Skip to main content
Glama
yuki9541134

Redash MCP Server

by yuki9541134
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: execute_query_and_wait runs queries, get_data_source retrieves details for a specific source, and list_data_sources enumerates all sources. There is no overlap or ambiguity between these operations.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern (execute_query_and_wait, get_data_source, list_data_sources) with clear, descriptive names that align well with their functions.

    Tool Count2/5

    With only 3 tools, the server feels under-scoped for a Redash-like data querying and visualization platform. Key operations like creating/updating queries, managing dashboards, or fetching query results are missing, making the set too thin for typical agent workflows.

    Completeness2/5

    The tool surface is severely incomplete for a Redash server. While it covers basic data source listing and query execution, it lacks essential CRUD operations for queries, dashboards, and visualizations, as well as lifecycle management tools, which will likely cause agent failures in complex tasks.

  • Average 2.9/5 across 3 of 3 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
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 'wait for the results', implying synchronous behavior, but fails to address critical aspects like error handling, timeout behavior (despite a 'max_age' parameter), authentication requirements, or rate limits. For a tool that executes queries, this leaves significant gaps in understanding its 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—a single sentence that directly states the tool's function without any fluff. It's front-loaded with the core action and efficiently communicates the basic purpose. Every word earns its place, 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 executing SQL queries (which involves data sources, query syntax, and result handling), the description is incomplete. With no annotations, no output schema, and 0% schema description coverage, it fails to provide necessary context about inputs, outputs, or behavioral expectations. The agent would struggle to use this tool effectively without additional information.

    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%, meaning none of the three parameters (data_source_id, query, max_age) are documented in the schema. The description adds no semantic information about these parameters—it doesn't explain what 'data_source_id' refers to, what format 'query' should be in, or what 'max_age' represents. This forces the agent to guess parameter meanings, which is inadequate.

    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 wait for the results'), which is specific and unambiguous. It distinguishes itself from sibling tools like 'get_data_source' and 'list_data_sources' by focusing on query execution rather than data source retrieval. However, it doesn't explicitly mention what resource it acts upon beyond 'SQL query', 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. It doesn't mention prerequisites like needing a valid data source ID, nor does it differentiate from potential other query execution tools. Without any context on when or why to choose this tool, the agent lacks decision-making support.

    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 states the tool retrieves details, implying a read-only operation, but doesn't cover aspects like authentication needs, rate limits, error handling, or what 'details' include. For a tool with zero annotation coverage, 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 a single, clear sentence with no wasted words. It's appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration.

    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 (a read operation with one parameter), no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't explain return values, error cases, or parameter semantics, leaving significant gaps for the 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 schema description coverage is 0%, and the description adds no information about the parameter 'data_source_id'. It doesn't explain what a data source ID is, its format, or where to find it. With one undocumented parameter, the description fails to compensate for the schema's lack of details.

    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') and resource ('details about a specific data source'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'list_data_sources', which might retrieve multiple data sources rather than a specific one. This 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. It doesn't mention sibling tools like 'list_data_sources' or 'execute_query_and_wait', nor does it specify prerequisites or contexts for usage. This leaves the agent without explicit direction.

    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 states the action but doesn't describe traits like whether this is a read-only operation, if it requires authentication, rate limits, pagination, or the format of returned data. For a tool with zero annotation coverage, this is a significant gap in 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 a single, efficient sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place by conveying essential information without redundancy.

    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 output schema, the description is incomplete. It doesn't address behavioral aspects like safety, performance, or return format, which are crucial for an agent to use the tool effectively. For a tool with no structured data support, the description should compensate more.

    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 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here, but it could have mentioned if there are implicit filters or options, though not required. Baseline is 4 for zero 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 verb 'List' and the resource 'all available data sources', which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_data_source', which might retrieve a single data source, leaving some room for improvement in sibling 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 'get_data_source' or 'execute_query_and_wait'. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name alone.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

mcp-redash MCP server

Copy to your README.md:

Score Badge

mcp-redash MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/yuki9541134/mcp-redash'

If you have feedback or need assistance with the MCP directory API, please join our Discord server