Ambiance MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: ast_grep_search is for structural code search, frontend_insights analyzes web architecture, local_context provides general query-aware retrieval, local_debug_context focuses on debugging, local_file_summary gives file-level analysis, and local_project_hints offers project navigation. The descriptions reinforce these unique roles, making tool selection unambiguous.
Naming Consistency4/5
The naming is mostly consistent with a clear pattern: all tools use snake_case and start with a descriptive prefix (ast_grep_, frontend_, local_) followed by a noun or noun phrase. The minor deviation is that 'frontend_insights' uses a compound noun while others are simpler (e.g., 'local_context'), but this doesn't hinder readability or predictability.
Tool Count5/5
With 6 tools, the count is well-scoped for a code analysis and development assistance server. Each tool serves a specific, valuable function (e.g., search, insights, context, debugging, file summary, project hints), and none feel redundant or out of place, fitting a typical range for such a domain.
Completeness5/5
The tool set provides comprehensive coverage for code analysis and development workflows: it includes search (ast_grep_search), architectural analysis (frontend_insights), context retrieval (local_context), debugging (local_debug_context), file-level inspection (local_file_summary), and project navigation (local_project_hints). There are no obvious gaps; agents can handle tasks from low-level code examination to high-level project understanding.
Average 3.7/5 across 6 of 6 tools scored. Lowest: 2.9/5.
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 full burden for behavioral disclosure. It mentions 'AI-powered analysis' and 'configurable performance options,' but lacks critical details: whether this tool is read-only or makes changes, what permissions are required, how errors are handled, whether it accesses external APIs (beyond the OpenAI mention), or what the output structure looks like. For a complex 7-parameter tool with AI capabilities, 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 reasonably concise and front-loaded, starting with the core purpose. Both sentences contribute value: the first defines capabilities, the second adds path and format context. There's no redundant information, though it could be slightly more structured by separating functional description from technical details.
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 (7 parameters, AI capabilities, multiple output formats) and the absence of both annotations and an output schema, the description is incomplete. It doesn't explain what the generated 'hints' actually contain, how word clouds or architecture detection work, what the performance implications are, or what happens when AI analysis is enabled without an API key. For a sophisticated analysis tool, this leaves too many operational questions unanswered.
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 adds minimal parameter semantics beyond the schema. It mentions 'absolute paths or relative paths' which aligns with the projectPath parameter, and 'multiple output formats' which aligns with the format parameter. However, with 100% schema description coverage, the schema already documents all 7 parameters thoroughly. The description doesn't provide additional context about parameter interactions or usage patterns beyond what's in the schema descriptions.
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 intelligent project navigation hints with word clouds, folder analysis, and architecture detection.' It specifies the verb 'generate' and the resource 'project navigation hints' with key capabilities. However, it doesn't explicitly differentiate this from sibling tools like 'local_context' or 'local_file_summary' which might also analyze local projects.
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 minimal usage guidance. It mentions 'Supports multiple output formats' and 'Accepts absolute paths or relative paths,' but offers no explicit advice on when to use this tool versus alternatives like 'local_context' or 'frontend_insights.' There's no guidance on prerequisites, performance trade-offs, or typical use cases beyond the basic functionality.
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 'embedding-enhanced analysis' and 'semantic embeddings', hinting at computational intensity, but does not disclose performance implications, rate limits, or error handling. For a tool with 10 parameters and no annotations, this leaves significant 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with a clear purpose and key features. It uses two sentences efficiently, though the second sentence could be slightly more streamlined. There is no wasted text, earning a high score for conciseness.
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 (10 parameters, no annotations, no output schema), the description is moderately complete. It covers the purpose and high-level functionality but lacks details on output format, error cases, and behavioral constraints. Without an output schema, the agent must infer return values, making this adequate but with 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 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by implying the tool uses embeddings for analysis, but it does not explain parameter interactions or provide additional context. 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.
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 ('map', 'analyzes') and resources ('routes, components, data flow, design system, and risks', 'Next.js/React projects'). It distinguishes itself from sibling tools by focusing on embedding-enhanced analysis for architecture insights, component similarities, and potential issues, unlike tools like ast_grep_search or local_file_summary which suggest different scopes.
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 explicit guidance on when to use this tool versus alternatives. It mentions analyzing Next.js/React projects but does not specify scenarios, prerequisites, or exclusions. Without context on how this differs from sibling tools like local_project_hints, the agent lacks clear usage 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 full burden and does well by disclosing key behavioral traits: it's 'completely offline with zero external dependencies for core functionality', uses 'AST + static heuristics', provides specific outputs (AnswerDraft, ranked JumpTargets, etc.), and mentions optional embedding enhancement. However, it doesn't cover error handling, performance characteristics, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with a high-level summary and listing four key outputs. It uses emojis and technical terms efficiently, though some phrases like 'actionable intelligence' are vague. Every sentence contributes, but it could be slightly more streamlined by integrating the offline note earlier.
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 (14 parameters, no annotations, no output schema), the description is moderately complete. It covers core functionality, offline nature, and output types, but lacks details on return values, error cases, or how outputs are structured. For a sophisticated analysis tool, more behavioral context would be helpful despite the rich schema.
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 baseline is 3. The description adds minimal parameter semantics beyond the schema—it mentions 'deterministic query-aware retrieval' (hinting at the 'query' parameter) and 'tight MiniBundle (≤3k tokens)' (relating to 'maxTokens'), but doesn't significantly enhance understanding of the 14 parameters. The value added is marginal given the comprehensive 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 tool provides 'enhanced local context with deterministic query-aware retrieval, AST-grep, and actionable intelligence' and lists four specific outputs (AnswerDraft, JumpTargets, MiniBundle, NextActions). It distinguishes from siblings by emphasizing AST-based analysis and offline functionality, though it doesn't explicitly contrast with tools like 'local_debug_context' or 'local_file_summary'.
