Generate-Prd-Prompt

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose with no ambiguity. The 'generate' tools create prompts based on specified inputs, while the 'get' tools fetch markdown templates, and each targets a different document type (bug analysis, codebase analysis, PRD). There is no overlap in functionality between tools.
Naming Consistency5/5
Tool names follow a consistent verb_noun pattern throughout. All tools use snake_case with 'generate' or 'get' as the verb followed by a specific noun (e.g., generate_bug_analysis_prompt, get_prd_template). This predictability makes the set easy to navigate and understand.
Tool Count5/5
With 6 tools, the server is well-scoped for its purpose of generating prompts and templates for analysis and PRD documents. Each tool earns its place by covering distinct aspects (prompt generation and template fetching) across three document types, avoiding bloat while providing comprehensive coverage.
Completeness5/5
The tool surface is complete for the domain of generating prompts and templates for analysis and PRD documents. It offers CRUD-like coverage with 'generate' for creation and 'get' for retrieval, covering all three document types (bug analysis, codebase analysis, PRD) without any gaps or dead ends.
Average 3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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 'generate' and 'assembled', implying creation or composition, but fails to detail output format, any side effects, error handling, or constraints like rate limits. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
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 function without unnecessary words. It is appropriately sized and front-loaded, though it could be slightly more informative without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a generation tool with 3 parameters, 0% schema coverage, no annotations, and no output schema, the description is insufficient. It lacks details on parameter usage, output expectations, and behavioral traits, making it incomplete for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 for undocumented parameters. It lists 'technology stacks and severity level', which partially maps to two parameters, but omits 'bug_context' entirely and provides no details on data types, formats, or examples. This adds minimal value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('generate') and resource ('assembled bug analysis prompt') with specific attributes ('specified technology stacks and severity level'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_bug_analysis_template', which might retrieve rather than generate prompts, leaving room for ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as 'get_bug_analysis_template' or other prompt-generation siblings. It lacks context about prerequisites, typical scenarios, or exclusions, offering minimal usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'generates' a prompt, implying a creation or assembly action, but doesn't clarify if this involves data processing, API calls, or other behaviors. Missing details include potential side effects, error handling, or output format, which are critical for a tool with no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the core action and parameters without unnecessary words. It's front-loaded with the main purpose, making it easy to parse quickly. Every part of the sentence earns its place by specifying key elements like 'assembled,' 'codebase analysis prompt,' and the parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (2 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the output (e.g., what the generated prompt looks like), behavioral traits, or parameter details. For a tool that generates content, more context is needed to guide effective use, especially with siblings that might overlap in functionality.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions parameters ('technology stacks and analysis focus areas'), but with 0% schema description coverage, it doesn't add meaningful semantics. It fails to explain what 'technology_stack' and 'analysis_focus' arrays should contain (e.g., strings, objects), their expected formats, or examples. This leaves the two required parameters largely undocumented, not compensating for the schema gap.
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 assembled codebase analysis prompt with specified technology stacks and analysis focus areas.' It uses specific verbs ('generate,' 'assembled') and resources ('codebase analysis prompt'), making the action clear. However, it doesn't differentiate from siblings like 'get_codebase_analysis_template' (which might retrieve a template rather than generate a prompt), leaving room for improvement.
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 siblings such as 'generate_bug_analysis_prompt' or 'get_codebase_analysis_template,' nor does it specify prerequisites, contexts, or exclusions. This lack of comparative information makes it harder for an agent to choose correctly among related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states what it does ('Generate an assembled...prompt') without disclosing behavioral traits like output format, whether it's idempotent, permission requirements, or error handling. It mentions 'assembled' but doesn't clarify what that entails operationally.
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 ('Generate an assembled Product Requirements Document prompt') and specifies key inputs. There is no wasted verbiage, making it 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and 0% schema description coverage for 3 parameters, the description is incomplete. It lacks details on output (what the generated prompt looks like), parameter usage, error conditions, and behavioral context, leaving significant gaps for an AI agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 names 'technology stacks' and 'analysis focus areas' (mapping to two parameters) and implies 'project_context' through 'specified', but doesn't explain what these parameters mean, their expected formats, or examples. This adds minimal semantic value beyond parameter names.
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 'Generate' and the resource 'Product Requirements Document prompt', specifying it's 'assembled' with 'technology stacks and analysis focus areas'. It distinguishes from siblings like 'get_prd_template' (which likely retrieves rather than generates) but doesn't explicitly differentiate from other generate_* tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'get_prd_template' or other generate_* prompts. The description implies usage for PRD generation but lacks context on prerequisites, timing, or comparisons with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states what is fetched without detailing behavioral traits like rate limits, authentication needs, or response format. It mentions 'comprehensive' and '8 sections total', adding some context, but lacks depth on how the tool operates beyond the basic fetch action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the key action and details without any waste. Every word earns its place by specifying the fetch action, template type, and structural details.
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 0 parameters and no annotations, the description adequately covers the purpose and structure of the template. However, without an output schema, it doesn't explain return values or format, and it lacks usage guidelines, leaving gaps in contextual understanding for a tool with siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by specifying the template type and section count, which is relevant context beyond the schema, though minimal since there are no parameters to explain.
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 ('Fetch') and resource ('bug analysis markdown template'), specifying it's comprehensive with 8 standard sections. It distinguishes from siblings like 'get_codebase_analysis_template' by focusing on bug analysis, though it doesn't explicitly contrast with 'generate_bug_analysis_prompt'.
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 'generate_bug_analysis_prompt' or other template-fetching siblings. The description implies usage for obtaining a template but lacks context on prerequisites 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 the full burden of behavioral disclosure. It states the tool fetches a template but doesn't describe any behavioral traits such as whether it requires authentication, has rate limits, returns static or dynamic content, or if it's idempotent. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the key action ('Fetch') and resource, with no wasted words. 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.
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 (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks behavioral details and usage context. With no output schema, it doesn't explain return values, which is a gap, but the simplicity of the tool keeps it from being severely incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description adds no parameter information, which is appropriate here. Baseline is 4 for zero parameters, as no compensation is needed.
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 ('Fetch') and resource ('comprehensive codebase analysis markdown template'), specifying it includes 'all standard sections (12 sections total)'. However, it doesn't explicitly differentiate from sibling tools like 'get_bug_analysis_template' or 'get_prd_template', which reduces 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 'generate_codebase_analysis_prompt' or explain scenarios where fetching a template is preferred over generating a prompt, 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. It mentions fetching a template but lacks details on behavioral traits like whether it requires authentication, has rate limits, returns static or dynamic content, or if the template is editable. This is a significant gap 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 front-loads the purpose ('Fetch a comprehensive Product Requirements Document markdown template') and adds useful detail ('with all standard sections (14 sections total)') without any wasted words.
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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but has clear gaps. It explains what the tool does but lacks behavioral context and usage guidelines, making it minimally viable for this low-complexity scenario.
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 adds context about the template's comprehensiveness and section count, which is valuable beyond the schema. Baseline is 4 for zero parameters.
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 ('Fetch') and resource ('Product Requirements Document markdown template'), specifying it's comprehensive with 14 standard sections. It distinguishes from siblings like 'get_bug_analysis_template' by focusing on PRDs, not bug analysis or codebase templates.
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 like 'generate_prd_prompt' is provided. The description implies it fetches a template, but doesn't clarify if this is for starting a PRD versus generating content, leaving usage context ambiguous.
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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