Maid-MCP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Every tool has a clearly distinct purpose with no ambiguity. For example, 'list_animations' and 'list_poses' are clearly separate listing functions, while 'play_animation' and 'stop_animation' handle animation control distinctly. The descriptions make it easy to differentiate between movement, visibility, voice, and animation operations.
Naming Consistency5/5All tools follow a consistent verb_noun pattern with snake_case throughout. Examples include 'create_animation', 'hide_avatar', 'list_animations', and 'set_voice'. This predictable naming convention makes the tool set easy to navigate and understand at a glance.
Tool Count5/5With 11 tools, this server is well-scoped for managing an avatar's animations, poses, voice, and visibility. Each tool earns its place by covering distinct aspects like listing resources, controlling playback, and configuring settings, without being overly sparse or bloated.
Completeness5/5The tool set provides complete coverage for the avatar management domain. It includes CRUD-like operations (create/list/play/stop animations), configuration (set voice, move/show/hide avatar), and core functionalities (speak, list poses/voices). There are no obvious gaps that would hinder agent workflows.
Average 3/5 across 11 of 11 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
- CI status not available
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Tools from this server were used 6 times in the last 30 days.
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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 the full burden of behavioral disclosure. It mentions 'create and save', implying a write operation, but lacks details on permissions, whether it overwrites existing animations, error handling, or response format. 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.
Conciseness5/5Is 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. It earns its place by clearly stating the tool's function in a concise manner.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a creation tool with no annotations and no output schema, the description is incomplete. It doesn't address behavioral aspects like what happens on duplicate IDs, success/failure responses, or system constraints, making it insufficient for safe and effective use by an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 parameters thoroughly. The description adds no additional meaning beyond what's in the schema, such as explaining the format of 'frames' or usage of 'id'. 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.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('create and save') and resource ('custom animation sequence'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'play_animation' or 'list_animations', which would require mentioning it's for creation rather than playback 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 Guidelines2/5Does 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. For example, it doesn't specify if this should be used instead of modifying existing animations or in what context (e.g., for new animations only), leaving the agent without 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?
No annotations are provided, so the description carries full burden for behavioral disclosure. It states the action ('move') but does not explain effects (e.g., whether movement is immediate, smooth, or bounded by screen limits), permissions needed, or error conditions. 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.
Conciseness5/5Is 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 action and resource, making it easy to parse. Every word earns its place without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's mutation nature, lack of annotations, and no output schema, the description is incomplete. It does not address behavioral aspects like side effects, success indicators, or error handling. For a 2-parameter tool with no structured support, more context is needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear documentation for 'x' and 'y' parameters as screen positions. The description adds no additional meaning beyond the schema (e.g., coordinate system origin, units, or valid ranges). Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('move') and target resource ('the avatar'), specifying the operation as positioning. It distinguishes from siblings like 'show_avatar' or 'play_animation' by focusing on spatial movement rather than visibility or animation. However, it lacks explicit differentiation from potential similar tools (e.g., 'set_position' if existed), keeping it at 4 instead of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 does not mention prerequisites (e.g., avatar must be visible), exclusions (e.g., cannot move while animating), or comparisons to siblings like 'create_animation' for motion effects. This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the action ('play') but lacks behavioral details: it doesn't specify if playback is immediate, blocking, or interruptible; whether it requires specific states (e.g., avatar visible); what happens on errors (e.g., invalid ID); or if it has side effects like audio. 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It front-loads the core purpose ('play an animation') and adds clarifying detail ('single pose or sequence') without 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.
Completeness2/5Given 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 1 parameter), the description is incomplete. It lacks behavioral context (e.g., playback behavior, error handling), usage prerequisites, and output expectations, leaving the agent with insufficient information to invoke it reliably beyond basic parameter passing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'id' documented as 'Animation ID (e.g., "idle", "happy", "treasure_hunt")'. The description adds no additional meaning beyond this, such as format constraints or how IDs map to poses/sequences. 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.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('play') and resource ('animation'), specifying it can handle both single poses and sequences. It distinguishes from siblings like 'stop_animation' (opposite action) and 'list_animations' (list vs. execute), but doesn't explicitly differentiate from 'create_animation' (creation vs. execution).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. It doesn't mention prerequisites (e.g., needing an animation to be loaded or an avatar visible), exclusions, or comparisons to siblings like 'list_animations' for discovery or 'stop_animation' for halting playback.
