How do I use Trade Surveillance Support MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Trade Surveillance Support MCP Server I got an email about a settlement discrepancy for trade ID 789012 - can you help investigate?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Trade Surveillance Support MCP Server

by vic3custodio

Overview Schema Related Servers Score Discussions

Python

Local

Trade Surveillance Support MCP Server

An MCP (Model Context Protocol) server designed to automate trade surveillance support workflows by integrating with your existing SQL configs and Java code repositories.

Overview

This MCP server enables you to:

Parse user inquiry emails - Extract key information from support emails automatically
Search SQL config files - Find relevant database queries and configurations
Search Java code - Locate report generation classes and methods
Execute Java reports - Run Java processes to generate data and reports
Generate response summaries - Create comprehensive responses for user inquiries

Related MCP server: JoyCode MCP Server - FOP Workflow Assistant

Installation

Prerequisites

Python 3.10 or higher
uv package manager (recommended) or pip

Install with uv (recommended)

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install the MCP server
uv pip install -e .

Install with pip

pip install -e .

Configuration

Setting up with VS Code

Open VS Code Settings
Search for "MCP"
Add a new MCP server configuration:

{
  "mcp.servers": {
    "trade-surveillance": {
      "type": "stdio",
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/mcp_test_2",
        "run",
        "trade-surveillance-mcp"
      ]
    }
  }
}

Setting up with Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "trade-surveillance": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/mcp_test_2",
        "run",
        "trade-surveillance-mcp"
      ]
    }
  }
}

macOS/Linux: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

🎯 Keyword-Based Search (No File Paths!)

Instead of searching by file paths, this MCP server uses metadata annotations so Copilot can find files by what they do:

Example SQL annotation:

-- @keywords: trade, settlement, daily, reconciliation
-- @type: compliance_report
-- @description: Daily trade settlement reconciliation report

Example Java annotation:

/**
 * @keywords settlement, report, generator
 * @type report_generator
 * @description Generates daily settlement reports
 */

Result: Copilot searches by keywords like "settlement report" instead of file paths!

📚 Documentation:

QUICKSTART.md - Get started in 5 minutes
METADATA_GUIDE.md - Complete annotation guide
examples/ - Annotated SQL and Java examples

Usage

With GitHub Copilot in VS Code

Open a chat with Copilot
Paste a user inquiry email
Copilot will automatically use the MCP tools to:
- Parse the email
- Search for relevant configs by keywords (not file paths!)
- Search for Java code by keywords
- Execute reports
- Generate a response

Example prompt:

I received this email from a user:

[Paste email content here]

Can you help me investigate and generate the necessary reports?

Available Tools

1. `parse_email_inquiry`

Extracts key information from user inquiry emails including inquiry type, trade IDs, time periods, and priority.

2. `search_sql_configs` ⭐ Metadata-based search

Searches through your SQL configuration files by keywords instead of file paths. Files are searched using metadata annotations (see METADATA_GUIDE.md).

3. `search_java_code` ⭐ Metadata-based search

Locates Java classes and methods by keywords instead of file paths. Classes are found using javadoc annotations (see METADATA_GUIDE.md).

4. `execute_java_report`

Runs Java processes with the appropriate config files to generate reports.

5. `rebuild_metadata_index`

Rebuilds the search index by scanning all annotated SQL configs and Java files. Run this after adding new files or updating annotations.

6. `generate_response_summary`

Creates a comprehensive summary response for the user.

Project Structure

mcp_test_2/
├── trade_surveillance_mcp/
│   ├── __init__.py
│   └── server.py          # Main MCP server implementation
├── pyproject.toml          # Project dependencies
├── README.md
└── .github/
    └── copilot-instructions.md

Development

Running Locally

# Run the server directly
uv run trade-surveillance-mcp

# Or with Python
python -m trade_surveillance_mcp.server

Customization

You'll need to customize the server to work with your specific repository structure:

Update search paths - Modify config_directory and code_directory parameters
Implement email parsing - Add your email parsing logic in parse_email_inquiry
Add file search - Implement actual file searching in search_sql_configs and search_java_code
Configure Java execution - Add your Java classpath and execution logic in execute_java_report

Connecting to Your Repository

Point the MCP server to your actual config and code repositories:

# Example: Update default directories
@mcp.tool()
async def search_sql_configs(
    search_term: str,
    config_directory: str = "/path/to/your/sql/configs"
):
    # Your implementation

Next Steps

✅ MCP server is ready! - Restart VS Code to load it
📝 Annotate your files - Add metadata keywords to your SQL configs and Java code (QUICKSTART.md)
🔍 Build the index - Use rebuild_metadata_index tool in Copilot
⚙️ Customize paths - Update default directories in server.py to your repos
🎯 Test with Copilot - Paste a user email and let Copilot search by keywords!

