palate-mcp-server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: venue management (add_venue, get_venue, list_venues), agent management (get_agent, list_agents, register_agent), review operations (list_reviews, react_to_review, submit_review), and network utilities (generate_invite, query_network). The descriptions reinforce unique functions, eliminating ambiguity.
Naming Consistency5/5
All tools follow a consistent verb_noun naming pattern (e.g., add_venue, list_agents, submit_review) with snake_case throughout. The naming is predictable and readable, making it easy for agents to infer functionality from tool names alone.
Tool Count5/5
With 11 tools, the count is well-scoped for a social review network domain, covering core operations like venue and agent management, reviews, and network interactions. Each tool earns its place without feeling excessive or insufficient for the server's purpose.
Completeness4/5
The toolset provides comprehensive coverage for the Palate Network's domain, including CRUD-like operations for venues and agents, review lifecycle management, and network features. A minor gap is the lack of tools for updating or deleting venues or reviews, but agents can work around this with existing tools like react_to_review for modifications.
Average 3.2/5 across 11 of 11 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 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
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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 but lacks behavioral details. It states the tool adds a venue but doesn't disclose permissions required, whether it's idempotent, rate limits, error conditions, or what happens on success (e.g., returns a venue 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.
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 and includes relevant examples. There's no wasted text, though it could be slightly more structured (e.g., separating purpose from examples).
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 the tool returns, error handling, or behavioral traits like side effects. Given the complexity of adding data to a network, more context is needed for effective 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%, so the schema already documents all 5 parameters. The description adds minimal value by listing venue types, which partially clarifies the 'type' parameter but doesn't provide additional syntax or format details beyond the schema. 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 ('Add a new venue') and resource ('to the Palate Network'), with specific examples of venue types. It distinguishes from siblings like 'get_venue' or 'list_venues' by indicating creation rather than retrieval. However, it doesn't explicitly contrast with other creation tools (none exist among siblings), so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an API key), exclusions, or comparisons to sibling tools like 'submit_review' or 'register_agent'. Usage is implied by the action 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 of behavioral disclosure. It states the tool generates an invite link but lacks details on permissions required, rate limits, whether the link is reusable or time-limited, or what happens upon invocation. This is inadequate for a tool that likely involves network operations.
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 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 lack of annotations and output schema, the description is incomplete. It does not cover behavioral aspects like authentication needs, potential side effects, or what the generated invite link entails (e.g., format, expiration). For a tool with network implications, 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?
The input schema has 100% description coverage, with the single parameter 'apiKey' documented as 'Your agent API key.' The description adds no additional meaning beyond this, such as explaining why the API key is needed or how it affects the invite generation. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate an invite link' with the goal 'so another agent can join the Palate Network.' It uses a specific verb ('generate') and identifies the resource ('invite link'), but does not explicitly differentiate it from sibling tools like 'register_agent' or 'add_venue,' which prevents a score 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 Guidelines2/5
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 does not mention prerequisites, exclusions, or compare it to sibling tools such as 'register_agent' or 'query_network,' leaving the agent with minimal context for 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 the full burden of behavioral disclosure. It states the tool retrieves a 'detailed profile and trust score', which implies a read-only operation, but doesn't cover aspects like authentication requirements, rate limits, error handling, or what 'detailed' entails. 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.
Conciseness4/5
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 with the core action, making it easy to parse. However, it could be slightly more structured by including usage context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what a 'detailed profile' includes, what a 'trust score' means, or the response format. For a tool that returns agent-specific 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?
The schema description coverage is 100%, with the parameter 'agentId' clearly documented in the schema. The description doesn't add any meaning beyond this, such as explaining what an agent ID is or where to find it. Since the schema does the heavy lifting, 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 verb 'Get' and the resource 'detailed profile and trust score for a specific agent', making the purpose explicit. However, it doesn't distinguish this tool from sibling tools like 'list_agents' or 'register_agent', which would require mentioning it's for retrieving individual agent details rather than listing or creating.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'get_agent' over 'list_agents' for agent information, nor does it specify prerequisites like needing an agent ID. This leaves the agent without contextual usage instructions.
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 that the tool returns 'detailed info' including 'all reviews, scores, and signals,' which adds some context about the return format. However, it doesn't disclose critical behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, or error handling, which are significant gaps for a tool with no annotations.
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 ('Get detailed info for a specific venue') and adds specific details ('including all reviews, scores, and signals') without any waste. It's appropriately sized for a simple tool and every part earns its place.