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 provides minimal behavioral context. 'Set' implies a mutation, but it doesn't disclose if this affects ongoing operations (e.g., interrupts current speech), requires specific permissions, has side effects, or how changes persist. This leaves significant gaps for an agent to understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is 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 and efficiently conveys the core 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.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's mutation nature and lack of annotations or output schema, the description is incomplete. It doesn't explain what 'current voice' means (e.g., for future 'speak' calls), success/failure conditions, or error handling, 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.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the schema documenting 'voiceId' as a string and noting 'ja-JP voices for Japanese accent'. The description adds no parameter details beyond what the schema provides, so it meets the baseline for high coverage without compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Set the current voice' clearly states the action (set) and resource (voice), making the purpose immediately understandable. It doesn't distinguish from siblings like 'list_voices' or 'speak', but the verb 'set' implies configuration rather than listing or execution.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. It doesn't mention prerequisites (e.g., whether a voice must be active), exclusions, or relationships to siblings like 'list_voices' (which might provide valid voice IDs) or 'speak' (which might use the set voice).
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 'optional emotion' and 'Japanese accent default', which gives some context about voice characteristics, but fails to disclose critical behavioral traits such as whether this is a read-only or mutating operation, what permissions are required, rate limits, output format (e.g., audio file, stream), or any side effects. This leaves significant gaps for an agent to understand how to invoke it correctly.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded with the core purpose in the first phrase. Every word earns its place: 'Convert text to speech' states the action, and 'with optional emotion (Japanese accent default)' adds necessary context without redundancy. It's a single, efficient sentence with no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (text-to-speech conversion with emotional modulation), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., audio data, success status), any prerequisites, or behavioral constraints. While concise, it fails to provide enough context for an agent to fully understand the tool's operation and outcomes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with both parameters well-documented in the schema ('text' and 'emotion' with enum values). The description adds minimal value beyond the schema by mentioning 'optional emotion' and 'Japanese accent default', which slightly clarifies the emotion parameter's context but doesn't provide additional semantic meaning. This meets the baseline of 3 when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Convert text to speech' with the specific action 'convert' and resource 'text to speech', which is distinct from sibling tools focused on animations, avatars, poses, and voices. However, it doesn't explicitly differentiate from potential similar text-to-speech tools that might exist elsewhere, keeping it at a 4 rather than 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 Guidelines2/5Does 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 'optional emotion (Japanese accent default)' but doesn't explain when to choose this over other voice or speech tools, nor does it reference any sibling tools for comparison. There's no explicit when/when-not 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 the full burden of behavioral disclosure. It states the action but doesn't explain what 'hide' entails (e.g., visual effects, persistence, permissions required, or error conditions). 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.
Conciseness5/5Is 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 and directly conveys the core action, 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.
Completeness2/5Given 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 insufficient. It lacks details on behavior, effects, or return values, leaving the agent with incomplete information for proper invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here, but a baseline of 4 is applied since no parameters exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('hide') and the target ('the visual avatar'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from its sibling 'show_avatar', which would be needed for 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 Guidelines2/5Does 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 'show_avatar' or other avatar-related tools. The description lacks context about prerequisites or typical scenarios for hiding an avatar.
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 ('show') but doesn't describe what happens: e.g., whether this creates a new avatar instance, makes an existing one visible, requires specific permissions, has side effects (like overriding other displays), or what the visual outcome looks like. 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.
Conciseness5/5Is 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 action and target. It's front-loaded and appropriately sized for a simple tool, with no redundant or verbose elements. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (visual display with positioning and animation), no annotations, and no output schema, the description is minimally adequate. It states the core function but lacks details on behavior, prerequisites, or visual outcomes. The schema covers parameters well, but the description doesn't fully compensate for the missing behavioral context, making it incomplete for optimal agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear defaults and meanings for 'animation', 'x', and 'y'. The description adds no parameter information beyond what the schema provides, so it doesn't enhance semantics. However, since the schema is comprehensive, the baseline score of 3 is appropriate as the description doesn't need to compensate for gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('show') and resource ('visual avatar on screen'), making the purpose understandable. It distinguishes from siblings like 'hide_avatar' (opposite action) and 'move_avatar' (different function), though it doesn't explicitly differentiate from tools like 'play_animation' that might also involve visual display. The description is specific but could be more precise about what 'show' entails versus other visual tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 an avatar must be loaded first), exclusions (e.g., not to use if avatar is already visible), or comparisons to siblings like 'play_animation' or 'move_avatar'. This lack of context leaves the agent to infer usage 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?
