Productboard MCP Server

Server Quality Checklist

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Latest release: v1.0.1
Disambiguation5/5
Every tool has a clearly distinct purpose targeting specific resources (companies, components, features, notes, products) with consistent get/list patterns. There is no ambiguity between tools as each operates on a different entity type with clear boundaries.
Naming Consistency5/5
All tools follow a perfect verb_noun pattern with 'get_' prefix, consistently using snake_case throughout. The naming convention is completely predictable with 'get_<resource>s' for list operations and 'get_<resource>_detail' for detail operations.
Tool Count4/5
11 tools is reasonable for a Productboard integration covering multiple entity types. The count feels slightly heavy since some operations could potentially be consolidated, but each tool serves a distinct purpose and the scope justifies the number.
Completeness2/5
The tool surface is severely incomplete for a product management platform. While read operations are well-covered for multiple entity types, there are no create, update, or delete operations, making this a read-only API that cannot support full product management workflows.
Average 3.1/5 across 11 of 11 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed 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.
Add a glama.json file to provide metadata about your server.
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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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 but only states the basic action. It doesn't mention pagination behavior (implied by parameters but not described), rate limits, authentication requirements, or what 'all notes' means in context (e.g., access permissions, default ordering). The description adds minimal value beyond the bare function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence that directly states the tool's purpose. There's no wasted language or unnecessary elaboration, making it front-loaded and efficient for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (14 parameters, no annotations, no output schema), the description is insufficient. It doesn't explain the return format, pagination behavior, or how filtering parameters interact. For a tool with rich filtering options and no structured output documentation, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 14 parameters with their constraints and mutual exclusivities. The description adds no parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Returns a list of all notes' states a clear verb ('returns') and resource ('notes'), but it's vague about scope and filtering capabilities. It doesn't distinguish from sibling tools like 'get_note_detail' or explain how it differs from other list tools like 'get_companies' or 'get_features'.
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 on when to use this tool versus alternatives is provided. The description doesn't mention when to use 'get_notes' versus 'get_note_detail' or other sibling list tools, nor does it provide any context about prerequisites or appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns detailed information but doesn't specify what 'detailed' includes (e.g., fields like name, address, revenue), whether it's a read-only operation, authentication requirements, rate limits, or error handling. For a tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core purpose, making it easy to parse quickly, and every part of the sentence contributes to understanding the tool's role.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool that retrieves detailed data. It doesn't explain what 'detailed information' entails (e.g., specific fields or data structure), potential side effects, or error scenarios. For a read operation with no structured output documentation, 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 input schema has 100% description coverage, with the single parameter 'companyId' documented as 'ID of the company to retrieve'. The description adds no additional meaning beyond this, such as format examples (e.g., numeric vs. string IDs) or sourcing instructions. With high schema coverage, the baseline score of 3 is appropriate as the schema handles parameter documentation adequately.
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 ('Returns') and resource ('detailed information about a specific company'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'get_companies' (which likely lists multiple companies) or 'get_product_detail' (which focuses on products rather than companies), missing explicit sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a company ID), exclusions, or comparisons to siblings like 'get_companies' for bulk retrieval or 'get_product_detail' for product-specific details, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a read operation ('Returns'), but doesn't cover critical aspects like authentication needs, rate limits, error conditions (e.g., invalid componentId), response format, or whether it's idempotent. For a detail-retrieval tool with zero annotation coverage, 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 with zero wasted words. It's front-loaded with the core purpose and appropriately sized for a simple retrieval tool. Every word earns its place by conveying the essential action and target.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema, no annotations), the description is incomplete. It lacks behavioral context (e.g., what 'detailed information' includes, error handling), usage guidance relative to siblings, and output expectations. While concise, it doesn't provide enough information for an agent to use the tool confidently without external knowledge.