Key Innovation: No More File Path Search! 🎉

Your SQL configs and Java files are now searchable by keywords:

Copilot finds files by what they do, not where they are
Search "settlement report" instead of remembering configs/reports/daily/settlement_v2.sql
See examples in examples/configs/ and examples/src/

Troubleshooting

Server not appearing in VS Code

Check the MCP server logs in VS Code Output panel
Verify the absolute path in configuration is correct
Ensure uv is installed and in PATH

Python version issues

Ensure Python 3.10+ is installed: python --version
Use uv for better environment management

Java execution errors

Verify Java is installed: java -version
Check classpath configuration
Ensure config files are accessible

Resources

License

MIT

Available Tools

6 tools

execute_java_reportA

Execute a Java unit test to generate a trade surveillance report.

This tool runs the unit test for the specified Java class with the given 
config file to generate the required report or data extract. Running tests
ensures the report generation is validated during execution.

Args:
    java_class: The fully qualified Java class name (e.g., "com.trade.SettlementReportGenerator")
    config_file: Path to the SQL config file to use
    output_directory: Directory where the report should be saved
    
Returns:
    A dictionary containing execution status, report path, and any errors

ParametersJSON Schema

Name	Required	Default
`java_class`	Yes
`config_file`	Yes
`output_directory`	No	./reports

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A3.9/5.0

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 of behavioral disclosure. It describes the tool as executing a unit test to generate a report, implying it performs a read/write operation (since it saves output) and validates execution. However, it lacks details on permissions, error handling, rate limits, or side effects. The description adds some context but is incomplete for a tool with mutation capabilities.

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 well-structured with a clear purpose statement, explanatory paragraph, and separate Args and Returns sections. It is appropriately sized and front-loaded, with no redundant sentences. Minor improvements could include tighter phrasing, but overall it is efficient and organized.

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?

Given the tool's complexity (3 parameters, no annotations, but has an output schema), the description is reasonably complete. It explains the purpose, parameters, and return value, and the output schema handles return details. However, for a tool that executes tests and generates reports, more behavioral context (e.g., execution environment, error modes) would enhance completeness.

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 schema description coverage is 0%, so the description must compensate. It provides a dedicated 'Args' section explaining each parameter's purpose (e.g., 'fully qualified Java class name,' 'Path to the SQL config file,' 'Directory where the report should be saved'), adding meaningful semantics beyond the bare schema. However, it does not specify formats or constraints for the config_file path or output_directory beyond examples.

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: 'Execute a Java unit test to generate a trade surveillance report.' It specifies the verb ('execute'), resource ('Java unit test'), and outcome ('generate a trade surveillance report'), distinguishing it from sibling tools like search_java_code or search_sql_configs. The first sentence provides a complete and specific purpose statement.

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 'trade surveillance report' and 'validated during execution,' but it does not explicitly state when to use this tool versus alternatives like search_java_code or generate_response_summary. There is no guidance on prerequisites, exclusions, or comparisons to sibling tools, leaving the agent to infer appropriate usage scenarios.

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

generate_response_summaryB

Generate a summary response for the user inquiry with all relevant information.

This tool combines all the gathered information into a clear, actionable
response that can be sent back to the user.

Args:
    parsed_email: The parsed email inquiry data
    config_files: List of config files that were used
    report_path: Path to the generated report file
    
Returns:
    A formatted summary string ready to send to the user

ParametersJSON Schema

Name	Required	Description	Default
`parsed_email`	Yes
`config_files`	Yes
`report_path`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

B3.1/5.0

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 describes the tool's function but lacks critical behavioral details: it doesn't mention whether this is a read-only or mutating operation, what permissions might be required, error handling, or performance characteristics. The description is functional but insufficient for a tool with complex inputs and output.

