Twitter MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
Most tools have distinct purposes, but there is significant overlap in some areas, such as multiple tweet search tools (advancedTweetSearch, historicalTweetSearch, searchTweets) and several analytics tools (e.g., analyzeFollowerDemographics, userGrowthAnalytics, userInfluenceMetrics) that could cause confusion. Descriptions help differentiate them, but the sheer number of similar tools increases ambiguity.
Naming Consistency4/5
Tool names generally follow a consistent verb_noun pattern (e.g., postTweet, getUserInfo, likeTweet), with only minor deviations like 'addUserToList' (which could be 'addUserToList' is consistent in structure but uses 'add' instead of a more standard verb like 'create'). Overall, the naming is predictable and readable across the set.
Tool Count2/5
With 53 tools, this server is overly comprehensive for a Twitter API wrapper, making it heavy and potentially overwhelming. The scope includes many niche analytics and advanced features that might be better grouped or omitted, as typical MCP servers for social media have 10-30 tools. This count feels excessive and could hinder usability.
Completeness5/5
The tool set provides complete coverage for Twitter operations, including CRUD for tweets, users, lists, and direct messages, along with advanced features like analytics, search, and engagement tracking. There are no obvious gaps; agents can perform all core and extended workflows without dead ends.
Average 3/5 across 53 of 53 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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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

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. While 'Get' implies a read operation, the description doesn't disclose important behavioral traits: whether authentication is required, rate limits, pagination behavior beyond the paginationToken parameter, what 'detailed information' specifically includes, or how results are structured. For a tool with 5 parameters and no annotation coverage, this represents significant gaps in behavioral transparency.
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, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a basic tool description, though it could potentially be more front-loaded with critical information. There's no wasted verbiage or redundant phrasing.
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 tool's complexity (5 parameters, no annotations, no output schema), the description is insufficiently complete. It doesn't explain what constitutes 'specific' events, how results are returned, what the response structure looks like, or how this differs from the sibling 'getDirectMessages' tool. For a data retrieval tool with multiple configuration options and no output schema, the description should provide more context about what the agent can expect.
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 schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description adds no parameter-specific information beyond what's already in the schema - it doesn't explain relationships between parameters, provide usage examples, or clarify semantics. However, with complete schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting for parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'Get specific direct message events with detailed information', which provides a basic verb+resource combination ('get' + 'direct message events'). However, it's vague about what 'specific' means (no filtering criteria mentioned) and doesn't differentiate from the sibling tool 'getDirectMessages' which appears to serve a similar purpose. The description is adequate but lacks specificity and sibling differentiation.
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's no mention of when this tool should be used instead of 'getDirectMessages' (the obvious sibling tool), nor any context about prerequisites, appropriate scenarios, or exclusions. The agent receives no usage direction beyond the basic purpose statement.
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 a read operation ('Get') but does not disclose behavioral traits such as rate limits, authentication needs, pagination, or what happens if the username is invalid. This leaves significant gaps for a tool with parameters.
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, efficient sentence with no wasted words. It is front-loaded and appropriately sized for a simple tool, though it could be more informative without losing conciseness.
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 tool with 3 parameters, the description is incomplete. It does not explain return values, error handling, or behavioral context, making it inadequate for effective tool selection and invocation.
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 like 'username', 'maxResults', and 'userFields'. The description adds no meaning beyond the schema, as it does not explain parameter interactions or usage examples. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get followers of a user' states a clear verb ('Get') and resource ('followers of a user'), but it is vague about scope and does not differentiate from sibling tools like 'getFollowing' or 'getUserInfo'. It lacks specificity such as whether it returns a list, count, or detailed profiles.
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 'getFollowing' (for users followed by a user) or 'getUserInfo' (for user details). The description does not mention prerequisites, context, or exclusions, leaving usage unclear.
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 'analyze' implies a read-only operation, the description doesn't specify whether this requires authentication, what data sources it uses, whether it has rate limits, or what format the analysis results take. For an analytics tool with zero annotation coverage, this leaves significant behavioral gaps.
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, efficient sentence that gets straight to the point without unnecessary words. It's appropriately sized for what it communicates, though it could be more informative. The structure is simple but effective for conveying the basic purpose.
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 an analytics tool with 3 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what kind of analysis is performed, what metrics are returned, or how the results should be interpreted. With no structured output information and minimal behavioral context, this leaves the agent with inadequate guidance.
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 all three parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain how these parameters interact or what specific analysis they enable. With high schema coverage, baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool analyzes user growth patterns and engagement trends over time, which provides a general purpose. However, it's somewhat vague about what specific metrics or outputs are involved, and it doesn't clearly distinguish this tool from sibling analytics tools like 'analyzeFollowerDemographics' or 'userInfluenceMetrics' that might also examine user-related data.
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. With multiple sibling tools that analyze user data (e.g., 'analyzeFollowerDemographics', 'userInfluenceMetrics'), there's no indication of what makes this tool unique or when it should be preferred over others. The description only states what it does, not when to use it.
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 mentions analysis but doesn't specify what the analysis entails (e.g., types of demographics, engagement metrics), whether it's read-only or has side effects, rate limits, or authentication requirements. For a tool with no annotations, this leaves significant behavioral gaps.
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 no wasted words, clearly front-loading the core purpose. It's appropriately sized for the tool's complexity, making it easy to scan and understand 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 tool's moderate complexity (3 parameters, no output schema, no annotations), the description is incomplete. It lacks details on behavioral traits (e.g., read-only vs. side effects), output format, or how it differs from sibling analytics tools. Without annotations or an output schema, 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?
Schema description coverage is 100%, so the schema already documents all three parameters (username, sampleSize, analyzeDemographics) with descriptions and constraints. The description adds no additional parameter semantics beyond what the schema provides, such as explaining how the sample is selected or what demographic data is included. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Analyze follower demographics and engagement patterns' clearly states the tool's purpose with a specific verb ('analyze') and resource ('follower demographics and engagement patterns'), but it doesn't differentiate from sibling tools like 'getFollowers', 'userGrowthAnalytics', or 'userInfluenceMetrics' that might provide related analytics. The purpose is understandable but lacks sibling 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. With siblings such as 'getFollowers' (which retrieves follower lists), 'userGrowthAnalytics' (which might analyze growth trends), and 'userInfluenceMetrics' (which could assess influence), there's no indication of when this specific analysis tool is preferred or what prerequisites exist 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 the full burden of behavioral disclosure. It states the action ('Get analytics') but does not reveal traits like whether this is a read-only operation, requires authentication, has rate limits, or what the analytics include (e.g., engagement metrics, sentiment). This leaves significant gaps for a tool with no structured safety hints.
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 is 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 analytics tools and the lack of annotations and output schema, the description is incomplete. It does not specify what analytics are returned (e.g., metrics, format), behavioral constraints, or how it fits with siblings, leaving the agent with insufficient context for 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 clear parameter descriptions (e.g., 'hashtag to analyze', 'start time for the analysis'). The description adds no additional meaning beyond this, such as explaining what 'analytics' entails or how time ranges affect results. With high schema coverage, the baseline score of 3 is appropriate as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get analytics for a specific hashtag' clearly states the verb ('Get') and resource ('analytics for a specific hashtag'), making the purpose understandable. However, it does not differentiate from sibling tools like 'getHashtagTrends' or 'trendingTopicsSearch', which might also involve hashtag-related data, leaving the scope vague in comparison.
