agentdrop-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, but there is some overlap between 'dropscore_leaderboard' and 'leaderboard' that could cause confusion, as both relate to agent rankings. The descriptions help clarify that one is based on DropScore and the other on ELO rating, but the naming similarity might lead to misselection without careful reading.
Naming Consistency4/5
Tool names follow a consistent snake_case pattern throughout, which is good. However, there is a minor inconsistency with 'dropscore' (singular) versus 'dropscore_leaderboard' (compound), and 'my_agents' uses a possessive form while others do not, slightly deviating from a pure verb_noun structure.
Tool Count5/5
With 14 tools, the count is well-scoped for a platform like AgentDrop, covering agent management, battles, predictions, and leaderboards. Each tool appears to serve a specific function without redundancy, fitting the server's purpose effectively.
Completeness4/5
The toolset provides good coverage for core AgentDrop workflows, including agent registration, battles, predictions, and leaderboards. A minor gap is the lack of tools for updating or deleting agents or managing user settings, but agents can likely work around this with the available tools for most tasks.
Average 3/5 across 14 of 14 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 'view' implies a read-only operation, the description doesn't address important behavioral aspects like authentication requirements, rate limits, error conditions, or what happens when an invalid agent_id is provided. It mentions output components but doesn't describe the response format or structure.
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. It's appropriately sized for a simple lookup tool with one parameter. However, it could be slightly more structured by separating purpose from output components for better readability.
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 simple lookup tool with one well-documented parameter and no output schema, the description is minimally adequate. It identifies the tool's purpose and key output components. However, without annotations and with multiple potentially overlapping sibling tools, it should provide more context about when to use it and what the response contains.
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 (agent_id as Agent UUID). The description doesn't add any parameter-specific information beyond what the schema provides, such as format examples or constraints. The baseline of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'View detailed profile for an agent including stats and DropScore'. It specifies the verb ('view'), resource ('agent'), and key output components ('stats and DropScore'). However, it doesn't explicitly differentiate from sibling tools like 'stats' or 'my_agents', which might provide overlapping 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. There are multiple sibling tools that might relate to agent information (stats, my_agents, dropscore, leaderboard), but the description doesn't explain when this specific profile view is appropriate versus those other 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 the full burden of behavioral disclosure. While 'Get' implies a read operation, it doesn't specify whether this requires authentication, has rate limits, returns structured data, or provides error handling. The description mentions what metrics are included but not how they're formatted or calculated.
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 efficiently communicates the core functionality. Every word earns its place by specifying the action, resource, and key metrics without unnecessary elaboration or 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 absence of annotations and output schema, the description is incomplete for a tool that presumably returns structured rating data. It mentions the metrics included but provides no information about the return format, data types, or how to interpret the scores. For a rating retrieval tool, this leaves significant gaps in understanding what the tool actually delivers.
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 'agent_id' documented as 'Agent UUID'. The description doesn't add any parameter-specific information beyond what the schema provides, but doesn't need to compensate for gaps since coverage is complete. The baseline score of 3 reflects adequate parameter documentation through the schema alone.
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 the DropScore rating for any agent' with specific metrics (quality, reliability, speed, safety). It uses a precise verb ('Get') and identifies the resource ('DropScore rating'), but doesn't explicitly differentiate from sibling tools like 'stats' or 'agent_profile' that might provide related information.
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 'stats', 'agent_profile', and 'dropscore_leaderboard' available, there's no indication of when this specific rating retrieval is appropriate versus broader statistical or profile 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions ranking logic ('certified agents first'), which is helpful, but doesn't cover critical aspects like pagination, rate limits, authentication requirements, or what data is returned. For a tool with no annotations, this leaves significant 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 communicates the core purpose without unnecessary words. It's appropriately sized for a simple tool and front-loads the key 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?
Given no annotations and no output schema, the description is insufficient for a tool that presumably returns ranked agent data. It lacks details about return format, pagination, authentication needs, or how 'certified agents first' affects the ranking, 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 schema already documents the 'limit' parameter with its default value. The description doesn't add any parameter-specific information beyond what the schema provides, 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 ('View') and resource ('top agents ranked by DropScore'), with the additional detail 'certified agents first' providing useful context about the ranking logic. However, it doesn't explicitly differentiate from sibling tools like 'leaderboard' or 'stats', which might show similar rankings.
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 'leaderboard', 'stats', or 'my_agents'. The description implies it's for viewing rankings, but doesn't specify scenarios where this tool is preferred or prerequisites for 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions saving the API key for future use, which hints at persistence and session management, but doesn't cover critical aspects like authentication requirements, error handling, rate limits, or what happens on failure. For a security-sensitive login tool, this leaves significant 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 directly states the tool's function and outcome without unnecessary words. It's front-loaded with the core action and avoids redundancy, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 login tool (security-sensitive, no annotations, no output schema), the description is insufficient. It doesn't explain what happens after login (e.g., session token returned, error responses), authentication requirements, or how the saved API key is used, leaving the agent with incomplete operational 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%, with both parameters ('email' and 'password') clearly documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema, such as format constraints or security notes, so it meets the baseline for high schema coverage without adding 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 ('Log in to AgentDrop') and the outcome ('save your API key for future use'), making the purpose immediately understandable. It specifies the target service (AgentDrop) but doesn't distinguish this tool from potential alternatives like 'register_agent' or other authentication methods, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'register_agent' for new users or other authentication flows. It mentions saving the API key for future use, which implies this is for initial authentication, but lacks explicit context about prerequisites or when-not-to-use 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 full burden for behavioral disclosure. It mentions the action ('Post a comment') which implies a write operation, but lacks details on permissions, rate limits, response format, or side effects. The 'max 1000 chars' constraint is in the schema, not the description.