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 well-structured and appropriately sized: a purpose statement, elaboration, parameter details, and return value—all in a compact format. It's front-loaded with the core function. Minor verbosity in the elaboration sentence could be tightened, but overall it earns its place efficiently.

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 complexity (3 parameters with nested objects, no annotations, but with an output schema), the description is moderately complete. It covers parameters and return value at a high level, and the output schema handles return details. However, it lacks behavioral context and usage guidelines, leaving gaps for safe and effective tool invocation.

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?

Schema description coverage is 0%, so the description must compensate. It provides a clear 'Args' section that names and briefly describes all three parameters ('parsed_email', 'config_files', 'report_path'), adding meaningful context beyond the bare schema. However, it doesn't detail expected formats or constraints (e.g., what 'parsed_email' structure entails).

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: 'Generate a summary response for the user inquiry with all relevant information' and 'combines all the gathered information into a clear, actionable response'. It specifies the verb ('generate', 'combines') and resource ('summary response'), though it doesn't explicitly differentiate from sibling tools like 'execute_java_report' or 'parse_email_inquiry'.

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 mentions combining 'gathered information' but doesn't specify prerequisites, timing relative to other tools (e.g., after 'parse_email_inquiry'), or exclusions. This leaves the agent without context for tool selection among siblings.

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

parse_email_inquiryA

Parse a user inquiry email to extract key information for investigation.

This tool analyzes email content and extracts:
- Inquiry type (trade issue, report request, data verification, etc.)
- Related trade IDs or account numbers
- Time period of interest
- Priority level
- Required actions

Args:
    email_content: The full text content of the user's inquiry email
    
Returns:
    A dictionary containing parsed information including inquiry_type,
    trade_ids, time_period, priority, and suggested_actions

ParametersJSON Schema

Name	Required	Description	Default
`email_content`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.2/5.0

Behavior4/5

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 effectively describes what the tool does (analyzes email content to extract specific information) and outlines the return structure, though it lacks details on error handling, performance 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 appropriately sized and front-loaded, starting with a clear purpose statement followed by bullet points of extracted information and a concise specification of arguments and returns. Every sentence adds value without redundancy.

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?

Given the tool's moderate complexity (1 parameter, no annotations, but with an output schema), the description is largely complete: it explains the purpose, parameter semantics, and return values. The output schema reduces the need to detail return structure, though additional context like error cases or limitations could enhance completeness.

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?

Schema description coverage is 0%, so the description must compensate. It adds meaning by specifying that 'email_content' is 'the full text content of the user's inquiry email', clarifying the parameter's purpose beyond the schema's basic type and title. However, it does not detail format constraints or examples.

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 with a specific verb ('parse') and resource ('user inquiry email'), and distinguishes it from sibling tools by focusing on email analysis rather than report execution, summary generation, metadata rebuilding, or code/config searching.

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 ('for investigation') but does not explicitly state when to use this tool versus alternatives. No exclusions or prerequisites are mentioned, leaving the agent to infer appropriate scenarios based on the purpose alone.

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

rebuild_metadata_indexA

Rebuild the metadata index by scanning all SQL configs and Java files.

Use this tool when you've added new files or updated metadata annotations.
The index is automatically built on first search, but you can manually rebuild
it with this tool.

Args:
    config_directory: Path to the directory containing SQL config files
    code_directory: Path to the directory containing Java source files
    
Returns:
    A summary of the indexing operation

ParametersJSON Schema

Name	Required	Description	Default
`config_directory`	No		./configs
`code_directory`	No		./src

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.8/5.0

Behavior4/5

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

With no annotations provided, the description carries full burden and does well by explaining the tool's purpose, triggering conditions, and automatic vs manual behavior. It doesn't mention performance implications, error handling, or permissions, but provides solid operational context.

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?

Perfectly structured with purpose statement, usage guidelines, and parameter explanations in separate logical sections. Every sentence earns its place with no wasted words, and key information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (rebuilding indexes across file systems), no annotations, and the presence of an output schema (handling return values), the description provides complete operational context including purpose, when to use, parameter meanings, and return value summary.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by clearly explaining both parameters (config_directory for SQL config files, code_directory for Java source files) and their purposes, adding essential meaning beyond the bare schema.

