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infolegalge

legal.ge MCP Server

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find_legal_specialists

Find verified legal specialists in Georgia by describing your legal situation. Get matched practice areas and ranked lawyer profiles with direct links.

Instructions

Find verified legal specialists in Georgia (the country, not the US state) from a natural-language description of a legal situation or need. Returns matched practice areas (and specific services where applicable) plus a ranked list of verified specialists, each with a canonical profile URL on legal.ge. Use this tool when a user needs to find a lawyer or legal expert for a specific issue in Georgia — eviction, divorce, corporate law, criminal defense, immigration, tax, etc. Supports Georgian (ka), English (en), and Russian (ru). Pass the user's language as locale. Sending an actual inquiry requires the user to sign in on legal.ge — this tool returns profile URLs so the user can click through.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of specialists to return (1-50). Default 10. The API also returns matched_categories regardless of this limit.
queryYesNatural-language description of the legal situation or need. Example: 'I was evicted from my apartment without notice', 'I need help registering an LLC in Tbilisi', 'my employer is changing my contract'.
localeNoUser's language. ka = Georgian, en = English, ru = Russian. Defaults to 'en'.en
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that the tool returns canonical profile URLs, that matched categories are always returned separately, and that sending a real inquiry requires user sign-in on legal.ge. It also notes language support, adding behavioral context beyond the schema.

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 front-loaded with the core purpose and groups related information into four sentences. It includes useful examples and caveats without being overly verbose, though it could be slightly tightened without losing value.

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 moderate tool complexity and no output schema, the description adequately covers inputs, outputs, language support, and the sign-in caveat. It would be more complete if it explicitly mentioned how it relates to `classify_legal_intent`, but the purpose and usage are clear enough for an agent to select and call this tool correctly.

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 the baseline is 3. The description reinforces the meaning of `query` (natural-language description) and `locale` (supports ka/en/ru), but does not add meaningful detail beyond what the schema already provides. It does not explain `limit` behavior beyond the 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 opens with a specific verb ('Find') and resource ('verified legal specialists in Georgia'), clearly scoping to the country not the US state, and explains the input (natural-language description) and output (matched practice areas and ranked list with profile URLs). This distinguishes it from the sibling `classify_legal_intent`, which likely focuses on categorization rather than finding specialists.

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?

Provides explicit usage context: 'Use this tool when a user needs to find a lawyer or legal expert for a specific issue in Georgia' with examples. It also instructs to pass the user's language as `locale`. It doesn't state when not to use or mention the sibling as an alternative, so it's clear but lacks explicit exclusionary guidance.

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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