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jacwalste

VendorOps MCP

by jacwalste

VendorOps MCP

VendorOps is a Model Context Protocol (MCP) server for a synthetic internal vendor-management workflow. It gives AI applications structured access to vendor records, contracts, spending, renewals, and security reviews.

The project is intentionally built in small, reviewable slices. Phase 1 uses Python, the official MCP Python SDK, SQLite, Pydantic, and pytest to expose read-only vendor data over a local stdio connection.

All vendor and contract data in this repository is synthetic.

Development

Install the locked dependencies:

uv sync

Run the test suite:

uv run pytest

Run the local stdio server:

uv run vendorops-mcp

The server waits for an MCP host to send protocol messages over stdin. It does not open a network port or print a user interface.

By default, VendorOps creates data/vendorops.db and seeds it with 30 synthetic vendors. Set VENDOROPS_DB_PATH to use a different SQLite file.

Related MCP server: SupliiChain MCP Sandbox

Current MCP Capabilities

Tools

  • search_vendors: Search vendor records by name, category, or owner, with optional category and status filters.

Available Tools

1 tool
search_vendorsSearch VendorsA

Search vendor records by a name, category, or owner substring. Optionally filter by exact category or vendor status.

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNo
queryNo
statusNo
categoryNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
countYes
vendorsYes

TDQS

A4.1/5.0
Behavior3/5

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

The description adds important behavioral context: the query is a substring match across name/category/owner, while category and status are exact filters. However, without annotations, it does not disclose behaviors such as pagination, result limits, or behavior when no query is provided (e.g., returns all vendors). These gaps make it adequate but not fully transparent.

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 concise and front-loaded: two short sentences that immediately convey the core functionality and the optional filters. There is no redundant or filler content.

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?

The description covers the main search and filter features, and an output schema exists, so return values are documented. However, it does not explain how query and filters combine (AND vs OR), nor the behavior when the query is omitted. These are important for effective invocation, leaving a clear gap.

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 coverage is 0%, so the description must compensate. It clarifies the semantics of the 'query' parameter (substring across fields), and the 'category' and 'status' parameters (exact filters). The 'limit' parameter is not mentioned, but its schema provides min/max/default, so the description adds value for the ambiguous parameters.

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 function: 'Search vendor records by a name, category, or owner substring.' It specifies the resource (vendor records), the actions (substring search and optional exact filters), and gives enough detail to distinguish it from a generic search tool.

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?

No sibling tools are listed, so explicit alternatives are not applicable. The description provides clear context on when to use the tool (searching vendors with substring or exact filters) and implies that filtering is optional. There are no exclusions or when-not guidance, but the purpose is clear enough for a standalone tool.

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

TDQS

A3.9/5.0
Disambiguation5/5

With only a single tool, there are no overlapping purposes or ambiguity. The tool clearly defines its own scope.

Naming Consistency5/5

The single tool name follows a clear verb_noun pattern (search_vendors), which is consistent and predictable.

Tool Count2/5

The server name VendorOps implies broader vendor management, but only one search tool is provided. This is too few for the apparent scope, leaving the toolset feeling thin.

Completeness1/5

The toolset covers only searching vendors, with no create, update, delete, or other lifecycle operations. This is a severe gap for a vendor operations server.

Maintenance

ActivityMaintained
ResponsivenessSyncing

Resources

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