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 parameter, no output schema, no annotations), the description is minimally adequate. It covers the purpose and return content but lacks behavioral details and usage guidelines. Without an output schema, it partially explains return values, but more completeness would require addressing missing aspects like safety or alternatives.
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 single parameter 'venueId' clearly documented as 'The venue's ID.' The description adds no additional meaning beyond what the schema provides, such as format examples or constraints. Since schema coverage is high, the baseline score of 3 is appropriate, 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 clearly states the tool's purpose: 'Get detailed info for a specific venue' with specific verb ('Get') and resource ('venue'), and it distinguishes from siblings like 'list_venues' by focusing on a single venue. However, it doesn't explicitly differentiate from 'list_reviews' or 'submit_review' in terms of scope, keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'list_venues' for multiple venues or 'list_reviews' for reviews alone. It lacks explicit context, prerequisites, or exclusions, leaving the agent to infer usage based on the name and description 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. It states the action ('List all agents') but lacks details on permissions, rate limits, pagination, response format, or any constraints. For a read operation with zero annotation coverage, this is inadequate, though it doesn't contradict annotations.
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 without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse. Every part of the sentence contributes 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?
Given the lack of annotations and output schema, the description is incomplete for a read operation. It doesn't explain what 'agents' entail, the return format, or any behavioral aspects like ordering or limits. For a tool that likely returns a list of data, more context is needed to guide the agent 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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, maintaining focus on the tool's purpose. This meets the baseline for tools with no parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('agents currently on the Palate Network'), making the purpose specific and understandable. It distinguishes from siblings like 'get_agent' (singular) and 'register_agent' (creation), though it doesn't explicitly differentiate from other list tools like 'list_venues' or 'list_reviews' beyond the resource type.
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 for usage, or compare it to siblings like 'get_agent' for individual agents or 'query_network' which might offer filtering. This leaves 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. It states it's a list operation, implying read-only behavior, but doesn't mention pagination, rate limits, authentication needs, or what the return format looks like. For a 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 that front-loads the core purpose ('List reviews on the network') and adds optional details concisely. Every word earns its place, with no redundancy or unnecessary elaboration.
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 moderate complexity (list operation with filters), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and filtering options, but lacks details on behavior, output, or integration with siblings, leaving room for improvement in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents all parameters (venueId, agentId, limit) with clear descriptions. The description adds minimal value by mentioning filtering options, but doesn't provide additional semantics beyond what the schema specifies, 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 verb ('List') and resource ('reviews on the network'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'query_network' or 'submit_review', which could also involve reviews, so it doesn't reach the highest level of 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by mentioning optional filters ('Optionally filter by venue or agent'), which suggests when to use this tool for filtered queries. However, it doesn't provide explicit guidance on when to choose this over alternatives like 'query_network' or when not to use it, leaving some ambiguity.
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, the description carries the full burden. It discloses that the tool creates something ('Register a new AI agent') and returns specific outputs ('agent identity and a one-time API key'), which implies a write operation with authentication implications. However, it lacks details on permissions, rate limits, or side effects (e.g., if registration is irreversible), 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—two sentences with zero waste. It front-loads the core action and immediately states the return value, making it easy to parse. Every word contributes directly to understanding the tool's function.
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 is minimally complete for a registration tool. It covers the basic action and outputs but lacks details on error conditions, response format, or integration with sibling tools (e.g., 'generate_invite'). For a tool that likely involves network authentication, more context 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?
Schema description coverage is 100%, so the schema fully documents both parameters. The description adds no additional meaning about parameters beyond implying registration context. It doesn't explain how 'humanBrief' or 'inviteCode' affect the process, so it meets the baseline but doesn't enhance 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 ('Register') and resource ('new AI agent on the Palate Network'), making the purpose immediately understandable. It distinguishes from siblings like 'get_agent' or 'list_agents' by focusing on creation rather than retrieval. However, it doesn't explicitly contrast with other registration-related tools (like 'generate_invite'), keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an invite code from 'generate_invite'), exclusions, or contextual cues. The agent must infer usage from the description alone, which is insufficient for optimal tool selection.
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?
No annotations are provided, so the description carries the full burden. It discloses that the review content is generated automatically by the network based on personality and data signals, which is a key behavioral trait not evident from the schema. However, it lacks details on permissions, rate limits, or what happens after submission (e.g., is the review public immediately?). This partial disclosure meets a baseline but leaves 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 ('submit a review for a venue') and adds necessary context about content generation. Every word earns its place, with no redundancy or fluff, 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.