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 for code analysis tasks ('deterministic query-aware retrieval') and mentions optional embedding enhancement, but lacks explicit guidance on when to use this tool versus alternatives like 'ast_grep_search' or 'local_debug_context'. It states 'completely offline with zero external dependencies' which provides some context but not clear when/when-not rules.
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 effectively communicates that the tool performs read-only analysis ('Get quick AST-based summary') and mentions path handling behavior, but it doesn't cover potential limitations like file size constraints, supported file types, error conditions, or performance characteristics. The description adds useful context but leaves significant behavioral aspects unspecified.
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 efficiently structured in two sentences that each serve distinct purposes: the first states the core functionality, and the second clarifies path handling. There's no redundant information, and the emoji adds visual distinction without compromising clarity. Every element earns its place in this compact description.
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 tool with 4 parameters, 100% schema coverage, but no annotations or output schema, the description provides adequate basic context about what the tool does and path handling. However, it doesn't compensate for the lack of output schema by describing return values or structure, and with no annotations, it should ideally provide more behavioral context about limitations or error handling to be fully complete.
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?
With 100% schema description coverage, the input schema already documents all four parameters thoroughly. The description adds minimal value beyond the schema by mentioning path flexibility ('Accepts absolute paths or relative paths'), but doesn't provide additional semantic context about parameter interactions or use cases. This meets the baseline expectation when schema coverage is complete.
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 specific action ('Get quick AST-based summary and key symbols') and resource ('any file'), distinguishing it from sibling tools like ast_grep_search or local_debug_context by emphasizing file analysis without external dependencies. It provides a precise verb+resource combination that makes the tool's function immediately understandable.
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 through 'Fast file analysis without external dependencies' and path flexibility, but it doesn't explicitly state when to use this tool versus alternatives like local_context or frontend_insights. There's no guidance on prerequisites or exclusion criteria, leaving the agent to infer appropriate scenarios from the tool's capabilities 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 full burden of behavioral disclosure. It effectively describes key behaviors: it parses logs, extracts contexts, uses tree-sitter and embeddings, processes errors separately, ranks matches, and returns a report. It also mentions performance ('Fast local analysis, ~1-3 seconds') and embedding features. However, it lacks details on error handling or specific limitations, preventing a perfect score.
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 well-structured with clear sections (e.g., 'When to use', 'What this does'), making it easy to scan. It is appropriately sized for a complex tool, but some sentences could be more concise (e.g., the detailed bullet points in 'What this does' are slightly verbose). Overall, it's front-loaded and efficient, with minimal waste.
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?
Given the tool's complexity (8 parameters, no annotations, no output schema), the description is mostly complete. It covers purpose, usage, behaviors, and performance. However, without an output schema, it only briefly mentions the output ('Returns comprehensive debug report'), lacking details on report structure or content, which is a minor gap for such a detailed tool.
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 8 parameters thoroughly. The description adds minimal parameter semantics beyond the schema, only briefly mentioning 'Input: Error logs or stack traces as text' and 'Output: Structured debug context report,' which are redundant with schema details. Thus, it meets the baseline of 3 without adding significant value.
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: 'Gather comprehensive debug context from error logs and codebase analysis with focused embedding enhancement.' It specifies the verb ('gather'), resource ('debug context'), and method ('from error logs and codebase analysis'), distinguishing it from sibling tools like 'local_context' or 'local_file_summary' which lack the debugging focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes an explicit 'When to use' section with four bullet points detailing specific scenarios (e.g., 'When you have error logs, stack traces, or console output to analyze'), and it mentions using this tool 'Before using AI debugging tools to get structured context,' providing clear guidance on when to use it versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and excels at disclosing behavioral traits. It specifies performance characteristics (120-second timeout, fast Rust-based execution), automatic behaviors (respects .gitignore files), validation behavior (comprehensive pattern validation with helpful error messages), and operational constraints (multi-language support, pattern syntax rules).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is excessively long (over 1000 words) with redundant sections. While well-structured with headings, it includes unnecessary content like CLI usage instructions, extensive pattern examples, and performance tips that could be condensed. Many sentences don't earn their place for a tool description aimed at AI agents.
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?
Given the tool's complexity (12 parameters, no output schema, no annotations), the description provides comprehensive context. It covers purpose, usage, behavioral traits, parameter guidance, examples, and edge cases. The only gap is output format details, but with no output schema, the description adequately compensates for all other aspects.
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?
With 100% schema description coverage, the baseline is 3, but the description adds significant value by explaining parameter relationships and practical usage. It clarifies that 'filePattern' is 'RECOMMENDED for large projects' and provides examples of how parameters work together (e.g., pattern syntax with wildcards, language selection). However, it doesn't fully explain all 12 parameters' interactions.
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 performs 'structural code search using ast-grep's pattern matching capabilities' and distinguishes it from text-based search. It specifies the verb ('search'), resource ('code'), and unique approach ('structural pattern matching'), making it distinct from any sibling tools.
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 provides clear context for when to use this tool (structural code search vs. text-based search) and includes 'Use Cases' section with specific applications. However, it doesn't explicitly state when NOT to use it or mention alternatives among sibling tools, though the structural vs. text distinction is implied.
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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