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deflucaseng

Legal Docket Monitor MCP Server

by deflucaseng

find_matching_clients

Fuzzy-search your firm's configured client list (SharePoint, Excel, or webhook) to find matching clients by name before linking a docket. Returns candidate matches for review.

Instructions

Fuzzy-search the firm's configured client list (SharePoint, Excel, or custom webhook) for clients matching a name or query. Requires CLIENT_SOURCE_TYPE to be configured. Use this to find candidate clients before linking a docket with link_docket_to_client.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesCase name, party name, or client name to search for
limitNoMaximum number of candidate matches to return
Behavior3/5

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

With no annotations, the description must carry the burden of behavioral disclosure. It covers the fuzzy-search behavior and the dependency on configuration, but does not mention performance, rate limits, or what happens if the source is misconfigured.

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?

Two sentences with zero waste. The most important information (operation, source, prerequisite, use case) is in the first sentence. Highly efficient.

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?

While all parameters are documented in the schema, there is no output schema and the description does not explain what the return format looks like (e.g., list of client objects with IDs). This is a moderate gap for a search tool.

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 coverage is 100%, so baseline is 3. The description adds context that the query is for client names, but does not add new meaning beyond what the schema parameter descriptions already provide.

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 it performs fuzzy-search on the configured client list, specifies the data sources (SharePoint, Excel, custom webhook), and directly distinguishes itself from the sibling tool link_docket_to_client by indicating its use case as a prerequisite.

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?

It explicitly mentions the prerequisite CLIENT_SOURCE_TYPE configuration and gives a specific usage scenario before linking a docket. It does not explicitly state when not to use it, but the context is clear enough.

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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