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 lacks context such as whether this is for historical analysis, real-time monitoring, or how it differs from siblings like 'getHashtagTrends' or 'searchTweets', offering no explicit or implied usage rules.
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 offers minimal behavioral insight. It doesn't disclose whether this is a read-only operation, what permissions are needed, rate limits, pagination behavior, or what the return format looks like. For a search 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 a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, though it could benefit from more detail given the tool's complexity.
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 tool's complexity (3 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain return values, error conditions, or behavioral traits, leaving significant gaps for the agent to operate 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 documents all parameters (query, maxResults, tweetFields). The description adds no additional meaning beyond what's in the schema—it doesn't explain query syntax, default values, or field options. Baseline 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Search for tweets using a query string' clearly states the verb ('search') and resource ('tweets'), but it's vague about scope and doesn't differentiate from sibling tools like 'advancedTweetSearch' or 'historicalTweetSearch'. It provides a basic purpose but lacks specificity about what kind of search this performs.
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 'advancedTweetSearch' or 'historicalTweetSearch'. The description doesn't mention any prerequisites, context, or exclusions, leaving the agent to guess based on tool names 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. It states the action but doesn't cover critical aspects like required permissions (e.g., if the user must own the list), rate limits, error conditions, or what happens on success/failure. This is a significant gap 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 directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, 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 this is a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., permissions, side effects), return values, or error handling, which are essential for safe and effective use by an AI agent.
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 ('listId' and 'username') adequately. The description doesn't add any additional meaning beyond what the schema provides, such as format examples or constraints, meeting the baseline for high schema coverage.
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 ('Add a user') and the resource ('to a Twitter list'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from its sibling 'removeUserFromList' beyond the opposite action, missing full sibling 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?
No guidance is provided on when to use this tool versus alternatives like 'followUser' or 'createList', nor are prerequisites mentioned (e.g., needing list ownership or permissions). The description lacks explicit when/when-not instructions or named alternatives.
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. It mentions sentiment analysis but fails to describe key traits like rate limits, authentication needs, output format, or whether it's a read-only or mutative operation. This leaves significant gaps for an agent to understand how to invoke it effectively.
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 unnecessary words or fluff. It is appropriately sized and front-loaded, 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 sentiment analysis, lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., sentiment scores, aggregated results), behavioral constraints, or how it differs from similar tools, leaving the agent with insufficient context for 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?
Schema description coverage is 100%, so the schema already documents all parameters (query, sampleSize, includeKeywords) with descriptions and constraints. The description adds no additional meaning beyond what the schema provides, such as examples or usage notes, resulting in a baseline score of 3.
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 ('perform sentiment analysis') and target resource ('tweets matching a query'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from sibling tools like 'searchTweets' or 'advancedTweetSearch' that might also involve queries, missing explicit differentiation.
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 such as 'searchTweets' or 'advancedTweetSearch', nor does it mention any prerequisites or exclusions. Usage is implied by the purpose but lacks explicit context for selection among siblings.
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 tool retrieves multiple profiles but doesn't mention rate limits, authentication requirements, error handling (e.g., if some usernames are invalid), or the response format. For a tool with no annotations, this is a significant gap in transparency about 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: 'Get multiple user profiles in a single request for comparative analysis.' It is front-loaded with the core purpose and wastes no words, making it highly concise and well-structured for quick understanding.
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 tool's complexity (fetching multiple user profiles with optional metrics), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like rate limits or error handling, and without an output schema, it should ideally hint at the return format (e.g., a list of profiles with metrics). This leaves gaps for effective tool 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 clear documentation for 'usernames,' 'userIds,' and 'includeMetrics.' The description adds no additional parameter semantics beyond what the schema provides, such as explaining how 'usernames' and 'userIds' interact or what 'comparative analysis' entails. 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 clearly states the tool's purpose: 'Get multiple user profiles in a single request for comparative analysis.' It specifies the verb ('Get'), resource ('multiple user profiles'), and context ('for comparative analysis'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'getUserInfo' or 'getAuthenticatedUser,' which likely fetch single user profiles, so it misses full sibling 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 mentions 'comparative analysis' as a use case but doesn't specify prerequisites, exclusions, or compare it to sibling tools like 'getUserInfo' (for single profiles) or 'analyzeFollowerDemographics' (for deeper analysis). This leaves the agent without clear usage context.
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 responsibility for behavioral disclosure. While 'Create' implies a write/mutation operation, the description doesn't mention authentication requirements, rate limits, what happens on success/failure, or whether the list becomes immediately available. This leaves significant behavioral gaps 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 that states exactly what the tool does without any wasted words. It's perfectly front-loaded and appropriately sized for its purpose.
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 explain what happens after creation, what gets returned, error conditions, or authentication requirements. Given the complexity of creating a resource in a social media API, this leaves too many contextual gaps.
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 all three parameters clearly documented in the schema itself. The description doesn't add any additional parameter context beyond what's already in the schema, so it meets the baseline expectation but doesn't provide extra value.
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 ('Create') and resource ('new Twitter list'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'getUserLists' or 'addUserToList', which would require more specific context about what makes this creation tool unique.
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's no mention of prerequisites (like authentication), when this should be used instead of other list-related tools, or any constraints on usage. The agent receives no contextual direction.
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. It states the destructive action ('Delete') but lacks critical details: whether deletion is permanent, if it requires specific permissions (e.g., user authentication), rate limits, or error conditions (e.g., invalid tweet ID). This is inadequate for a mutation tool with zero annotation coverage.
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 with zero wasted words, front-loading the core action and resource. It efficiently communicates the essential purpose without unnecessary elaboration, 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?
For a destructive tool with no annotations and no output schema, the description is insufficient. It doesn't address behavioral risks (e.g., irreversibility), success/error responses, or integration with sibling tools (e.g., 'getTweetById' for validation). Given the complexity of tweet deletion in a social media context, more contextual guidance 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?
Schema description coverage is 100%, with the single parameter 'tweetId' clearly documented in the schema. The description adds no additional semantic context beyond implying the parameter is required for deletion, meeting the baseline score when the schema handles parameter documentation effectively.
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 ('Delete') and target resource ('a tweet by its ID'), making the purpose immediately understandable. However, it doesn't differentiate itself from sibling tools like 'unlikeTweet' or 'undoRetweet' that might also remove tweet-related content, leaving some ambiguity about its specific role in the toolset.
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 ownership of the tweet), exclusions (e.g., cannot delete retweets), or related tools like 'getTweetById' for verification, leaving the agent to infer usage context 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?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'find' implies a read operation, it doesn't specify what 'mutual connections and interactions' actually returns (e.g., list of users, interaction metrics, timestamps), whether there are rate limits, authentication requirements, or data freshness considerations. For a social network analysis 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 a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a tool with clear parameters documented elsewhere and follows good front-loading principles.
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 3 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'mutual connections and interactions' means operationally, what format the results take, or how this differs from related sibling tools. The combination of missing behavioral context and lack of output information creates significant gaps for an AI agent.
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 all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline score of 3 is appropriate when the schema does all the parameter documentation 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 clearly states the verb 'find' and the resource 'mutual connections and interactions between two users', making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'getFollowers' or 'getFollowing', but the focus on mutual relationships is specific enough to avoid confusion with those individual relationship tools.
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. With many sibling tools for analyzing user relationships (getFollowers, getFollowing, mapInfluenceNetwork, userInfluenceMetrics), there's no indication of when mutual connections analysis is preferred over individual relationship queries or broader network analysis.