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 specific action ('rebuild the metadata index') and resources involved ('scanning all SQL configs and Java files'), distinguishing it from sibling tools like search_java_code or search_sql_configs which only search rather than rebuild indexes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance is provided on when to use this tool ('when you've added new files or updated metadata annotations') and when not to use it ('automatically built on first search'), with clear context about manual vs automatic rebuilding.

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

search_java_codeA

Search for Java classes using metadata keywords instead of file paths.

This tool searches through indexed Java files by their javadoc metadata annotations.
Java classes should include metadata in javadoc comments like:

/**
 * @keywords trade, settlement, report_generator
 * @type report_engine
 * @description Generates daily settlement reports
 */

Args:
    search_keywords: Keywords to search for (e.g., "report generator", "trade processor")
    code_directory: Path to the directory containing Java source files (used for initial scan)
    
Returns:
    A dictionary containing matching Java files with their metadata and methods

ParametersJSON Schema

Name	Required	Description	Default
`search_keywords`	Yes
`code_directory`	No		./src

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.2/5.0

Behavior3/5

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 discloses that the tool searches 'indexed Java files' and uses 'javadoc metadata annotations', adding context about the search mechanism. However, it lacks details on behavioral traits such as performance (e.g., speed, limitations), error handling, or prerequisites (e.g., need for pre-indexed files), leaving gaps 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by explanatory details and a structured format with 'Args' and 'Returns' sections. Every sentence adds value, such as the javadoc example and parameter explanations, with no redundant information.

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?

Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is mostly complete. It covers purpose, usage context, parameter semantics, and return value overview. The output schema exists, so detailed return explanations are not needed, but it could improve by addressing behavioral aspects like indexing requirements or error cases.

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?

Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining 'search_keywords' with examples (e.g., 'report generator') and clarifies that 'code_directory' is 'used for initial scan', providing context beyond the schema's basic titles. However, it does not detail parameter constraints or formats (e.g., keyword syntax, directory validity), partially compensating for the low coverage.

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 specific action ('search for Java classes') and the unique mechanism ('using metadata keywords instead of file paths'), distinguishing it from potential file-based search tools. It explicitly mentions the resource (Java classes) and the method (javadoc metadata annotations), making the purpose unambiguous and distinct from siblings like 'search_sql_configs'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context on when to use this tool: for searching Java classes via metadata keywords rather than file paths. It implies usage for indexed Java files with javadoc metadata, but does not explicitly state when not to use it or name alternatives among siblings like 'rebuild_metadata_index' for indexing tasks.

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

search_sql_configsA

Search for SQL configuration files using metadata keywords instead of file paths.

This tool searches through indexed SQL config files by their metadata annotations.
Files should include metadata comments like:

-- @keywords: trade, transaction, daily_report
-- @type: compliance_check
-- @description: Daily trade reconciliation report

Args:
    search_keywords: Keywords to search for (e.g., "trade settlement", "compliance", "daily report")
    config_directory: Path to the directory containing SQL config files (used for initial scan)
    
Returns:
    A dictionary containing matching config files with their metadata

ParametersJSON Schema

Name	Required	Description	Default
`search_keywords`	Yes
`config_directory`	No		./configs

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations provided, the description carries full burden and does well by explaining the search mechanism (indexed files, metadata annotations), providing concrete examples of metadata format, and describing the return format. It doesn't mention performance characteristics, rate limits, or authentication needs, but covers the core behavior adequately.

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 efficiently structured with a clear purpose statement, usage explanation, metadata examples, and separate Args/Returns sections. Every sentence adds value, and information is well-organized without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity, no annotations, but with output schema present, the description provides complete context: clear purpose, usage guidance, behavioral details, parameter explanations, and return format description. The output schema handles return structure, so the description focuses appropriately on operational context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by explaining both parameters: 'search_keywords' gets examples and context about metadata matching, and 'config_directory' explains its purpose ('used for initial scan') with a default value. The description adds significant meaning beyond the bare schema.

