, or as a prerequisite for other operations, nor does it reference any 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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