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 whether this requires authentication, has rate limits, affects user relationships, or what the expected outcome is (e.g., success confirmation or error handling). This is inadequate for a mutation tool with zero annotation coverage.
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, front-loading the core action. It's appropriately sized for a simple tool, 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 tool's complexity (a mutation with no annotations or output schema), the description is incomplete. It doesn't cover behavioral aspects like authentication needs, rate limits, or what happens upon execution, leaving significant gaps for an AI agent to operate 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?
The description implies the 'username' parameter but doesn't add meaning beyond the schema's 100% coverage, which already documents it as 'The username of the user to follow'. With high schema coverage, the baseline is 3, and the description doesn't compensate with extra details like format examples or constraints.
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 target resource ('a user by their username'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'addUserToList' or 'muteUser' which also involve user interactions, so it misses 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 'addUserToList' or 'followUser' versus 'unfollowUser'. It lacks context about prerequisites (e.g., authentication needs) or exclusions, leaving the agent with minimal usage direction.
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 mentions retrieving 'full conversation history' but doesn't clarify what 'full' means, whether there are rate limits, authentication requirements, or how pagination works. The description lacks important behavioral context that would help an agent use this tool effectively.
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 communicates the core purpose efficiently. It's front-loaded with the essential information and contains no unnecessary words or redundant information.
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 4 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what constitutes 'full conversation history,' how pagination works with maxResults and paginationToken, or what the return format looks like. The description leaves too many operational questions unanswered.
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 all parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain relationships between parameters or provide usage examples. Baseline 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 action ('Get full conversation history') and resource ('for a specific conversation'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like getConversationTree or getDirectMessageEvents, but the specificity of 'full conversation history' 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 like getConversationTree, getDirectMessageEvents, or getFullThread. It simply states what the tool does without context about appropriate use cases or comparisons to sibling tools.
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 states 'Retrieve' which implies a read-only operation, but doesn't disclose any behavioral traits like pagination behavior (implied by paginationToken parameter), rate limits, authentication requirements, or what 'conversations' means structurally. The description adds minimal value beyond the basic action.
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 states exactly what the tool does with zero wasted words. It's appropriately sized for a simple retrieval tool and front-loads the core purpose immediately. Every word earns its place.
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 3 parameters, the description is incomplete. It doesn't explain what 'conversations' means in the return value, how pagination works, or any behavioral constraints. For a tool that retrieves conversations with pagination and field selection capabilities, more context is needed 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 with their types, constraints, and descriptions. The description adds no additional parameter semantics beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no parameter info in the description.
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 ('Retrieve') and resource ('direct message conversations'), making the purpose immediately understandable. It distinguishes this from other messaging tools like 'sendDirectMessage' or 'getDirectMessageEvents' by focusing on conversations rather than individual events or sending. However, it doesn't explicitly differentiate from all siblings beyond the obvious messaging category.
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 'getDirectMessageEvents' or 'sendDirectMessage'. There's no mention of prerequisites, context, or exclusions. The agent must infer usage from the tool name alone, which is insufficient given multiple messaging-related tools in the sibling list.
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 tool fetches a list but omits critical details: whether it's paginated, rate-limited, requires authentication, returns partial data on errors, or has side effects. For a read operation with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves in practice.
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, front-loaded sentence that directly states the tool's purpose without unnecessary words. It avoids redundancy with the tool name ('getFollowing') and efficiently communicates the core functionality. Every part of the sentence earns its place, 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 lack of annotations and output schema, the description is incomplete for a tool with three parameters and no structured output documentation. It doesn't describe the return format (e.g., list structure, field mappings), error conditions, or authentication requirements. For a read operation in a social media context, this leaves the agent with insufficient information to use the tool effectively without trial and error.
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 three parameters (username, maxResults, userFields). The description adds no additional parameter semantics beyond what the schema provides—it doesn't explain parameter interactions, default behaviors, or usage examples. With high schema coverage, the baseline score of 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 ('Get a list') and resource ('users that a user is following'), making the purpose immediately understandable. It distinguishes itself from sibling tools like 'getFollowers' (which fetches followers rather than following) by specifying the direction of the relationship. However, it doesn't explicitly mention the platform context (e.g., Twitter/X), which could slightly limit clarity for agents unfamiliar with the domain.
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 needs), compare to similar tools like 'getFollowers' or 'findMutualConnections', or specify use cases (e.g., social network analysis). Without such context, an agent must infer usage from the tool name and parameters 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 mentions 'reconstruct' but doesn't clarify if this is a read-only operation, what permissions are needed, rate limits, or how the thread is structured in output. 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 any wasted words. It's front-loaded and appropriately sized for the task, 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 reconstructing a Twitter thread, no annotations, and no output schema, the description is insufficient. It doesn't explain the return format, how threads are ordered, or any limitations (e.g., depth, deleted tweets). For a tool with rich potential output and no structured guidance, more detail 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?
Schema description coverage is 100%, so the schema already documents both parameters ('tweetId' and 'includeMetrics'). The description doesn't add any meaning beyond the schema, such as explaining what 'complete Twitter thread' entails or how 'includeMetrics' affects the reconstruction. Baseline 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 ('reconstruct') and resource ('complete Twitter thread with all tweets and replies'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'getConversationTree' or 'getThreadMetrics', which might have overlapping functionality, so it falls short of 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 such as 'getConversationTree' or 'getThreadMetrics'. It lacks context about prerequisites, exclusions, or specific scenarios where this tool is preferred, leaving the agent with minimal usage direction.
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 'analyze' suggests a read-only operation, the description doesn't clarify whether this requires authentication, has rate limits, returns paginated results, or what format the analysis output takes. Significant behavioral details are missing.
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 gets straight to the point with zero wasted words. It's appropriately sized for a tool with three parameters and front-loads the essential information.
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. It doesn't explain what 'analyze' returns (metrics, charts, time series data?), doesn't address authentication requirements or rate limits, and doesn't differentiate from similar sibling tools. The agent would have significant gaps in understanding how to properly use this tool.
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 all three parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain how the parameters interact or what 'analyze' specifically entails. Baseline 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 tool's purpose with a specific verb ('analyze') and resource ('hashtag trends and performance over time'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'getHashtagAnalytics' or 'trendingTopicsSearch', which appear to have related functionality.
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. With sibling tools like 'getHashtagAnalytics' and 'trendingTopicsSearch' available, there's no indication of how this tool differs or when it should be preferred over those options.
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 tool fetches liked tweets but omits critical details: whether it requires authentication, if it's read-only or has side effects, rate limits, pagination behavior, or error handling. For a tool with no annotation coverage, this is a significant gap in transparency.
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 front-loaded with the core purpose and efficiently communicates the tool's function without unnecessary elaboration, 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 complexity of a tweet retrieval tool with no annotations, no output schema, and multiple parameters, the description is incomplete. It lacks behavioral context (e.g., authentication needs, rate limits), output format details, and usage guidelines relative to siblings, leaving the agent under-informed for proper tool selection and invocation.
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 all three parameters (userId, maxResults, tweetFields) with descriptions and constraints. The description adds no additional parameter semantics beyond implying a user context, which is already covered by the schema. This meets the baseline for high schema coverage.
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 a list') and resource ('tweets liked by a user'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential siblings like 'getUserTimeline' or 'searchTweets' that might also retrieve tweets, leaving room for ambiguity in a crowded toolset.