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 'componentId' fully documented in the schema. The description doesn't add any parameter-specific details beyond what the schema provides (e.g., format examples, sourcing guidance). With high schema coverage, 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 action ('Returns detailed information') and the resource ('about a specific component'), which is a specific verb+resource combination. However, it doesn't explicitly distinguish this tool from its sibling 'get_components' (which presumably lists components) or other detail-retrieval siblings like 'get_company_detail' and 'get_product_detail'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a component ID), contrast with 'get_components' for listing, or specify contexts where detailed vs. summary information is required. The agent must infer usage from the tool name and sibling 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 of behavioral disclosure. It states the tool returns information (implying a read operation) but lacks details on permissions, rate limits, error handling, or response format. For a tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero waste, front-loading the core purpose efficiently. It's appropriately sized for a simple tool and doesn't include unnecessary details, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'detailed information' includes, how results are structured, or any behavioral traits like error cases. For a tool with no structured support, the description should provide more context to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description doesn't add any parameter-specific information beyond what's in the schema, which has 100% coverage and clearly documents the 'featureId' parameter. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract from the schema's documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Returns') and resource ('detailed information about a specific feature'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_features' (which likely lists features) or 'get_feature_statuses' (which might return status information), missing full sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'get_feature_detail' over 'get_features' (for listing) or 'get_feature_statuses' (for status info), nor does it specify prerequisites or exclusions, leaving the agent with no 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 it 'returns' information, implying a read-only operation, but doesn't address permissions, error handling, rate limits, or what 'detailed information' includes (e.g., fields, format). This leaves significant gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action and resource, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a detail-retrieval tool. It doesn't explain what 'detailed information' entails (e.g., content, metadata), potential errors, or behavioral traits, leaving the agent with insufficient context to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'noteId' fully documented in the schema. The description adds no additional meaning beyond implying retrieval of a 'specific note', which aligns with the schema but doesn't provide extra context like ID format or examples. Baseline 3 is appropriate given the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Returns') and resource ('detailed information about a specific note'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_notes' (which likely lists multiple notes) or other detail tools, keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_notes' for listing notes or specify prerequisites such as needing a note ID, leaving the agent with no contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns detailed information, but doesn't describe what 'detailed' includes, potential errors (e.g., invalid product IDs), performance traits, or authentication needs. This leaves significant gaps in understanding how the tool behaves beyond its basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action ('Returns'), making it easy to parse quickly. Every part of the sentence contributes essential information, earning a perfect score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a detail-retrieval tool. It doesn't specify what 'detailed information' includes (e.g., fields, structure), error handling, or how it differs from sibling tools. For a tool with one parameter but no structured output documentation, 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 input schema has 100% description coverage, with the 'productId' parameter clearly documented. The description adds no additional parameter semantics beyond implying retrieval of a 'specific product' tied to the ID. This meets the baseline score of 3, as the schema adequately covers parameter details without extra value from the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Returns') and resource ('detailed information about a specific product'), making it immediately understandable. However, it doesn't explicitly differentiate this tool from its sibling 'get_products' (which likely returns a list rather than details for one product), preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_products' (for lists) or other detail tools (e.g., 'get_company_detail'). It lacks context about prerequisites, such as needing a product ID from another source, which is essential for effective 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It usefully reveals pagination behavior and the fixed page limit of 100, which are important operational details not inferable from the schema alone. However, it doesn't mention whether this is a read-only operation, what authentication is required, rate limits, or what the response format looks like.
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 brief (two sentences) and front-loaded with the core purpose. The second sentence adds important behavioral context about pagination. There's no wasted verbiage, though it could be slightly more structured with explicit parameter documentation.
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, no output schema, and 0% schema description coverage for the single parameter, the description provides adequate but incomplete context. It covers the core purpose and pagination behavior but leaves the parameter undocumented and doesn't describe response format. For a simple list tool with pagination, this is minimally viable but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It doesn't mention the 'page' parameter at all, leaving it completely undocumented. The description's mention of pagination and page limit indirectly relates to the parameter but doesn't explain its purpose, format, or usage. With 1 parameter at 0% coverage and no compensation in the description, this meets the baseline for minimal documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Returns') and resource ('list of all companies'), making the purpose immediately understandable. It distinguishes from siblings like 'get_company_detail' by indicating it returns a list rather than a single entity. However, it doesn't explicitly differentiate from other list tools like 'get_components' or 'get_features' beyond the resource name.
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. While it's clear this returns companies (not components or features), there's no mention of when to use 'get_companies' versus 'get_company_detail' or other sibling tools. No prerequisites, exclusions, or comparative context is provided.
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 returns details (not just summaries), is paginated, and has a fixed page limit of 100. However, it doesn't mention other important behaviors like rate limits, authentication requirements, error handling, or what 'detail' includes (e.g., fields returned).