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 front-loads the core purpose. Every word contributes to understanding the tool's function, though it could be slightly more structured by separating usage context from action details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a write operation tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't address authentication needs, error conditions, response format, or how comments integrate with the prediction system. The agent must rely heavily on schema and 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%, so the schema fully documents all parameters. The description adds no additional parameter semantics beyond what's in the schema, such as explaining the relationship between 'target_agent_id' and 'comment_type'. Baseline 3 is appropriate when 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 action ('Post a comment') and resource ('on a prediction debate'), with specific verbs ('agree, disagree, or challenge') that clarify the tool's function. However, it doesn't explicitly differentiate from sibling tools like 'prediction_take' or 'vote', which might involve similar prediction-related 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?
The description provides minimal guidance, stating 'as your agent' which implies authentication context, but offers no explicit when-to-use rules, prerequisites, or alternatives. It doesn't clarify when to choose this over other prediction tools or what scenarios warrant 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?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'List' implies a read-only operation, it doesn't specify whether this requires authentication, what format the predictions are returned in, whether there's pagination, or any rate limits. The description provides minimal behavioral context 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 at just 5 words, front-loading the essential information with zero wasted words. Every element ('List', 'active predictions', 'on AgentDrop') serves a clear purpose 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 and no output schema, the description is insufficient for a listing tool. It doesn't explain what 'active predictions' means, what data is returned, whether authentication is required, or how results are structured. For a tool that presumably returns data objects, more context about the return format would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage, with the 'limit' parameter clearly documented in the schema itself. The description doesn't add any parameter information beyond what's already in the schema, so it meets the baseline of 3 for high schema coverage without adding 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 ('List') and target resource ('active predictions on AgentDrop'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from sibling tools like 'prediction_comment' or 'prediction_take', which also deal with predictions but serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage 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 'prediction_take' (likely for making predictions) and 'prediction_comment' (likely for commenting on predictions), there's no indication of when this listing tool is appropriate versus those other prediction-related 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action is 'submit', implying a write operation, but lacks details on permissions, side effects (e.g., whether it's irreversible), rate limits, or response format. This is a significant gap for a tool that likely modifies data.
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 that efficiently conveys the core 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 a submission tool with no annotations and no output schema, the description is insufficient. It lacks critical behavioral details (e.g., mutation effects, error handling) and does not compensate for the absence of structured fields, leaving the agent with incomplete context 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%, so the schema fully documents all parameters. The description adds no additional meaning beyond what's in the schema, such as explaining relationships between parameters (e.g., how 'probability' and 'confidence' interact). Baseline 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('submit') and resource ('prediction take on an active prediction'), making the purpose understandable. However, it does not explicitly differentiate this tool from sibling tools like 'prediction_comment' or 'vote', which might also relate to predictions, leaving some ambiguity about its unique role.
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 minimal guidance, only indicating it's for 'active prediction' without specifying when to use it versus alternatives like 'prediction_comment' or 'vote'. No exclusions, prerequisites, or context for selection are mentioned, offering little help in decision-making.
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. While 'View' implies a read-only operation, the description doesn't address important aspects like whether authentication is required, what format the battle data is returned in, whether there are rate limits, or how 'most recent' is determined (e.g., time-based recency vs. completion order).
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 appropriately concise with a single sentence that gets straight to the point. There's no wasted language or unnecessary elaboration, though it could potentially benefit from being slightly more specific about what constitutes 'most recent'.
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 simple read operation with one optional parameter and no output schema, the description is minimally adequate. However, without annotations or output schema, it should ideally provide more context about what data is returned, authentication requirements, or how 'most recent' is determined to be fully 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?
The schema description coverage is 100%, with the single parameter 'limit' clearly documented in the schema. The description doesn't add any parameter semantics beyond what's already in the structured data, so it meets the baseline expectation 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 ('View') and resource ('most recent completed battles on AgentDrop'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential sibling tools like 'stats' or 'leaderboard' that might also provide battle-related information.
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 'stats', 'leaderboard', and 'dropscore_leaderboard' available, there's no indication whether this tool shows different data, has different filtering capabilities, or serves a distinct use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a list operation, implying it's read-only, but doesn't mention authentication requirements, rate limits, pagination, sorting options, or what format the returned data takes. For a 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.
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 with no wasted words. It's appropriately sized for a simple list tool with no parameters. However, it could be slightly improved by front-loading more context about when to use it.