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. With many sibling tools for tweet retrieval (e.g., 'getUserTimeline', 'searchTweets', 'getTweetById'), there's no indication of context, prerequisites, or exclusions, leaving the agent to guess based on 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 full burden but only states the basic action without behavioral details. It doesn't disclose rate limits, authentication requirements, pagination behavior, error conditions, or what the response contains (e.g., user objects with fields). 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, efficient sentence with zero wasted words. It's front-loaded with the core purpose and appropriately sized for a straightforward retrieval tool, 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?
For a tool with 3 parameters, no annotations, and no output schema, the description is insufficient. It lacks behavioral context (rate limits, auth), output format details, and differentiation from siblings. Given the complexity and missing structured data, it should provide more completeness to guide the agent 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 fully documents all three parameters. The description adds no additional meaning about parameters beyond what's in the schema (e.g., format of listId, default behavior of maxResults). Baseline 3 is appropriate when 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 'members of a Twitter list', making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'getFollowers' or 'getFollowing' that also retrieve user collections, missing explicit 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?
No guidance is provided about when to use this tool versus alternatives like 'getFollowers' or 'getFollowing'. The description lacks context about prerequisites (e.g., needing list ownership/access) or exclusions, offering minimal usage direction.
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 what the tool does but doesn't mention rate limits, authentication requirements, pagination behavior, error conditions, or what the response format looks like. For a data retrieval 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, clear sentence with zero wasted words. It's front-loaded with the core purpose and doesn't include any unnecessary information, making it highly 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?
For a data retrieval tool with 3 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the response contains (e.g., retweet objects, user details), how results are structured, or any behavioral constraints. With rich sibling tools available, more context is needed for proper tool selection and usage.
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 (tweetId, maxResults, userFields). The description doesn't add any additional meaning about parameters beyond what's in the schema, such as explaining why userFields might be useful or providing examples. 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 clearly states the verb ('Get') and resource ('a list of retweets of a tweet'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential sibling tools like 'getConversation' or 'getFullThread' that might also retrieve tweet-related data, preventing 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. With many sibling tools available (e.g., 'getTweetById', 'getConversation', 'getLikedTweets'), there's no indication of when retweets are the appropriate data to fetch versus other tweet-related 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 the full burden of behavioral disclosure. It mentions 'performance and engagement distribution' but lacks details on what metrics are returned, rate limits, authentication needs, or data freshness. For an analysis tool with no annotations, 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 waste. It's front-loaded and appropriately sized for its purpose, making it easy to parse 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 no annotations, no output schema, and a 3-parameter tool for analysis, the description is incomplete. It doesn't explain return values, error conditions, or behavioral traits, 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%, so the schema fully documents all parameters (tweetId, analyzeEngagement, timeframe). The description adds no additional meaning beyond the schema, such as explaining what 'engagement distribution' entails or how the timeframe affects analysis. Baseline 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 tool's purpose as 'Analyze thread performance and engagement distribution,' which specifies the verb ('analyze') and resource ('thread performance and engagement distribution'). It distinguishes from siblings like getConversation or getFullThread by focusing on metrics/analysis rather than retrieval, though it doesn't explicitly name 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 prerequisites, context, or exclusions, such as how it differs from other analysis tools like analyzeSentiment or trackVirality. The agent must infer usage from the name and parameters 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. It states 'Get a tweet by its ID', implying a read-only operation, but doesn't disclose behavioral traits like rate limits, authentication needs, error handling (e.g., for invalid IDs), or response format. For a tool with zero annotation coverage, this is a significant gap in transparency.
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: 'Get a tweet by its ID'. It's front-loaded with the core action and resource, with zero wasted words. This is appropriately sized for a straightforward tool, 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 no annotations, no output schema, and a simple input schema, the description is incomplete. It doesn't cover behavioral aspects like rate limits or auth, response format, or usage context. For a tool in a complex server with many siblings, more guidance is needed to ensure correct invocation, making it inadequate overall.
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 descriptions for tweetId and tweetFields. The description adds no parameter semantics beyond what the schema provides, such as examples of tweetFields values or ID format. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.
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 'Get a tweet by its ID' clearly states the action (get) and resource (tweet), specifying it's by ID. It distinguishes from siblings like getTweetsByIds (plural) and getConversation (by context), but doesn't explicitly contrast them. The purpose is specific and unambiguous, though it could be more precise about differentiation.
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 getTweetsByIds (for multiple IDs), getConversation (for thread context), or searchTweets (for broader queries). It lacks context about prerequisites, such as needing a valid tweet ID, or exclusions, leaving the agent to 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. It states the core action but omits critical details: it doesn't mention rate limits, authentication requirements, error handling (e.g., for invalid IDs), or the response format (e.g., JSON structure, pagination). For a tool that fetches data, this leaves significant gaps in understanding its operational 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 front-loads the core purpose without any wasted words. It's appropriately sized for a straightforward tool, making it easy to parse and understand at a glance.
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. It doesn't address behavioral aspects like rate limits or auth needs, nor does it hint at the return structure. For a tool with two parameters and no structured safety hints, more context is needed to ensure reliable agent 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?
Schema description coverage is 100%, with both parameters ('tweetIds' and 'tweetFields') well-documented in the schema itself. The description adds no additional semantic context beyond what's in the schema (e.g., it doesn't explain ID formats or field selection implications), so it meets the baseline for high schema coverage without compensating value.
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 ('multiple tweets by their IDs'), making the purpose immediately understandable. It distinguishes itself from sibling tools like 'getTweetById' (singular) and 'searchTweets' (query-based), though it doesn't explicitly mention these distinctions in the description 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 like 'getTweetById' (for single tweets) or 'searchTweets' (for query-based retrieval). There's no mention of prerequisites, limitations, or contextual usage scenarios beyond the basic 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states what the tool does ('Get information about a Twitter user') without adding context such as rate limits, authentication requirements, error handling, or what specific information is returned (e.g., profile details, follower counts). This leaves significant gaps in understanding 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, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, 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 complexity of a user info tool with no annotations and no output schema, the description is incomplete. It doesn't explain what information is returned (e.g., user profile, statistics), potential limitations, or how it fits among sibling tools. This leaves the agent with insufficient context to use the tool 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?
The input schema has 100% description coverage, with the 'username' parameter clearly documented. The description adds no additional meaning beyond the schema, as it doesn't explain parameter usage, constraints, or examples. This meets the baseline score of 3 since the schema adequately covers parameter semantics.
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 ('information about a Twitter user'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'getAuthenticatedUser' or 'bulkUserProfiles', which also retrieve user information but with different scopes or parameters.
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 (e.g., for a single user by username) or when to use siblings like 'getAuthenticatedUser' (for the logged-in user) or 'bulkUserProfiles' (for multiple users), leaving the agent to infer usage from context 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. It mentions fetching lists but doesn't cover aspects like rate limits, authentication requirements, pagination behavior, or error handling. This leaves significant gaps for a tool that likely involves API calls.
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 no wasted words, making it easy to parse and front-loaded with the core purpose. It efficiently conveys the essential information 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 the lack of annotations and output schema, the description is insufficient for a tool with 3 parameters and likely complex behavior (e.g., API interactions). It doesn't address return values, error cases, or operational constraints, leaving the agent with incomplete 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 input schema already documents all parameters thoroughly. The description adds no additional meaning beyond implying a user context, which is redundant with the schema. This meets the baseline for high schema coverage.
