wsqlite-mcp

by wisrovi

Overview Schema Related Servers Score Discussions

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes: validation, migration generation, pattern search, scaffolding deployment, and reference. However, 'get_wsqlite_architect_blueprints' and 'get_wsqlite_architect_manual' have overlapping descriptions as reference materials, causing slight ambiguity.
Naming Consistency4/5
Tool names follow a consistent verb_noun pattern in snake_case (validate, generate, search, deploy, get). The 'generate_migration_from_models' is slightly longer than others, and the two 'get_wsqlite_architect_*' tools share a prefix, but overall the naming is predictable and readable.
Tool Count5/5
Six tools is well-scoped for a development-oriented server, covering validation, migration, search, scaffolding, and documentation. Each tool fills a clear role without redundancy.
Completeness4/5
The toolset covers the core workflow of model validation and migration generation, plus project scaffolding and reference lookup. Missing tools for applying migrations or direct model CRUD, but these gaps are not critical for a development assistant.
Average 3.2/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 7 commits in the last 12 weeks
- No stable releases found
- 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 provided, the description carries the full burden of behavioral disclosure. It states that a project structure is deployed but does not reveal side effects (e.g., filesystem writes, overwrite behavior), required permissions, or what 'wisrovi standards' entail. This is insufficient for a mutation tool.
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, front-loaded sentence with no wasted words. It is appropriately brief for a high-level summary, though it sacrifices informational depth for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and a schema with zero parameter descriptions, the description is incomplete. The output schema may document return values, but the description does not explain what structure is created, how parameters affect the output, or what side effects occur. For a scaffolding tool with three parameters, this is inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has three parameters (target_dir, project_name, scaffold_type) with no descriptions, and the tool description mentions none of them. Schema description coverage is 0%, and the description does not compensate by explaining the roles or constraints of these 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 uses a specific verb ('Deploys') and resource ('WSQLite project structure'), clearly distinguishing it from sibling tools like validate_model_schema and generate_migration_from_models. The qualifier 'following wisrovi standards' adds context without obscuring the core purpose.
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. It does not mention prerequisites, exclusions, or when a different scaffolding or project setup tool would be more appropriate. The usage context is only implied by the verb 'Deploys'.
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 must fully disclose behavior, but it ambiguously says 'Generate a migration file' while also 'Returns the migration code as a string.' It does not clarify whether it writes to disk or only returns the string, nor does it mention side effects or error behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main purpose and concise expansion. Every sentence earns its place with no filler or repetition.
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?
Despite having an output schema, the description fails to cover key contextual aspects: parameter semantics are missing, side effects are ambiguous, and no usage guidance is provided. The agent would struggle to invoke the tool correctly, especially regarding db_path and migration_name.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage and the description names no parameters. It does not explain that models_code is the required Pydantic code, nor does it mention db_path or migration_name, leaving the agent without essential parameter meaning.
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 generates a migration file from Pydantic model definitions, and further details that it parses models and creates table-creating migration code. This is a specific verb+resource and is distinct from sibling tools like validate_model_schema or deploy_wsqlite_scaffolding.
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, nor any prerequisites or expected context. It does not mention when-not-to-use or reference sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing behavioral traits. It only says the tool is a 'manual' but does not state whether it returns static content, requires no side effects, or how it behaves. There is no mention of read-only nature, authentication, or output format. 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 sentence that conveys the essential purpose without any fluff. It is front-loaded and concise, earning every word it uses. No unnecessary details or repetitions.
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 (no params, likely static content) and has an output schema. However, the description lacks context such as what kind of manual content is included, how it relates to the other architect tools, or what to expect in the output. It is minimally viable but leaves room for clarifying its role within the toolkit.
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 schema has 100% coverage (empty properties). The baseline for 0 parameters is 4, and the description need not explain parameter semantics. It correctly implies there are no inputs to configure.
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 an 'Expert manual' for building high-performance SQLite-backed systems, which is specific to the 'wisrovi standard'. It distinguishes itself from sibling tools like get_wsqlite_architect_blueprints by positioning itself as a manual rather than blueprints. The verb is implicit (get) but the resource is clearly a manual.
