Simple Snowflake MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation2/5
There is significant overlap between tools, particularly 'execute-query' and 'execute-snowflake-sql' which appear to serve nearly identical purposes, both executing SQL queries on Snowflake. Additionally, 'describe-view' and 'query-view' both target views but with different outputs, which could cause confusion about when to use each. This ambiguity makes it difficult for an agent to reliably select the correct tool without careful examination of descriptions.
Naming Consistency3/5
The naming is mixed with some consistency issues. Most tools use a verb-noun pattern (e.g., 'list-databases', 'describe-view'), but there are deviations like 'execute-snowflake-sql' which includes the platform name, unlike the simpler 'execute-query'. The use of hyphens is consistent, but the verb choices and specificity vary, leading to a readable but not fully predictable naming scheme.
Tool Count4/5
With 7 tools, the count is reasonable for a Snowflake database interaction server, covering key operations like listing resources, executing queries, and managing views. It is slightly on the lower side but still well-scoped for basic database tasks, with each tool having a clear role except for the overlapping execution tools.
Completeness3/5
The tool set covers core read and list operations for Snowflake, including databases, warehouses, and views, but has notable gaps. There is no support for write operations (e.g., INSERT, UPDATE, DELETE) or schema modifications, which limits the server to querying and describing existing data. While agents can perform basic exploration, the lack of data manipulation tools makes the surface incomplete for full database management.
Average 2.9/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 24 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
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool returns 'markdown result' which gives some context about output format, but fails to describe important behavioral aspects like whether this is a read-only operation, what permissions are required, how errors are handled, or any rate limits. The description is minimal and leaves critical behavioral traits unspecified.
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 extremely concise at just 10 words in a single sentence. It's front-loaded with the core purpose ('Query a view') and efficiently mentions both the optional parameter and output format. While perhaps too brief for adequate completeness, it wastes no words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a database query tool with 4 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool actually does beyond 'query', doesn't describe the expected input formats for parameters, and provides minimal context about the operation's behavior or results. The mention of 'markdown result' is helpful but inadequate for the tool's complexity.
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?
With 0% schema description coverage and 4 parameters (3 required), the description provides minimal parameter information. It only mentions 'optional row limit' which corresponds to the 'limit' parameter, leaving the other 3 parameters (database, schema, view) completely unexplained. The description doesn't compensate for the schema's lack of descriptions, resulting in poor parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'Query a view' which indicates a read operation on a database view, but it's vague about what constitutes a 'query' versus just retrieving data. It mentions 'markdown result' which adds some specificity about output format, but doesn't clearly distinguish this from sibling tools like 'execute-query' or 'describe-view'.
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 like 'execute-query' or 'describe-view'. It mentions an 'optional row limit' which implies usage for limiting results, but offers no context about appropriate scenarios, prerequisites, or exclusions compared to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but doesn't mention whether this is a read-only operation, if it requires specific permissions, what the output format looks like, or any rate limits. This leaves significant gaps for a tool that presumably queries system metadata.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It is appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a tool that lists database views, the lack of annotations, no output schema, and 0% schema description coverage, the description is insufficient. It doesn't explain what information is returned, how results are structured, or any behavioral traits, leaving the agent with inadequate context for effective use.
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 description coverage is 0%, meaning the input schema provides no descriptions for the two required parameters. The description adds no semantic information about what 'database' and 'schema' represent, their expected formats, or examples, failing to compensate for the schema's lack of documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all views in a database and schema'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list-databases' or 'describe-view' beyond the resource type, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'list-databases', 'describe-view', or 'query-view'. It lacks context about prerequisites, such as whether the database and schema must exist or be accessible, or any exclusions for usage.
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 states this is a read operation ('Get details'), implying it's non-destructive, but doesn't disclose behavioral traits like authentication needs, rate limits, error conditions, or what happens if the view doesn't exist. For a tool with 3 required parameters and no annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Get details of a view') and adds specific details ('columns, SQL') without waste. Every word earns its place, making it appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 required parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return values (e.g., structure of details), error handling, or parameter semantics. The agent lacks sufficient context to use this tool effectively beyond its basic purpose.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the schema provides no parameter descriptions. The tool description adds no information about the parameters (database, schema, view), such as their formats, examples, or relationships. With 3 undocumented parameters, the description fails to compensate for the schema's lack of documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get details') and resource ('a view'), specifying what details are retrieved (columns, SQL). It distinguishes from siblings like 'list-views' (which lists views) and 'query-view' (which queries view data). However, it doesn't explicitly contrast with 'execute-query' or 'execute-snowflake-sql', which are more general query 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites like needing an existing view, nor does it contrast with 'list-views' (for listing views) or 'query-view' (for querying view data). The agent must infer usage from the tool name and description alone.