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 ('lists owned by a user'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'getListMembers' or 'createList', which also involve lists, so it lacks explicit sibling 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?
No guidance is provided on when to use this tool versus alternatives such as 'getListMembers' or 'getUserInfo', nor does it mention prerequisites or context for usage. The description only states what it does without indicating appropriate scenarios.
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. It states what the tool does but doesn't mention rate limits, authentication needs, pagination, or error handling. For a read operation with potential API constraints, this leaves significant gaps in understanding how the tool behaves beyond basic functionality.
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 with no wasted words. It's front-loaded and appropriately sized for a simple retrieval tool, 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 no annotations, no output schema, and a read operation with potential complexities (e.g., rate limits, data format), the description is incomplete. It doesn't address what the tool returns, error cases, or behavioral constraints, which are crucial for an agent to use it effectively in a real-world 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 input schema already documents all parameters thoroughly. The description adds no additional meaning about parameters beyond implying 'username' is required, which is covered in the schema. This meets the baseline for high schema coverage without extra value.
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 ('tweets from a specific user's timeline'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'getTweetsByIds' or 'getLikedTweets', which also retrieve tweets but from different sources, so it misses explicit sibling 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 such as 'getTweetsByIds' for specific tweets or 'searchTweets' for broader queries. It lacks context about prerequisites or exclusions, leaving the agent to 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Like a tweet' implies a write/mutation operation, the description doesn't specify authentication requirements, rate limits, idempotency, error conditions, or what happens if the tweet is already liked. 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 a single, efficient sentence that states the core functionality without unnecessary words. It's front-loaded with the essential information ('Like a tweet'), making it immediately scannable and perfectly concise for this simple operation.
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 explain what the tool returns (success/failure, updated tweet object, etc.), error handling, or behavioral constraints. Given the complexity of social media APIs and the lack of structured data, more context 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?
Schema description coverage is 100%, with the single parameter 'tweetId' clearly documented in the schema. The description adds no additional parameter information beyond what's in the schema (e.g., format examples, constraints). With high schema coverage, the baseline score of 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 ('Like') and target resource ('a tweet by its ID'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling 'unlikeTweet', but the verb 'Like' is specific enough to distinguish it from most other tweet-interaction tools like 'retweet' or 'replyToTweet'.
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, tweet visibility), when not to use it, or how it differs from similar actions like 'retweet'. With many 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions mapping and analyzing but doesn't specify whether this is a read-only operation, what permissions are required, how results are returned, or any rate limits. This is inadequate for a tool that likely involves data analysis.
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 front-loads the core purpose without unnecessary words. Every part 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 the complexity of mapping influence networks, no annotations, and no output schema, the description is incomplete. It doesn't explain what the output looks like, how to interpret results, or any behavioral traits, leaving significant gaps for an AI agent.
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 all parameters thoroughly. The description adds no additional meaning beyond what the schema provides, such as explaining how 'depth' affects analysis or what 'connectionTypes' imply. Baseline 3 is appropriate when 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 tool's purpose with a specific verb ('Map') and resource ('user influence network and connection patterns'), making it distinct from most sibling tools that focus on tweets, users, or lists. However, it doesn't explicitly differentiate from 'findMutualConnections' or 'userInfluenceMetrics', which could be related.
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 'findMutualConnections' or 'userInfluenceMetrics', nor does it mention prerequisites or exclusions. Usage is implied by the purpose but lacks explicit context.
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. It states the action ('Post a tweet') but doesn't mention that this is a write operation requiring authentication, potential rate limits, or that it might have side effects like publishing publicly. This leaves significant gaps in understanding the tool's behavior beyond the basic action.
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 a single sentence that directly states the tool's purpose. It's front-loaded with no wasted words, making it easy to parse quickly. Every word earns its place by conveying essential information 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 of a social media posting tool with no annotations and no output schema, the description is incomplete. It doesn't cover authentication needs, rate limits, error conditions, or what the return value might be (e.g., tweet ID or success status). For a write operation in this context, more behavioral and contextual information 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 schema description coverage is 100% with one parameter ('text') fully documented in the schema. The description adds no additional meaning about the parameter beyond what's in the schema (e.g., character limits, formatting rules, or emoji handling). This meets the baseline for high schema coverage but 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 ('Post') and resource ('a tweet to Twitter'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'postTweetWithMedia' or 'replyToTweet', which would require mentioning it's for basic text-only tweets without media or specific threading context.
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 that 'postTweetWithMedia' should be used for tweets with images/videos, 'replyToTweet' for replying to existing tweets, or any other context like character limits or prerequisites, leaving the agent to infer usage from tool names 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 full burden for behavioral disclosure. While 'Post a tweet' implies a write/mutation operation, the description doesn't mention authentication requirements, rate limits, character limits for text, media size restrictions, or what happens on success/failure. For a mutation tool with zero annotation coverage, this is a significant gap in behavioral transparency.
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's appropriately sized for a straightforward tool and front-loads the core functionality immediately. Every word earns its place in this concise formulation.
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 (e.g., returns tweet ID, success status), doesn't mention authentication requirements, and doesn't address constraints like media size limits or text character limits. Given the complexity of posting media to Twitter, this leaves significant gaps for an AI agent.
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 all four parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (text, mediaPath, mediaType, altText). Baseline score of 3 is appropriate when the schema does the heavy lifting for parameter documentation.
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 ('Post a tweet with media attachment') and target resource ('to Twitter'), providing specific verb+resource combination. However, it doesn't distinguish this tool from its sibling 'postTweet' (which presumably posts tweets without media), missing explicit differentiation that would earn 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 like 'postTweet' (for text-only tweets) or 'createMediaMessage' (for media in direct messages). There's no mention of prerequisites, constraints, or appropriate contexts for this specific media-posting 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?
With no annotations provided, the description carries full burden but only states the basic action. It doesn't disclose behavioral traits such as permissions required (e.g., must own the list), rate limits, whether removal is reversible, or error conditions (e.g., if user isn't in list). This is inadequate for a mutation tool with zero annotation coverage.
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 no wasted words. It's front-loaded with the core action and resource, 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?
For a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral context (e.g., effects, errors), usage guidelines, and output expectations, leaving significant gaps for the agent to operate 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 fully documents both parameters ('listId' and 'username'). The description adds no additional meaning beyond implying these parameters are used for removal, meeting the baseline score without compensating for gaps.
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 ('Remove') and target ('a user from a Twitter list'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'getListMembers' or 'unfollowUser' which might involve similar user-list operations, 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. It doesn't mention prerequisites (e.g., needing list ownership), exclusions, or relationships with siblings like 'addUserToList' or 'getListMembers', leaving the agent to infer usage context.
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. 'Reply to a tweet' implies a write/mutation operation but doesn't disclose any behavioral traits: no mention of authentication requirements, rate limits, whether replies are public/private, character limits, or what happens when the tweet doesn't exist. This leaves significant gaps 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 maximally concise with just three words that directly convey the core functionality. There's zero wasted language, and it's perfectly front-loaded with the essential information.
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 inadequate. It doesn't explain what the tool returns, error conditions, authentication requirements, or behavioral constraints. The 100% schema coverage helps with parameters, but the overall context for using this tool remains incomplete.
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 both parameters (tweetId and text). The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score for high schema coverage.