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 sized with two sentences that each add value: the first states the purpose, the second adds critical behavioral context (pagination and limit). It's front-loaded with the core purpose. No wasted words, though it could be slightly more 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 no annotations and no output schema, the description is incomplete. It covers the purpose and pagination behavior but misses parameter documentation, return value details, error conditions, and usage context relative to siblings. For a list tool with pagination, this leaves significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It doesn't mention the 'page' parameter at all, leaving it undocumented. However, since there's only one parameter and it's optional with a default, the impact is moderate. The description implies pagination but doesn't explain how the 'page' parameter interacts with it.
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 ('Returns') and resource ('detail of all products'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_product_detail' which presumably returns a single product's details versus all products.
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 'get_product_detail' or other list tools (e.g., 'get_components', 'get_features'). The description mentions pagination but doesn't explain when to use paginated vs. non-paginated alternatives if they exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 effectively adds key behavioral traits: it specifies that the API is paginated and that the page limit is always 100, which are crucial for understanding how to interact with the tool. However, it doesn't cover other aspects like error handling or response format.
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 purpose, and the second adds essential behavioral context (pagination and limit). It's appropriately sized and front-loaded, with every sentence earning 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 (1 parameter, no output schema, no annotations), the description is moderately complete. It covers the core purpose and pagination behavior, but gaps remain: no parameter details, no sibling differentiation, and no output information. It's adequate but has clear omissions.
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 1 parameter with 0% description coverage, so the description must compensate. It doesn't mention the 'page' parameter at all, failing to explain its purpose or usage. Baseline is 3 because the schema does minimal work, but the description adds no parameter-specific value beyond the general pagination note.
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 ('Returns') and resource ('list of all components'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'get_component_detail' which presumably returns a single component, so it misses full sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get_component_detail' or other list tools such as 'get_features'. It lacks explicit when/when-not instructions or named alternatives, leaving usage context implied at best.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 effectively adds key behavioral traits: it specifies that the API is paginated and that the page limit is always 100. This informs the agent about performance constraints and data handling, which is crucial for a list operation. However, it doesn't cover other aspects like error handling, rate limits, or authentication needs.
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 and front-loaded, consisting of just two sentences that directly state the purpose and key behavioral details. Every word earns its place, with no redundant or vague language, making it efficient and 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 the tool's low complexity (1 parameter, no output schema, no annotations), the description is adequate but has clear gaps. It covers the purpose and pagination behavior well, but lacks details on usage guidelines, error handling, and full parameter semantics. For a simple list tool, this is minimally viable but could be more comprehensive.
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 1 parameter with 0% description coverage, so the schema provides no semantic information. The description compensates by implying pagination context, which relates to the 'page' parameter, but it doesn't explicitly explain what 'page' does or its format. This adds some value but doesn't fully clarify the parameter's role, keeping it at the baseline for minimal 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 ('Returns') and resource ('a list of all features'), making the purpose specific and understandable. It distinguishes from sibling tools like 'get_feature_detail' (which returns a single feature) by indicating it returns a list. However, it doesn't explicitly differentiate from other list tools like 'get_components' or 'get_notes' beyond the resource name.
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 use 'get_features' over 'get_feature_detail' for individual features, or how it relates to other list tools like 'get_components'. There's no context on prerequisites, exclusions, or comparative use cases, leaving the agent with minimal direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 effectively reveals two key traits: pagination (a structural behavior) and a fixed page limit of 100 (a constraint). This goes beyond basic functionality to inform the agent about operational characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste: the first states the core purpose, the second adds critical behavioral context (pagination and limit). Every word earns its place, and the structure is front-loaded with 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?
For a read-only list tool with no annotations and no output schema, the description covers the basic purpose and key behavioral constraints (pagination/limit). However, it lacks details on response format, error handling, or how to interpret 'feature statuses' in context of sibling tools, leaving some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for its single parameter 'page', and the tool description provides no information about parameters. However, with only one parameter and a default value in the schema, the baseline is 3 as the schema does minimal but sufficient lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Returns') and resource ('list of all feature statuses'), making the purpose unambiguous. It doesn't explicitly differentiate from siblings like 'get_features' or 'get_feature_detail', but the specific focus on 'statuses' provides some implicit distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'get_features' or 'get_feature_detail'. The description only states what it does, without context about appropriate use cases or prerequisites.
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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