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 full burden for behavioral disclosure. While 'Execute a SQL query' implies a write operation could occur, the description doesn't specify whether this tool supports read-only queries, requires specific permissions, has transaction implications, or includes any rate limits. It provides minimal behavioral context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that directly states the tool's function. There's zero wasted language, and it's front-loaded with the essential information. Every word earns its place in this minimal description.
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?
For a SQL execution tool with no annotations and no output schema, the description is inadequate. It doesn't explain what 'return the result' means - whether it's a data table, success/failure status, or something else. Given the complexity of database operations and the lack of structured metadata, the description should provide more complete context about behavior and outputs.
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 description coverage is 100%, with the single parameter 'sql' clearly documented as 'SQL query to execute'. The description adds no additional parameter semantics beyond what the schema already provides, so the baseline score of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Execute a SQL query') and target resource ('on Snowflake'), providing a specific verb+resource combination. However, it doesn't differentiate this tool from sibling tools like 'execute-query' or 'query-view', which appear to have similar functions.
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 about when to use this tool versus alternatives like 'execute-query' or 'query-view'. There's no mention of prerequisites, limitations, or appropriate contexts for selecting this specific SQL execution tool over others in the sibling set.
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 full burden for behavioral disclosure. It mentions 'available' warehouses but doesn't clarify what 'available' means (e.g., accessible to current user, currently running), nor does it describe output format, pagination, permissions required, or error conditions. This leaves significant gaps for a tool that presumably returns a list.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without any fluff or redundant information. It's appropriately sized for a zero-parameter list operation and front-loads the core 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?
For a list operation with no annotations and no output schema, the description is insufficient. It doesn't explain what information is returned about warehouses (e.g., names, statuses, sizes), how results are structured, or any limitations (e.g., max results). The lack of behavioral context makes it incomplete for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so the schema already fully documents the input structure. The description appropriately doesn't add parameter information since none exist, maintaining focus on the tool's purpose without unnecessary detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('available Data Warehouses (DWH) on Snowflake'), making the purpose unambiguous. It doesn't explicitly differentiate from sibling tools like 'list-databases' or 'list-views', but the resource specificity provides implicit distinction.
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 like 'list-databases' or 'list-views', nor does it mention any prerequisites or context for usage. It simply states what the tool does without addressing when it's appropriate.
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 mentions 'accessible' databases, hinting at permission-based filtering, but lacks details on output format, pagination, error handling, or any constraints like rate limits 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality ('List all accessible Snowflake databases'). There is no wasted verbiage or redundancy, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It lacks details on behavioral aspects like output format or error conditions, which are important for a list operation even without complex inputs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description appropriately doesn't add unnecessary param details, earning a baseline score of 4 for zero-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and resource ('all accessible Snowflake databases'), making the tool's purpose immediately understandable. It doesn't differentiate from sibling tools like 'list-views' or 'list-snowflake-warehouses', but the scope is well-defined.
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 'list-views' or 'list-snowflake-warehouses'. The description only states what it does without indicating context or prerequisites for usage.
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 full burden. It discloses key behavioral traits: read-only mode by default, allowed query types, and result format. However, it doesn't mention important aspects like error handling, execution time limits, authentication requirements, or whether queries are logged/audited. For a SQL execution tool with zero annotation coverage, this leaves significant 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 concise with two main clauses that convey essential information. However, it could be more front-loaded by starting with the core purpose before detailing the read_only parameter behavior. The parenthetical about query types interrupts the flow slightly.
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 SQL execution tool with no annotations and no output schema, the description provides basic context about allowed operations and output format. However, it doesn't address critical aspects like what happens with non-SELECT queries when read_only=true, how results are structured beyond 'markdown format', or error conditions. Given the complexity of SQL execution, more completeness would be expected.
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 both parameters thoroughly. The description adds minimal value beyond the schema - it mentions the 'read_only' parameter's effect on query types but doesn't provide additional semantic context about parameter interactions or usage patterns. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'execute' and resource 'SQL query', specifying the types of queries allowed (SELECT, SHOW, DESCRIBE, EXPLAIN, WITH) and the output format. However, it doesn't explicitly differentiate from sibling tools like 'execute-snowflake-sql' or 'query-view', which appear to be similar query execution tools.
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 by mentioning 'read-only mode' and listing allowed query types, but doesn't provide explicit guidance on when to use this tool versus alternatives like 'execute-snowflake-sql' or 'query-view'. It mentions the 'read_only' parameter behavior but doesn't clarify when to set it to false versus using other tools.
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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