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 ('reply to') and resource ('a tweet'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'postTweet' or 'sendDirectMessage' which also involve creating content, missing an opportunity for clearer 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. There's no mention of prerequisites (like authentication), when not to use it, or how it differs from similar tools like 'postTweet' (which creates original tweets) or 'sendDirectMessage' (for private messages).
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. While 'retweet' implies a write/mutation operation, the description doesn't specify authentication requirements, rate limits, whether it's idempotent, what happens on success/failure, or any side effects. This is inadequate for a mutation tool with zero annotation coverage.
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's appropriately sized for a simple tool with one parameter and gets straight to the point 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?
For a mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what happens after retweeting (success response, error conditions), authentication requirements, or how this differs from related operations. The agent would need to guess about important behavioral aspects.
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 mentions 'by its ID' which aligns with the single parameter 'tweetId' in the schema. Since schema description coverage is 100% (the parameter already has a clear description), the description adds minimal value beyond what's in the structured schema, meeting the baseline for high coverage.
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 ('retweet') and target resource ('a tweet by its ID'), providing specific verb+resource combination. However, it doesn't explicitly distinguish this from sibling tools like 'undoRetweet' or 'getRetweets', which would require mentioning it's a creation/action tool rather than a retrieval or undo operation.
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 about when to use this tool versus alternatives. The description doesn't mention prerequisites (like authentication requirements), when not to use it, or how it differs from similar tools like 'undoRetweet' or 'getRetweets' in the sibling list.
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 states the tool sends a message but lacks critical behavioral details: whether this requires specific permissions, if it's rate-limited, what happens on failure, or if messages are reversible. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
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 directly states the tool's purpose without redundancy. It's appropriately sized and front-loaded, 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?
For a mutation tool with no annotations and no output schema, the description is incomplete. It lacks behavioral context (e.g., side effects, error handling), usage guidelines, and details on return values. While the schema covers parameters well, the overall context for safe and effective tool invocation is insufficient.
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 all parameters well-documented in the schema itself. The description adds no additional parameter semantics beyond implying 'recipientId' and 'text' are required (matching the schema). Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't compensate with extra context like format examples or constraints.
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 ('send a direct message') and target ('to a specified user'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'createMediaMessage' or 'postTweet' that might also involve messaging, leaving room for ambiguity in tool selection.
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. The description doesn't mention prerequisites (e.g., authentication), exclusions, or compare it to similar tools like 'createMediaMessage' or 'postTweet', leaving the agent to infer usage context 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 full burden for behavioral disclosure. 'Track viral spread patterns and engagement velocity' implies a read-only analytics operation, but it doesn't specify whether this requires special permissions, has rate limits, returns real-time vs. historical data, or what the output format looks like. For a tool with no annotation coverage, this leaves significant behavioral gaps.
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 one sentence containing no wasted words. It's front-loaded with the core purpose and uses efficient phrasing. Every word earns its place in communicating the tool's function.
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 multiple sibling tools with potential overlap, the description is incomplete. It doesn't address when to use this versus similar analytics tools, doesn't describe output format or behavioral constraints, and leaves the agent guessing about the tool's full context. For a 3-parameter analytics tool with rich sibling context, this is inadequate.
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 all three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain how 'trackingPeriod' values affect analysis, what 'analyzeSpread' entails, or provide examples. Baseline 3 is appropriate when 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 tool's purpose with specific verbs ('track viral spread patterns' and 'engagement velocity') and identifies the resource (tweets via tweetId parameter). It distinguishes from siblings like 'getTweetById' or 'getThreadMetrics' by focusing on virality analysis rather than retrieval. However, it doesn't explicitly differentiate from 'mapInfluenceNetwork' or 'userInfluenceMetrics' which might have overlapping analytics 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. With siblings like 'getThreadMetrics', 'userInfluenceMetrics', and 'mapInfluenceNetwork' that might offer related analytics, there's no indication of when this specific virality tracking tool is preferred. No prerequisites, exclusions, or comparative context are mentioned.
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 'Get' implies a read-only operation, it doesn't specify whether this requires authentication, rate limits, data freshness, or what the return format looks like (e.g., list of topics with metrics). For a tool with zero annotation coverage, this is a significant gap.
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 front-loads the core purpose without unnecessary words. Every part of the sentence earns its place by specifying the action, resource, and purpose ('for analysis').
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 tool's complexity (3 parameters, no output schema, no annotations), the description is incomplete. It lacks details on behavioral traits (e.g., authentication needs, rate limits), output format, and differentiation from siblings. For a tool that likely returns structured data, more context is needed to guide 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?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly with defaults, constraints, and enums. The description adds no additional meaning beyond what the schema provides, such as explaining how 'location' affects results or what 'popular content' entails. Baseline 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 tool's purpose with a specific verb ('Get') and resource ('trending topics and popular content'), and distinguishes it from siblings by focusing on trending analysis rather than user actions or tweet retrieval. However, it doesn't explicitly differentiate from similar tools like 'getHashtagTrends' or 'trackVirality'.
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 'getHashtagTrends' or 'searchTweets', nor does it mention any prerequisites or exclusions. 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 implies a mutation ('Unblock') but doesn't disclose behavioral traits such as required permissions, whether the action is reversible, rate limits, or what happens if the user isn't blocked. This is inadequate for a mutation tool with zero annotation coverage.
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, front-loaded with the core action. It's appropriately sized for the tool's purpose, 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 this is a mutation tool with no annotations, no output schema, and 2 parameters, the description is incomplete. It lacks behavioral details (e.g., effects, error cases), usage context, and doesn't compensate for the absence of structured data, leaving gaps for an AI agent.
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 both parameters (userId and username). The description adds no meaning beyond what the schema provides, not explaining parameter relationships (e.g., use one or both) or additional context. Baseline 3 is appropriate as the schema handles parameter documentation.
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 ('Unblock') and target resource ('a previously blocked user account'), providing specific verb+resource. However, it doesn't differentiate from sibling tools like 'blockUser' or 'unmuteUser' beyond the obvious name difference, missing explicit 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 like 'unmuteUser' or 'blockUser', nor does it mention prerequisites (e.g., user must be blocked first) or context for selection among sibling tools. It states the action but lacks usage context.
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. While 'unfollow' implies a mutation operation, the description doesn't specify whether this requires authentication, what permissions are needed, whether the action is reversible, what happens to existing relationships, or what the response looks like. For a mutation tool with zero annotation coverage, this is insufficient.
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 gets straight to the point with zero wasted words. It's appropriately sized for a simple single-parameter tool and front-loads the essential information.
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 unfollowing, whether there are side effects, what the return value might be, or error conditions. Given the complexity of user relationship management and the lack of structured data, the description should provide more context about the operation's behavior and outcomes.
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 the single 'username' parameter. The description adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.
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 ('unfollow') and target resource ('a user by their username'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'unfollowUser' vs 'blockUser' or 'muteUser' - all could involve ceasing some form of user interaction, so the distinction isn't explicit.
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 about when to use this tool versus alternatives like 'blockUser', 'muteUser', or 'removeUserFromList'. There's no mention of prerequisites (e.g., must be following the user first), consequences, or typical use cases for unfollowing versus other user management actions.
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 mentions the action ('Unlike') which implies a mutation, but doesn't specify permissions required, rate limits, whether the operation is reversible, or what happens on success/failure. For a mutation tool with zero annotation coverage, this is insufficient.
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's front-loaded with the core action and immediately communicates the tool's purpose 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?