No annotations are provided, so the description carries the full burden. It states the tool stops animations, implying a mutation, but doesn't disclose behavioral traits like whether it requires specific permissions, what happens if no animation is running, or if it affects other avatar states. 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.
Conciseness5/5Is 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 a simple tool with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters, no annotations, and no output schema, the description is minimal. For a mutation tool that stops animations, it lacks details on behavior, error handling, or interaction with siblings, making it incomplete 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.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so there's no need for parameter details in the description. The baseline for this scenario is 4, as the description appropriately doesn't add unnecessary param info, but it doesn't fully compensate for other gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('stop') and target ('any running animation'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'hide_avatar' or 'move_avatar' which might also affect animations, 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 Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when an animation is running, but provides no explicit guidance on when to use this tool versus alternatives (e.g., 'hide_avatar' or 'move_avatar' might also stop animations indirectly) or any prerequisites. It lacks clear 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?
With no annotations provided, the description carries full burden for behavioral disclosure. It states it lists animations but doesn't describe what 'list' entails (e.g., format, pagination, sorting) or any constraints (e.g., permissions, rate limits). This leaves significant gaps for a 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.
Conciseness5/5Is 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 action and resource, 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.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. However, it lacks details on output format or behavioral traits, which would be helpful even for a simple list operation, keeping it at a baseline level.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does 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 fully documents the lack of inputs. The description adds no parameter information, which is appropriate here, but doesn't explicitly state 'no parameters needed,' so it's not a perfect 5.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all available animations'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list_poses' or 'list_voices' beyond the resource name, 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 Guidelines2/5Does 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 'list_poses' or 'list_voices', nor does it mention prerequisites or context for usage. It's a basic statement of function without operational guidance.
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 lists poses as PNG files, which hints at a read-only operation, but doesn't disclose behavioral traits like whether it requires authentication, has rate limits, returns structured data, or includes pagination. For a 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core action ('List all available sprite poses') and adds clarifying detail ('PNG files') without waste. 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.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It covers the purpose but lacks behavioral context like return format or usage guidelines. For a list tool with no structured data on outputs or behavior, it should provide more completeness, such as hinting at the response structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does 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 fully documents the lack of inputs. The description adds no parameter details, which is appropriate. Baseline is 4 for zero parameters, as no additional semantic information is needed beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all available sprite poses'), specifying they are PNG files. It distinguishes from siblings like list_animations and list_voices by focusing on poses, though it doesn't explicitly contrast them. The purpose is specific and actionable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. It doesn't mention prerequisites, such as whether an avatar must be shown first, or contrast with tools like play_animation or create_animation. The description implies usage for listing poses 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. 'List available voices' implies a read-only operation, but it doesn't specify whether this requires authentication, what the return format is (e.g., list of names, objects with properties), if there are rate limits, or if it's cached. For a tool with zero annotation coverage, this leaves significant gaps in understanding its 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence ('List available voices') that is front-loaded and wastes no words. It directly conveys the core action and resource without unnecessary elaboration, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on behavior, usage context, or output. For a simple read operation, this might suffice, but without annotations or output schema, it doesn't provide enough context for an agent to fully understand how to integrate it, such as what data is returned.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, but it correctly implies no inputs are required. Baseline for 0 parameters is 4, as the description aligns with the empty schema without introducing confusion.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List available voices' clearly states the verb ('List') and resource ('available voices'), making the tool's purpose immediately understandable. It distinguishes itself from siblings like 'set_voice' (which modifies voice) and 'speak' (which uses voice), though it doesn't explicitly contrast with them. The description avoids tautology since 'list_voices' as a name could imply other actions, but the description clarifies it's about listing available options.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 it should be called before 'set_voice' or 'speak'), nor does it indicate if it's for discovery, configuration, or other contexts. With siblings like 'set_voice' and 'speak', some implicit usage might be inferred, but no explicit when/when-not or alternative tools are stated.
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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