Ferret MCP

by fabdendev

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Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool has a clearly distinct purpose: scan provides an overview, dependencies extracts dependency info, architecture analyzes layers, patterns detects conventions, api_surface extracts API endpoints, ask allows Q&A, deep produces a deep analysis report, and full_extraction combines all. No two tools have overlapping functionality.
Naming Consistency4/5
Most tool names follow a consistent pattern of lowercase nouns with underscores (e.g., scan, dependencies, architecture, patterns, api_surface, full_extraction). However, 'ask' and 'deep' are verbs rather than nouns, slightly breaking the pattern, but they are still clear and descriptive.
Tool Count5/5
With 8 tools, the server is well-scoped for comprehensive codebase analysis. Each tool covers a distinct aspect, and the count is neither too few to be insufficient nor too many to be overwhelming.
Completeness4/5
The tool set covers essential aspects of repository analysis: overview, dependencies, architecture, patterns, API surface, AI-powered Q&A, deep analysis, and a combined extraction. While minor gaps like test coverage analysis might exist, the surface is strong and covers the stated purpose well.
Average 3.6/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on April 3, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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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 behavioral traits. It states 'scan a repository' but does not indicate if the operation is read-only, if network access is required, or any side effects. The nature of 'overview' is vague and does not reveal potential limitations or prerequisites.
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 short and front-loaded with the purpose. It uses two sentences to convey the core function and parameter. However, it could be more structured (e.g., bullet points) but remains efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter and an output schema exists, the description adequately covers the input but lacks details about the output format or behavioral constraints. It does not explain what happens if the path is invalid or if the repository is large.
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?
Schema coverage is 0%, meaning the description must compensate. It adds meaning to the path parameter by specifying it should be an 'absolute path to the repository root directory', which is useful beyond the schema's simple 'string' type.
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 that the tool scans a repository and returns an overview including languages, file structure, entry points, and config files. It specifies verb and resource but does not differentiate itself from sibling tools like full_extraction or deep, which may have overlapping functionality.
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 the sibling tools (api_surface, architecture, deep, etc.). No context for when to choose scan over alternatives or when not to use it.
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 only states the tool's function without disclosing any behavioral traits such as permissions needed, side effects, or limitations. For a tool with no annotations, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences with an Args line. It front-loads the purpose and provides necessary parameter detail without waste. Every sentence is informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one parameter and an output schema (which likely documents return values), the description covers the essential purpose and parameter semantics. However, it lacks comparison to sibling tools and does not explain the output beyond what the schema might provide. It is minimally complete but could be improved.
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 single parameter 'path' is described as 'Absolute path to the repository root directory', which adds meaning beyond the schema (which only specifies type). The description clarifies the expected format and scope. Schema coverage is 0%, so description compensates well.
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?
Description clearly states it analyzes repository architecture, including layers, patterns (with examples), and module breakdown. While it distinguishes from some siblings like 'api_surface' or 'dependencies', it does not explicitly differentiate from closely related sibling 'patterns', which could be seen as overlapping.
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 'patterns' or 'full_extraction'. It does not specify prerequisites, scenarios where it is preferred, or when to avoid it. The usage context is only implied by the description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that an LLM is invoked, uses specific data sources, and produces a report. However, it does not mention whether the tool is read-only, performance considerations, or if the operation may affect the repository. This is adequate but not comprehensive.
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 a purpose sentence, behavioral details, configuration requirements, and parameter definition. No redundant sentences; it is concise but informative.
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 (AI analysis), the description covers prerequisites and output type but lacks clarity on whether modifications are made. The output schema exists, so return values are not needed, but behavioral completeness could be enhanced by stating non-destructive nature.
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?
There is only one parameter 'path', and the description explains it as 'Absolute path to the repository root directory', adding meaning beyond the schema's type-only definition. Since schema_description_coverage is 0%, this compensation is valuable and clear.
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 specifies the tool's output as a 'comprehensive Knowledge Extraction Report' and explains it uses an LLM to analyze static extraction data and file contents for architectural analysis. This clearly states what the tool does, distinguishing it from siblings like 'full_extraction' which likely performs raw extraction rather than analysis.
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 mentions prerequisites (API key or local LLM) but provides no guidance on when to use this tool versus alternatives like 'ask' or 'scan'. There is no explicit context for selection, making it harder for an agent to decide.
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 provided, so the description must disclose behavior. It only states the purpose and one parameter, with no mention of side effects, permissions, or output nature.
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?