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 with specific verb ('search') and resource ('SQL configuration files'), and distinguishes it from path-based searches by emphasizing metadata keyword searching. It explicitly differentiates from potential sibling tools by focusing on SQL config files rather than Java code or other resources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context about when to use this tool ('using metadata keywords instead of file paths') and includes examples of metadata annotations. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools, though the distinction from path-based searching is implied.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

6 tool updatesv1.0.0Jan 3, 2026
- Changedexecute_java_report1 field changed
  - addedInput schema / title
```
Added value: +"execute_java_reportArguments"
```
- Changedgenerate_response_summary1 field changed
  - addedInput schema / title
```
Added value: +"generate_response_summaryArguments"
```
- Changedparse_email_inquiry1 field changed
  - addedInput schema / title
```
Added value: +"parse_email_inquiryArguments"
```
- Changedrebuild_metadata_index1 field changed
  - addedInput schema / title
```
Added value: +"rebuild_metadata_indexArguments"
```
- Changedsearch_java_code1 field changed
  - addedInput schema / title
```
Added value: +"search_java_codeArguments"
```
- Changedsearch_sql_configs1 field changed
  - addedInput schema / title
```
Added value: +"search_sql_configsArguments"
```
6 tool updatesNov 17, 2025
- First observedexecute_java_report
- First observedgenerate_response_summary
- First observedparse_email_inquiry
- First observedrebuild_metadata_index
- First observedsearch_java_code
- First observedsearch_sql_configs

Install Server

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TDQS

A4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap. The tools cover different stages of a workflow: parse_email_inquiry for input analysis, search_sql_configs/search_java_code for resource discovery, execute_java_report for execution, generate_response_summary for output formatting, and rebuild_metadata_index for maintenance. An agent can easily distinguish between them based on their specific functions.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with snake_case naming. The verbs are clear and descriptive (execute, generate, parse, rebuild, search), and the nouns precisely indicate the target resource or action. There are no deviations in naming conventions across the six tools.

Tool Count5/5

Six tools is well-scoped for a trade surveillance support server. Each tool serves a distinct purpose in the workflow, from inquiry parsing to report generation and response formatting. The count is neither too thin nor bloated, providing comprehensive coverage without redundancy.

Completeness4/5

The toolset covers the core trade surveillance workflow effectively: parsing inquiries, searching for relevant configurations and code, executing reports, and generating responses. A minor gap exists in direct data manipulation or validation tools (e.g., validate_trade_data, update_config), but agents can work around this using the existing search and execution tools. The surface supports end-to-end processing without dead ends.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

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Source-linked SEC 13F research, quality checks, watchlists and audit trails for agents.
Finance Research & Data
1323
matterfactOAuth
com.matterfact
Investment research superagent: podcasts, SEC filings, and no-code research pipelines.
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2
radmail-mcp A
ai.radmail
Email OS for agents - real-inbox search, triage, commitments, and a verifiable BEC hard-stop.
Email Security
62
Raza SOR A
com.razarizwan
Raza Rizwan's record: ask how much of your workflow AI can automate; governed, cited answers.
Knowledge & Memory Search Documentation Access
41

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MCP directory API

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Trade Surveillance Support MCP Server

Overview

Installation

Prerequisites

Install with uv (recommended)

Install with pip

Configuration

Setting up with VS Code

Setting up with Claude Desktop

🎯 Keyword-Based Search (No File Paths!)

Usage

With GitHub Copilot in VS Code

Available Tools

1. parse_email_inquiry

2. search_sql_configs ⭐ Metadata-based search

3. search_java_code ⭐ Metadata-based search

4. execute_java_report

5. rebuild_metadata_index

6. generate_response_summary

Project Structure

Development

Running Locally

Customization

Connecting to Your Repository

Next Steps

Key Innovation: No More File Path Search! 🎉

Troubleshooting

Server not appearing in VS Code

Python version issues

Java execution errors

Resources

License

Available Tools

Output Schema

Output Schema

Output Schema

Output Schema

Output Schema

Output Schema

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

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Latest Blog Posts

MCP directory API

1. `parse_email_inquiry`

2. `search_sql_configs` ⭐ Metadata-based search

3. `search_java_code` ⭐ Metadata-based search

4. `execute_java_report`

5. `rebuild_metadata_index`

6. `generate_response_summary`