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FDEP MCP Server

FDEP MCP Server

by juspay
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

execute_query

Execute SQL queries to analyze Haskell codebases by searching modules, functions, types, and imports with customizable filters for enterprise-scale static code analysis.

Instructions

Execute a basic SQL query on the code 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": {
      "query_type": "<query_type>"
    },
    "name": "execute_query"
  }
}

Input Schema

NameRequiredDescriptionDefault
query_typeYesType of query to execute
filtersNoFilters to apply to the query

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It states the tool executes queries but doesn't disclose behavioral traits such as whether it's read-only or mutating, what permissions are required, potential rate limits, or what happens on errors. The mention of 'basic SQL query' hints at limitations but doesn't specify them.

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 states the core purpose without unnecessary words. It's front-loaded and wastes no space, 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 no annotations and no output schema, the description is incomplete for a tool that executes queries. It doesn't explain what 'basic' means, what the output looks like, or any constraints or side effects. For a query execution tool with multiple siblings, this leaves significant gaps in understanding.

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 fully documents parameters. The description adds no meaning beyond the schema—it doesn't explain what 'basic SQL query' entails in terms of allowed query types or filter usage. With high schema coverage, the baseline score of 3 is appropriate.

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') and target ('basic SQL query on the code database'), which distinguishes it from sibling tools like 'execute_advanced_query' or 'execute_custom_query'. However, it doesn't specify what constitutes a 'basic' query versus advanced/custom alternatives, leaving some ambiguity.

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_advanced_query' or 'execute_custom_query'. It mentions 'basic SQL query' but doesn't define what makes a query basic or when to choose this over other query-related tools in the sibling list.

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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