Extremely concise: one sentence for purpose followed by parameter description. No wasted words, front-loaded.
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?
Adequate for a simple tool with one parameter and an output schema (so return values not needed). However, it lacks context relative to siblings and does not specify when to prefer this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'path' is clearly described as an absolute path to the repository root, adding meaning beyond the schema's type string. Schema has 0% coverage, so description compensates fully.
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 it detects code patterns and conventions, listing specific types (design patterns, naming, testing, error handling, config). However, it does not differentiate from sibling tools like scan or deep.
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. The description lacks explicit context for usage 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, and the description only states what is extracted. It does not disclose any behavioral traits such as whether the tool modifies files, requires network access, or has side effects. The bare description leaves the agent uninformed 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?
The description is extremely concise (two sentences) with no wasted words. The main action is presented first, followed by parameter explanation. Every sentence adds necessary 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 simple extraction tool with one parameter and an output schema, the description covers the essential purpose and parameter semantics. It could mention the return format or highlight that it finds API definitions, but completeness is adequate given the presence of an output schema.
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 zero description coverage for the single parameter 'path'. The description adds 'Absolute path to the repository root directory', which clarifies the parameter's meaning and usage. This compensates well for the sparse schema.
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 'Extract the complete API surface' and lists specific API types (REST, MCP, CLI, GraphQL, gRPC, public exports). This provides a specific verb-resource combination and distinguishes it from sibling tools like full_extraction or patterns.
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. No context about prerequisites, limitations, or when to prefer sibling tools like full_extraction or patterns.
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. It only describes what it does (run analyses and produce a report) without disclosing behavioral traits such as execution time, resource usage, side effects, or error cases. This lacks sufficient transparency for an AI agent to gauge implications.
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 concise and front-loaded, stating the main purpose first, then listing sub-analyses in a bullet-like format. It could be more structured, but every sentence adds value without unnecessary 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 presence of an output schema, the description does not need to explain return values. It lists the analyses combined, which is useful. However, it lacks any prerequisites, limitations, or typical usage patterns that would make it complete for a tool of this complexity.
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 description explicitly defines the 'path' parameter as 'Absolute path to the repository root directory,' adding clear meaning beyond the schema's simple string type. Since schema description coverage is 0% but there is only one parameter, the description adequately compensates.
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 it runs ALL analyses and returns a comprehensive report, listing the individual analyses (scan, dependencies, architecture, patterns, API surface) it combines. It distinguishes itself from sibling tools by indicating it is a composite of them for complete repo understanding.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says 'Use this for complete repo understanding,' implying when to choose this over individual sibling tools. However, it does not explicitly state when not to use it or provide precise alternatives, though the list of sibling tools is implicitly the alternative.
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 must convey all behavioral traits. It mentions extracting dependencies and the need for a path, but does not disclose whether the tool is read-only, has permissions requirements, or any side effects. The description is adequate but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: one sentence followed by a clear argument listing. It front-loads the purpose and wastes no words.
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?
The tool has output schema (signal true), so the description need not detail return values. It explains the scope of extraction (external packages, internal import graph, core modules), which is sufficient for a tool with one parameter.
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?
Schema description coverage is 0%, but the description adds 'Absolute path to the repository root directory' for the path parameter, providing essential context beyond the schema's type and required flag.
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 'Extract all dependencies — external packages and internal import graph with core modules,' which provides a specific verb and resource. This differentiates it from sibling tools like api_surface or architecture that focus on other aspects.
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 explains what the tool does but does not provide guidance on when to use it versus alternatives. No exclusions or explicit when-to-use hints are given, leaving the agent to infer context.
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. It discloses that the tool uses AI, requires an API key, and processes static analysis data. However, it does not detail error behavior or results format, missing a chance for full 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 concise and well-structured: first sentence states purpose, then caveats and requirements, followed by parameter explanations. Every sentence adds value without redundancy.
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 simplicity (2 parameters) and existing output schema, the description adequately covers usage and context. It only lacks explicit information about return values, but the presence of an output schema mitigates this.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must add meaning. It clearly defines 'path' as the absolute path to the repository root and 'question' as the query about the codebase, effectively compensating for the schema's lack of description.
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: 'Ask a specific question about a repository, answered by AI with full codebase context.' It uses a specific verb ('ask') and resource ('repository'), and the task is distinct from sibling tools like scan or dependencies.
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 explains what the tool does but does not explicitly advise when to use it instead of siblings or provide exclusion criteria. The requirement for FERRET_LLM_API_KEY is a notable prerequisite, but no comparative guidance is given.
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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