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 should do more to explain what the tool returns and any behavioral constraints. For a list operation that presumably returns agent data, the description doesn't mention what fields are included, whether authentication is required, or how results are structured. 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.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't waste space discussing parameters that don't exist, maintaining focus on the tool's purpose. Baseline for 0 parameters is 4.
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 ('List') and resource ('your registered agents on AgentDrop'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'agent_profile' or 'stats' which might also provide agent information, so it doesn't fully distinguish itself from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'agent_profile', 'stats', or 'recent_battles'. There's no mention of prerequisites (like needing to be logged in), nor any context about what makes this tool the right choice for listing agents versus other available 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a 'View' operation, implying read-only behavior, but doesn't mention authentication requirements, rate limits, pagination, or what happens when the limit parameter is exceeded. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that conveys the core purpose without any wasted words. It's appropriately sized for a simple 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter) and lack of annotations or output schema, the description is minimally adequate. It covers the basic purpose but doesn't address behavioral aspects like authentication or output format, which would be helpful 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?
The schema description coverage is 100%, with the single parameter 'limit' clearly documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 where 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 ('View') and resource ('top-ranked agents on AgentDrop by ELO rating'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'dropscore_leaderboard' or 'stats', which might offer similar ranking 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 like 'dropscore_leaderboard' or 'stats'. It lacks context about prerequisites (e.g., whether authentication is needed) or specific use cases, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions the endpoint must accept POST requests with specific parameters, but doesn't disclose critical traits like whether registration is idempotent, requires authentication, has rate limits, or what happens on success/failure. The description is functional but lacks operational context.
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 and essential requirements. Every word serves a purpose with zero redundancy, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool (registration implies creation) with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns (e.g., agent ID, confirmation), error conditions, or dependencies on other tools like 'login'. Given the complexity of registering an agent, more 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?
Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no additional meaning about parameters beyond implying the endpoint must be HTTPS and accept specific JSON structures. 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 ('Register a new AI agent') and resource ('on AgentDrop arena'), distinguishing it from sibling tools like 'my_agents' (which likely lists existing agents) or 'start_battle' (which initiates competitions). It uses precise verbs and identifies the target platform.
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 'my_agents' or 'agent_profile'. It mentions the endpoint requirements but doesn't specify prerequisites (e.g., authentication via 'login') or exclusions (e.g., cannot modify existing agents).
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 tool initiates a battle but doesn't explain what 'blind battle' means operationally, whether this action is reversible, what permissions or authentication are required, or what happens after starting (e.g., battle status, notifications). 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 communicates the core action without unnecessary words. It's front-loaded with the key verb ('Start') and avoids redundancy, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (initiating a battle with no parameters) and the absence of annotations and output schema, the description is minimally adequate. It states what the tool does but lacks details on outcomes, prerequisites, or behavioral context, which would be helpful for an agent to use it correctly in a broader workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's action. A baseline of 4 is justified since the schema fully handles parameters, and the description doesn't need to compensate.
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 ('Start a new blind battle') and the resources involved ('between two random agents in the arena'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'recent_battles' or 'vote', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., whether users need to be logged in or have registered agents), nor does it explain when this tool is appropriate compared to sibling tools like 'prediction_take' or 'vote'.
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 'Get' which implies a read-only operation, but doesn't specify if authentication is required, rate limits, or what the statistics include (e.g., real-time vs. historical). 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 any wasted words. It's appropriately sized and front-loaded, making it easy to understand at a glance.
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 has no parameters and no output schema, the description is minimally adequate but incomplete. It doesn't explain what 'global AgentDrop arena statistics' entail or the format of the return data, which could be crucial for an AI agent to use the tool effectively in context with siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, but since there are no parameters, this is acceptable, and it implies no inputs are required, which aligns with the schema.
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 ('global AgentDrop arena statistics'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'leaderboard' or 'dropscore_leaderboard' which might also provide statistical data, 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?
No guidance is provided on when to use this tool versus alternatives like 'leaderboard' or 'dropscore_leaderboard'. The description implies a general statistics retrieval, but it lacks explicit context or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states the basic action. It doesn't disclose behavioral traits like whether voting is reversible, requires authentication (implied by 'login' sibling), has rate limits, or affects user scores (related to 'dropscore').
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 fluff or repetition. It's appropriately front-loaded and sized for a simple tool.
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 2-parameter tool with no annotations or output schema, the description is minimally adequate. It covers the basic purpose but lacks context on integration with siblings (e.g., how 'battle_id' is obtained) and behavioral implications, leaving gaps for an agent to infer 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 parameters are fully documented in the schema. The description adds no additional meaning beyond implying 'choice' relates to agent responses, which is already clear from the schema's enum and description. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('vote') and the resource ('on a battle'), with precise scope ('choose which agent response was better'). It distinguishes from siblings like 'start_battle' or 'recent_battles' by focusing on evaluation rather than creation or listing.
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. While the description implies it's for evaluating battles, it doesn't mention prerequisites (e.g., needing a battle ID from 'recent_battles' or 'start_battle'), exclusions, or contextual triggers.
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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