Mercury Spec Ops MCP Server

Server Quality Checklist

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Latest release: v0.1.2
Disambiguation5/5
Each tool is clearly tied to a specific domain (PRD, codebase analysis, bug analysis) and a specific action (generate prompt or fetch template). There is no overlap between the six tools, so an agent can reliably select the correct one.
Naming Consistency5/5
All tool names follow the consistent pattern generate_<domain>_prompt or get_<domain>_template. The distinction between 'prompt' and 'template' is meaningful and matches the tool function, and the domain segment is consistent throughout.
Tool Count5/5
Six tools is well-scoped for a prompt/template serving server. Each tool serves a distinct purpose, and the count is large enough to be useful without being bloated.
Completeness5/5
For each of the three domains, there is both a prompt generator and a template fetcher, covering the full lifecycle of using these resources. There are no obvious missing operations within the stated purpose of the server.
Average 3.7/5 across 6 of 6 tools scored. Lowest: 2.9/5.
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
- Last stable release on November 14, 2025
- 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.
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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, the description must disclose behavior, but it only says 'generate' without explaining what happens (e.g., returns a prompt string, whether it calls an LLM, or any side effects). The term 'assembled' is ambiguous and not elaborated.
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 sentence with no wasted words, front-loading the core purpose. However, it could have been slightly more informative without becoming verbose, so it doesn't reach the 'zero waste plus added value' level of a 5.
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?
The tool has no output schema and no annotations, and the description does not specify what the generated prompt looks like or what the caller should do with it. For a simple tool, this is still a notable gap that an agent would need to infer.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the parameters. The description mentions technology_stack and severity, reinforcing the schema, but adds no extra meaning for the optional bug_context parameter.
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 a specific action ('Generate an assembled bug analysis prompt') and identifies the key inputs ('specified technology stacks and severity level'). While this distinguishes it from the sibling tools by name, it does not explicitly contrast with generate_codebase_analysis_prompt or generate_prd_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_codebase_analysis_prompt or get_bug_analysis_template. The description only states what it does, not the appropriate context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description should disclose behavioral traits. It only states the action of generating a prompt but does not mention whether it is a pure read-only operation, what the output looks like, or any side effects. This is a 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 sentence that front-loads the key information ('Generate an assembled Product Requirements Document prompt') and then specifies the inputs. Every word contributes to the purpose.
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?
The tool is simple, but with no output schema and no annotations, the description should at least state what the tool returns. It does not mention the output format or how the prompt is 'assembled', leaving the agent with uncertainties about the tool's behavior beyond the parameters.
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 already provides descriptions for all three parameters, so the baseline is 3. The description's mention of 'specified technology stacks and analysis focus areas' aligns with the parameters but does not add significant meaning beyond what the schema already states.
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 uses the specific verb 'generate' and identifies the resource as an 'assembled Product Requirements Document prompt'. It mentions the key inputs (technology stacks and analysis focus areas), clearly distinguishing this from sibling tools like generate_codebase_analysis_prompt or get_prd_template by specifying the PRD context.
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 when to use the tool (when needing an assembled PRD prompt with specified stacks and focus areas) but does not explicitly state when not to use it or name alternatives like get_prd_template for simple templates. There is no guidance on selecting between this and the sibling prompt-generating tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It only says the tool 'generates an assembled prompt' but does not explain the output format, side effects, or how the assembly works. This leaves significant ambiguity about what the tool actually returns or does.
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, concise sentence that front-loads the verb and resource, with no filler or redundant content. It is appropriately sized for the tool's purpose.
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?
The tool is simple and the schema covers parameters, but there is no output schema or description of what the generated prompt looks like. While the description is minimal, it is adequate for basic understanding, though it leaves room for more detail about the assembled prompt's content or return value.
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 descriptions for both parameters (technology_stack and analysis_focus), providing 100% coverage. The description merely echoes these parameter names without adding new semantic detail, so it meets the baseline but does not exceed it.