For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't address behavioral aspects like error conditions, authentication requirements, or what the tool returns. The 100% schema coverage helps with parameters, but other critical context is missing.
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 the single parameter 'tweetId'. The description doesn't add any additional semantic context about the parameter beyond what's in the schema (e.g., format examples, constraints). Baseline 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 action ('Unlike') and target resource ('a previously liked tweet'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'likeTweet' or 'undoRetweet', but the action is specific enough to imply 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 like 'undoRetweet' (which might serve a similar function) or prerequisites (e.g., the tweet must be currently liked). It simply states what the tool does without context about appropriate usage scenarios.
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. It states the action is to 'unmute,' implying a mutation that reverses a mute, but doesn't cover permissions needed, rate limits, error conditions, or what happens if the user isn't muted. This leaves significant gaps 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 that front-loads the core action without unnecessary words. Every part earns its place by clearly stating the tool's purpose, 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 this is a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., side effects, error handling) and doesn't explain return values, leaving the agent with insufficient context for reliable 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?
Schema description coverage is 100%, so the input schema fully documents the parameters (userId and username). The description adds no additional meaning beyond implying these identify the user, which the schema already covers. This meets the baseline of 3 for high schema coverage.
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 ('unmute') and target ('a previously muted user account'), which is specific and unambiguous. However, it doesn't explicitly differentiate from its sibling 'muteUser' beyond the opposite action, missing a direct comparison that would elevate it to a 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 like 'unblockUser' or other user management tools, nor does it mention prerequisites (e.g., the user must be muted first). It only implies usage through the phrase 'previously muted,' but lacks explicit context or exclusions.
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 mentions 'calculate' but doesn't specify whether this is a read-only operation, requires authentication, has rate limits, or what the output format might be. 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 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 complexity of calculating influence metrics, no annotations, and no output schema, the description is incomplete. It doesn't explain what the output looks like, potential side effects, or error conditions. For a tool with no structured behavioral data, more context is needed 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?
Schema description coverage is 100%, so the input schema already documents all parameters thoroughly. The description doesn't add any additional meaning beyond what's in the schema, such as explaining how 'analyzeEngagement' and 'analyzeReach' interact or providing usage examples. Baseline 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 tool's purpose with specific verbs ('calculate') and resources ('user influence scores and engagement metrics'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'mapInfluenceNetwork' or 'userGrowthAnalytics', which might have overlapping functionality in analyzing user metrics.
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. With siblings like 'mapInfluenceNetwork' and 'userGrowthAnalytics' that might analyze similar user data, there's no indication of context, prerequisites, or exclusions to help an agent choose appropriately.
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 'Send' implies a write operation, it doesn't specify whether this requires specific permissions, whether media attachments are limited in size or type beyond the enum, what happens if the recipient doesn't exist, or what the response looks like. For a mutation tool with zero annotation coverage, this leaves significant behavioral gaps.
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 gets straight to the point with zero wasted words. It's appropriately sized for a tool with good schema documentation and follows the principle of front-loading the most important information.
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 media-sending tool with 5 parameters and no output schema, the description is minimally adequate but has clear gaps. The schema handles parameter documentation well, but without annotations or output schema, the description should ideally cover more behavioral aspects like error conditions, response format, or usage constraints. It's complete enough to understand what the tool does but not 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?
The schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema - it doesn't explain relationships between parameters (e.g., that mediaId must correspond to previously uploaded media) or provide usage examples. With complete schema coverage, the baseline 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 clearly states the action ('Send a direct message') and resource ('with media attachments'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'sendDirectMessage' (which presumably sends text-only messages) or 'postTweetWithMedia' (which posts to a public timeline rather than sending direct messages).
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 about when to use this tool versus alternatives. It doesn't mention when to choose this over 'sendDirectMessage' (for text-only messages) or 'postTweetWithMedia' (for public posts with media), nor does it specify prerequisites like needing to upload media first or mention any usage constraints.
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 mentions 'beyond standard API limitations' but doesn't explain what this entails—whether it involves higher rate limits, access to older data, different authentication requirements, or potential costs. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its operational behavior and constraints.
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 core function without unnecessary words. It's appropriately sized and front-loaded, with every part of the sentence contributing to understanding the tool's purpose.
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 complexity (search tool with 3 parameters, no output schema, and no annotations), the description is minimally adequate but has clear gaps. It states the purpose but lacks usage guidelines, behavioral details, and output information. For a tool that likely involves significant API limitations or special access, more context would be helpful, though the schema covers parameters well.
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 all parameters (query, dateRange, maxResults) with descriptions and constraints. The description adds no additional parameter semantics beyond what's in the schema, such as query syntax examples or date format details. Baseline 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 tool's purpose as 'Search historical tweets beyond standard API limitations', which specifies the verb (search), resource (historical tweets), and key capability (beyond standard API limitations). However, it doesn't explicitly differentiate from sibling tools like 'searchTweets' or 'advancedTweetSearch', which likely handle different search scopes or capabilities.
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 mentions 'beyond standard API limitations' but doesn't specify what those limitations are or when this tool is preferable over sibling tools like 'searchTweets' or 'advancedTweetSearch'. No exclusions or prerequisites are mentioned.
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. It mentions pagination, which is useful, but fails to describe critical traits like authentication requirements, rate limits, error conditions, or the response format. For a read operation with zero annotation coverage, 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 a single, efficient sentence that is front-loaded with the core purpose. There is no wasted language, and it immediately conveys the essential information 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 the complexity of a paginated list tool with no annotations and no output schema, the description is incomplete. It lacks details on authentication, response structure, error handling, or usage context, which are crucial for an agent to invoke this tool correctly. The description does not compensate for the missing structured data.
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 all three parameters (maxResults, paginationToken, userFields). The description does not add any additional meaning beyond what the schema provides, such as explaining parameter interactions or usage examples. Baseline 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Retrieve'), resource ('paginated list of users you have blocked'), and scope ('you have blocked'), distinguishing it from sibling tools like getMutedUsers or getFollowers. It precisely communicates what the tool does without ambiguity.
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 getMutedUsers or getUserInfo, nor does it mention prerequisites such as authentication or context for blocked users. It lacks explicit when/when-not instructions or named alternatives.
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. It mentions pagination but fails to describe response format, error handling, rate limits, or authentication requirements. For a read operation 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 that front-loads the core purpose without unnecessary details. 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 and no output schema, the description is incomplete for a tool with three parameters and pagination. It lacks details on return values, error cases, or operational constraints, leaving the agent with insufficient context for 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?
Schema description coverage is 100%, so the schema fully documents all three parameters. The description adds no additional parameter semantics beyond implying a list retrieval, which the schema already covers. This meets the baseline for high schema coverage.
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 specific action ('Retrieve'), resource ('list of users you have muted'), and scope ('paginated'), distinguishing it from siblings like getBlockedUsers or getUserInfo. It precisely communicates what the tool does without being tautological.
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 getBlockedUsers or getUserInfo, nor does it mention prerequisites such as authentication or context. It lacks explicit usage instructions or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool performs a mutation ('mark as read') and hints at an access requirement ('may require special API access'), which adds useful context. However, it doesn't describe what 'mark as read' entails operationally (e.g., whether it updates server state, affects notifications, or has side effects), leaving behavioral gaps.
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, efficient sentence that states the core purpose upfront. The parenthetical note about API access is relevant but could be integrated more smoothly. Overall, it's appropriately sized with minimal waste.
