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BlazeSQL MCP Server

BlazeSQL MCP Server

by arjshiv
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

blazesql_query

Execute natural language queries on BlazeSQL databases by specifying the database ID and query request for quick, intuitive data retrieval and analysis.

Instructions

Executes a natural language query against a specified BlazeSQL database.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "db_id": "<db_id>",
      "natural_language_request": "<natural_language_request>"
    },
    "name": "blazesql_query"
  }
}

Input Schema

NameRequiredDescriptionDefault
db_idYesThe ID of the BlazeSQL database connection to query.
natural_language_requestYesThe query expressed in natural language (e.g., 'show me total users per city').

Output Schema

NameRequiredDescriptionDefault
queryYesThe SQL query generated and executed by BlazeSQL.
data_resultYesThe structured data returned by the query, as a map of column names to value arrays.
agent_responseYesNatural language explanation of the results from BlazeSQL.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
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 executes queries but doesn't describe traits like error handling, performance implications, authentication needs, or rate limits. For a query execution 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 that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized, making it easy to understand quickly. Every part of the sentence contributes to clarifying the tool's function.

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 an output schema (which handles return values), 100% schema coverage, and no annotations, the description is minimally complete. It covers the basic purpose but lacks behavioral context and usage guidelines. For a query tool with no annotations, it should do more to compensate, but the output schema mitigates some 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?

The schema description coverage is 100%, so the schema already documents both parameters (db_id and natural_language_request) with clear descriptions. The description adds no additional meaning beyond what the schema provides, such as examples or constraints. Baseline 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 tool's purpose with a specific verb ('executes') and resource ('natural language query against a specified BlazeSQL database'). It distinguishes what it does (execute natural language queries) from potential alternatives (like SQL queries), though without sibling tools, differentiation isn't needed. However, it could be more specific about the type of queries or results.

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, prerequisites, or exclusions. It mentions the tool's function but lacks context on appropriate scenarios, such as when natural language queries are supported or if there are limitations. With no sibling tools, this gap is less critical but still present.

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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