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 its siblings (e.g., search_wsqlite_pattern, deploy_wsqlite_scaffolding). The description does not mention use cases, prerequisites, or situations where this manual would be preferred. It is implied that usage is for general reference, but no explicit direction is given.
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 only indicates a 'search' action, implying a read-only operation, but does not explicitly state side effects, result ordering, or any limitations. There is no information about authentication, rate limits, or what qualifies as a 'pattern,' leaving significant behavioral ambiguity.
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 with no filler words. It is front-loaded with the action verb and clearly communicates the core purpose. Every word contributes value, making it an appropriate length for the tool's simplicity.
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, one required parameter, and the presence of an output schema (which reduces the need to explain return values), the description is minimally sufficient. However, it does not cover usage alternatives or parameter details, and relies heavily on the tool's name and sibling context. It is adequate but leaves noticeable gaps in guidance for an AI agent.
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 input schema provides a single 'query' parameter with no description, giving 0% coverage. The description implies that 'query' is the search term for patterns, which adds some meaning beyond the schema, but it does not clarify expected format, matching behavior, or any constraints. This is insufficient compensation for the lack of schema documentation, so the parameter semantics remain mostly inferred.
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 verb 'Search' and specifies the resource: 'production-ready SQLite patterns in official catalog.' This is distinct from sibling tools like validate_model_schema or generate_migration_from_models, which focus on different actions and entities. The scope is unambiguous.
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 its usage for searching SQLite patterns but does not explicitly state when to use it over alternatives or mention any exclusions. There is no reference to sibling tools or conditions, leaving the guidance implicit rather than explicit. This meets the 'implied usage' level, not the 'clear context' level.
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 the full burden of behavioral disclosure. It only describes the content (read/write/update examples) but does not reveal how the tool behaves, such as return format, size, or whether it is a static document or dynamically generated. This leaves the agent uncertain about what to expect when invoking it.
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?
A single, well-structured sentence that immediately communicates the tool's purpose and scope. Every word contributes to the meaning, and it is appropriately front-loaded with 'Complete reference'.
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 no-parameter retrieval tool with an existing output schema, the description is largely sufficient. It specifies the content and coverage, and the output schema presumably details return values. Minor gap: it does not clarify the relationship to the sibling 'get_wsqlite_architect_manual', which could be important context.
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 schema coverage is trivially 100%. The description adds contextual value by specifying the nature of the content (complete reference with examples), which helps set expectations despite not requiring parameters.
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 a complete reference with read/write/update examples for WSQLite features. The 'get' prefix implies retrieval, and the scope is well-defined. However, it does not distinguish itself from the sibling tool 'get_wsqlite_architect_manual', which may also serve as a reference.
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?
It implies usage when a comprehensive reference is needed, but does not explicitly mention when not to use it or alternatives like 'search_wsqlite_pattern'. There is no exclusionary guidance to differentiate from the sibling 'get_wsqlite_architect_manual', leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the specific validation checks performed, which is useful. However, it does not mention whether the tool is read-only, what happens on failure, or any side effects. This leaves some behavioral assumptions unstated.
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. It opens with a clear one-sentence summary, followed by a succinct bulleted list of checks. Every sentence adds value and there is no redundancy or filler.
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 description is largely complete given the tool's complexity: one parameter, an output schema, and no annotations. It covers the purpose and key checks, and the output schema handles return-value details. Missing only a few behavioral notes (e.g., non-destructive guarantee) and explicit usage guidance, which are already penalized in other dimensions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no description for the single parameter 'model_code', and schema coverage is 0%. The description adds that it is a 'Pydantic model definition', which clarifies the parameter's role, but it does not specify the exact format (e.g., source code string, file path, JSON). This partial compensation is insufficient to fully overcome the schema gap.
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: 'Validate a Pydantic model definition for WSQLite compatibility.' It uses a specific verb and resource, and the bulleted list of checks further specifies the scope. This clearly distinguishes it from sibling tools like generate_migration_from_models or deploy_wsqlite_scaffolding.
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: you would use this tool to validate a model before relying on it in WSQLite. However, it does not explicitly mention when to use it relative to siblings, nor does it state exclusions or alternatives. The guidance is implicit rather than explicit.
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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