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 mutation tool with no annotations and no output schema, the description is adequate but incomplete. It covers the basic action and a potential access constraint, but lacks details on behavior, error conditions, or return values, which are important given the tool's mutative nature and context among many 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?
Schema description coverage is 100%, so the schema fully documents both parameters. The description adds no parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 where the schema handles the documentation burden.
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 ('mark as read') and resource ('direct messages'), making the purpose unambiguous. However, it doesn't differentiate this tool from potential siblings like 'getDirectMessages' or 'getDirectMessageEvents' that might also handle message status, though those appear to be read-only operations.
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 mentions a special API access requirement, but doesn't specify conditions, prerequisites, or exclusions for usage relative to other messaging tools in the sibling list.
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. It mentions the outcome ('stop seeing their tweets') but lacks details on permissions needed, rate limits, reversibility (e.g., via 'unmuteUser'), or error conditions. For a mutation tool with zero annotation coverage, this is insufficient.
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 directly states the tool's purpose and outcome without unnecessary words or structural fluff.
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 tool's complexity (a mutation with no annotations, no output schema, and 2 parameters), the description is incomplete. It lacks behavioral details, usage context, and output information, making it inadequate for safe and effective use by an AI agent.
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 'username') with clear descriptions. The description adds no additional parameter semantics beyond what the schema provides, meeting the baseline for high schema coverage.
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 specific action ('Mute a user account') and the resource ('user account'), with the explicit outcome ('to stop seeing their tweets in your timeline'). It distinguishes from siblings like 'blockUser' by focusing on timeline filtering rather than blocking interactions.
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 'blockUser' or 'unmuteUser', nor any prerequisites or contextual constraints. The description only states what it does, not when or why to use it.
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. 'Undo a retweet' implies a mutation/deletion action, but it doesn't specify permissions required, whether it's reversible, rate limits, or what happens on success/failure. For a mutation tool with zero annotation coverage, this leaves critical behavioral traits unaddressed.
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 with zero wasted words. It's front-loaded with the core action and efficiently conveys the purpose without unnecessary elaboration, 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 the tool's complexity (a mutation operation), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like error conditions, side effects, or return values, leaving gaps that could hinder an AI agent's correct invocation.
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 parameter 'tweetId' documented as 'The ID of the tweet to un-retweet'. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without extra value.
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 'Undo a retweet by its ID' clearly states the action (undo/remove) and the resource (retweet), using a specific verb that distinguishes it from sibling tools like 'retweet', 'unlikeTweet', or 'deleteTweet'. It precisely communicates what the tool does without ambiguity.
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., must have retweeted the tweet first), exclusions, or compare it to similar tools like 'deleteTweet' (for original tweets) or 'unlikeTweet' (for likes). Usage is implied 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?
No annotations are provided, so the description carries the full burden. It mentions mapping 'complete conversation structure' but does not disclose behavioral traits such as rate limits, authentication needs, data format returned, or potential limitations (e.g., depth constraints implied by maxDepth). This leaves gaps for a tool with 3 parameters and no output 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 a single, efficient sentence with zero waste. It is front-loaded with the core purpose and includes key details ('replies and quotes') without unnecessary elaboration, making it easy to parse.
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 3 parameters, 100% schema coverage, and no output schema, the description is minimally adequate. It states what the tool does but lacks details on behavioral aspects (e.g., return format, error handling) and usage context, which are important for a mapping tool with no annotations or output schema.
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 all parameters (tweetId, maxDepth, includeQuotes). The description adds no additional meaning beyond the schema, such as explaining how 'maxDepth' affects mapping or what 'complete conversation structure' entails. 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 tool's purpose with a specific verb ('Map') and resource ('complete conversation structure'), including what it maps ('replies and quotes'). It distinguishes from sibling tools like 'getConversation' (likely simpler) and 'getFullThread' (different scope), though not explicitly named.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for mapping conversation structures, but does not explicitly state when to use this tool versus alternatives like 'getConversation' or 'getFullThread'. It provides some context (e.g., 'including replies and quotes') but lacks clear exclusions or prerequisites.
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. It mentions the effect ('prevent them from following you or viewing your tweets') but lacks details on permissions required, whether the action is reversible, rate limits, or what the response looks like. This is a significant gap 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 that front-loads the purpose ('Block a user account') and adds necessary context ('to prevent them from following you or viewing your tweets'). There is no wasted wording.
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 complexity (a mutation with no annotations and no output schema), the description is minimal but functional. It explains what the tool does but lacks details on behavioral aspects like error handling or return values, which are important for an agent to use it 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 description coverage is 100%, so the schema already documents both parameters ('userId' and 'username'). The description does not add any meaning beyond what the schema provides, such as clarifying if both parameters can be used together or which takes precedence.
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 action ('block') and resource ('user account'), specifying the purpose to 'prevent them from following you or viewing your tweets'. This distinguishes it from sibling tools like 'muteUser' (which hides content) and 'unblockUser' (which reverses the action).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you want to prevent a user from interacting with your account, but it does not explicitly state when to use this tool versus alternatives like 'muteUser' or 'unblockUser', nor does it mention prerequisites such as authentication or user identification.
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 the tool retrieves profile information but doesn't disclose behavioral aspects like authentication requirements, rate limits, error conditions, or what happens if userFields is omitted. For a tool with no annotation coverage, this is insufficient.
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 front-loads the core purpose. Every word contributes to understanding the tool's function without redundancy.
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 no annotations and no output schema, the description adequately covers the basic purpose but lacks details on behavior, return values, or error handling. For a simple read operation with one optional parameter, it's minimally viable but could be more complete.
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 the optional userFields parameter. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3.
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 verb ('Get') and resource ('authenticated user's own profile information'), specifying it's for the current user without needing identifiers. It distinguishes from sibling tools like getUserInfo which likely requires a username/ID parameter.
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 implies usage context ('without needing to specify username or ID'), suggesting this tool is for accessing the current user's profile. However, it doesn't explicitly state when to use alternatives like getUserInfo or other user-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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. It mentions 'bypassing API tier restrictions' which is valuable behavioral context about capability beyond standard search. However, it doesn't disclose other important behaviors like rate limits, authentication requirements, whether results are real-time or historical, or what format the results return.
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 perfectly concise - a single sentence that communicates the core functionality ('advanced tweet search with operators and filters') and unique value proposition ('bypassing API tier restrictions') with zero wasted words. It's front-loaded with the most important information.
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 search tool with 6 parameters, 100% schema coverage, but no output schema and no annotations, the description provides adequate but incomplete context. It explains the 'what' and hints at unique capabilities, but doesn't address result format, pagination, error conditions, or how it differs from other search tools in the sibling list.
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 schema description coverage is 100%, so the schema already fully documents all 6 parameters. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. This meets the baseline of 3 for high schema coverage, but doesn't provide additional semantic context about how parameters interact or advanced usage patterns.
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 purpose with specific verbs ('advanced tweet search') and resources ('tweets'), and explicitly distinguishes it from potential siblings by mentioning 'bypassing API tier restrictions' - a unique capability not implied by other search tools like 'searchTweets' or 'historicalTweetSearch'.
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 about when to use this tool ('advanced tweet search with operators and filters'), but doesn't explicitly state when NOT to use it or name specific alternatives. It implies this is for complex searches beyond basic functionality, but lacks explicit comparison to sibling tools like 'searchTweets'.
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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