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 action ('Generate') and resource ('codebase analysis prompt'), with qualifiers 'assembled', 'specified technology stacks', and 'analysis focus areas' that distinguish it from sibling prompt generators like generate_prd_prompt or 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (when you need a codebase analysis prompt) but does not explicitly mention when to use this tool over alternatives. Sibling tools exist but are not referenced, so the guidance is only implicit.
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 burden of behavioral disclosure. It does convey that this is a fetch operation (implying read-only) and mentions the template has 8 sections, which adds some transparency. However, it doesn't describe the return format, any limitations, or what the template actually includes beyond the section count. For a simple template retrieval, this is adequate but not rich.
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 concise sentence that front-loads the verb and resource, includes a meaningful detail ('8 sections total'), and contains no fluff or redundant information. It is perfectly 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.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity tool with no parameters and no output schema, the description covers the essential purpose and content. It could be improved by noting that this is a static template versus the dynamic generation provided by generate_bug_analysis_prompt, which would help the agent choose correctly, but the given description is still sufficiently complete for the tool's simplicity.
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 zero parameters, and the input schema is an empty object, so there are no parameter semantics to explain. The baseline for 0 params is 4, and the description adds useful context about the output (8 sections) without being parameter-related.
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 uses a specific verb 'Fetch' and names the exact resource 'comprehensive bug analysis markdown template.' The added detail '8 sections total' gives specificity and differentiates it from sibling template tools like get_prd_template and get_codebase_analysis_template, as well as from 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?
The description offers no guidance on when to use this tool versus alternatives such as generate_bug_analysis_prompt. It does not state that this is a static template for standard use, nor does it mention that a custom prompt would require the sibling tool. This lack of context leaves the agent to infer usage without explicit direction.
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 burden. The verb 'Fetch' implies a read-only operation, and the detail about 12 sections gives some expectation of the return. However, it does not disclose potential side effects, prerequisites, or whether the template is static or dynamically generated. This is acceptable for a simple getter but not rich.
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?
One single sentence, front-loaded with the action and resource, and every word earns its place. No fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With zero parameters and no output schema, the description fully conveys the tool's output (a comprehensive template with 12 standard sections) and is sufficient for an agent to invoke it correctly. No additional context is needed.
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 schema has zero parameters, so there is nothing to document. The description adds no parameter-specific meaning, but the baseline of 4 applies for seamlessly handling the no-parameter case.
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?
Uses the specific verb 'Fetch' and names the resource 'comprehensive codebase analysis markdown template', with a concrete detail (12 sections total). This clearly distinguishes it from sibling template tools for PRDs or bug analysis and from the prompt-generation 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?
The description states what the tool does but gives no explicit guidance on when to choose it over the sibling generate_codebase_analysis_prompt or other template getters. There is no mention of alternatives or exclusions, so the agent is left to infer usage from the name alone.
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 transparency burden. It clearly communicates a read-only 'Fetch' operation (implying no side effects) and discloses the output format (markdown) and content scope (all standard sections, 14 total). This is sufficient behavioral context for a simple template retrieval 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, well-structured sentence that front-loads the action and resource. Every element adds value: 'comprehensive', 'markdown template', 'all standard sections (14 sections total)'. There is no redundancy or extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, no-output-schema tool, the description is nearly complete: it specifies the resource (PRD template), the format (markdown), and the scope (14 sections). It could optionally mention return type or safety, but these are either implied or available after invocation. The description fully covers the tool's purpose.
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 zero parameters, so the baseline score is 4. The description adds no parameter-specific details (as none exist) but mentions content characteristics ('14 sections total'), which is appropriate given there is nothing else to document.
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 uses a specific verb ('Fetch') and resource ('Product Requirements Document markdown template'), and explicitly mentions '14 sections total' to define scope. It clearly distinguishes itself from sibling tools like get_codebase_analysis_template and get_bug_analysis_template by focusing specifically on PRD templates.
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?
The description implies usage: users should call this tool when they need a PRD markdown template. The resource is explicitly identified, providing clear context without naming alternatives. It does not explicitly state when not to use it, but the distinction from sibling tools is implicit through the PRD-specific wording.
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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