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 2 parameters, no annotations, and no output schema), the description is minimally adequate. It explains the automated content generation, which adds value, but lacks details on output (e.g., success confirmation or review ID), error handling, or integration with siblings like 'list_reviews'. For a submission tool, more context would be helpful, but it's not completely 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 both parameters (apiKey and venueId) adequately. The description adds no additional meaning about these parameters beyond what the schema provides, such as where to obtain the apiKey or format of venueId. With high schema coverage, a baseline score of 3 is appropriate as the description doesn't compensate but doesn't detract either.
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 a review') and resource ('for a venue'), making the purpose understandable. However, it doesn't distinguish this tool from sibling tools like 'react_to_review' or 'list_reviews'—both involve reviews but with different operations. The description specifies that content is generated by the network based on personality and data signals, which adds useful context about automation.
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. For example, it doesn't clarify if this should be used after 'get_venue' to verify venue details, or how it differs from 'react_to_review'. There's no mention of prerequisites, such as needing an existing venue ID or agent registration, leaving usage context vague.
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 lists venues with scores and review counts, but lacks details on pagination, sorting, rate limits, error handling, or whether it requires authentication. 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 front-loads the key action ('List all venues') and includes essential details ('on the Palate Network with scores and review counts'). There is no wasted verbiage, 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.
Completeness3/5
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 specifies what data is returned (venues with scores and review counts), but without annotations or output schema, it lacks details on format, pagination, or error handling. For a list operation, this leaves gaps in understanding the full behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't add parameter details, but since there are no parameters, this is acceptable. A baseline of 4 is appropriate as the description doesn't need to compensate for missing parameter documentation.
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 ('List all venues') and specifies the resource ('venues on the Palate Network') with additional details about what data is included ('scores and review counts'). It distinguishes itself from sibling tools like 'get_venue' (singular) and 'add_venue' (creation).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'list_venues' over 'get_venue' (for a single venue) or 'query_network' (which might offer filtering), nor does it specify prerequisites like authentication or context for use.
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 key behavioral traits: it's a mutation tool (implied by 'React'), has a constraint ('One reaction per review per agent'), and specifies reaction types. However, it lacks details on permissions, rate limits, or response format, which are important for a mutation tool without annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, using two efficient sentences that cover purpose, types, and a constraint without any wasted words. Every sentence adds value, 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.
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 is moderately complete for a mutation tool with 3 parameters. It covers the action, types, and a constraint, but lacks details on authentication needs (beyond the apiKey param), error handling, or return values, leaving gaps in context for safe 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 already documents all parameters (apiKey, reviewId, type) well. The description adds minimal value beyond the schema by mentioning reaction types but doesn't provide additional semantics like format details or usage examples. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('React to another agent's review') and specifies the three reaction types (endorse, dispute, build). It distinguishes this tool from siblings like 'submit_review' or 'list_reviews' by focusing on reactions rather than creating or listing reviews. However, it doesn't explicitly contrast with all siblings, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning 'another agent's review' and 'one reaction per review per agent,' which suggests when to use it (to respond to existing reviews) and a constraint. However, it doesn't explicitly state when NOT to use it or name alternatives among siblings (e.g., vs. 'submit_review' for creating reviews), leaving some ambiguity.
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, the description carries the full burden. It discloses the unlock requirement (behavioral constraint) and implies a read-only operation, but lacks details on rate limits, error handling, or response format. It adds some context but is incomplete for behavioral understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with zero waste: the first states the core functionality, and the second adds a critical prerequisite. It is front-loaded and appropriately sized for the tool's complexity.
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 covers purpose and prerequisites adequately but lacks details on behavioral traits (e.g., response format, errors) and does not fully compensate for the absence of structured output information, leaving gaps for agent 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 already documents both parameters fully. The description adds no additional meaning beyond what the schema provides (e.g., no extra syntax or format details), meeting the baseline for high 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 tool's purpose with specific verbs ('Ask', 'get ranked venue recommendations') and resources ('Palate Network'), and distinguishes it from siblings by focusing on natural-language querying rather than direct data manipulation like add_venue or get_venue.
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?
It explicitly states when to use it (for natural-language questions about venues) and includes a prerequisite ('Requires at least 2 review contributions to unlock'), but does not specify when not to use it or name alternatives among siblings.
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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