Law.AI MCP Server

Server Quality Checklist

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Latest release: v0.2.1
Disambiguation5/5
Each tool has a clearly distinct purpose: searching, profiling, name lookup, practice areas, jurisdictions, natural language matching, and cost estimation. No overlap or ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., search_lawyers, get_lawyer_profile). No deviations.
Tool Count5/5
7 tools is well-scoped for a legal directory and matching service, covering search, profile, lookup, metadata, matching, and cost estimates without being too many or too few.
Completeness5/5
The tool set comprehensively covers the domain: finding lawyers by search, name, or natural language; retrieving profiles; exploring practice areas and jurisdictions; and estimating costs. No obvious gaps.
Average 3.9/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations; description discloses that it lists practice areas with counts, but omits potential behavioral traits like auth requirements or response structure.
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 concise sentences, front-loaded with action and result, no unnecessary words.
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?
Sufficient for a simple list-all tool with no params and no output schema; could mention sorting or filtering, but not required.
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?
No parameters; schema coverage is 100% trivially. Description adds context (lawyer counts) beyond empty schema, but baseline is adequate.
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?
Clear verb 'list' and resource 'practice areas' with added detail 'with lawyer counts'. Distinguishes from sibling tools that search for specific lawyers or profiles.
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?
Mentions usefulness for discovering types of lawyers, but lacks explicit when-not-to-use or alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool classifies the issue into practice areas and returns ranked matches, but does not mention side effects, auth needs, rate limits, or behavior when no match is found. This is adequate but not comprehensive.
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 two sentences, concise and to the point. Every word contributes to understanding, with no filler or redundancy.
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 has 5 parameters and no output schema, the description covers the purpose and process but lacks details on the output format, such as what fields the matches contain or how to interpret the ranking. It is adequate for a straightforward match tool but could be more complete.
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 baseline is 3. The description adds minimal extra meaning beyond the schema's parameter descriptions. It states the overall process but does not enhance individual parameter semantics, such as what 'budget' or 'urgency' values mean in context.
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: describe a legal issue and get matched with relevant lawyers. It specifies classification into practice areas and ranking by relevance, credentials, and location, distinguishing it from sibling tools like search_lawyers or find_lawyer_by_name.
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 for matching based on a legal issue, but does not explicitly state when to use this vs alternatives. It lacks guidance on when not to use it or what specific scenarios it is best suited for, though sibling tools provide context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that contact info is only available for claimed profiles, which is a useful behavioral note. However, it doesn't mention rate limits, authentication, or other behaviors.
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 two sentences, front-loading the main purpose and then listing returned fields. Every part is necessary, with no wasted words.
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?
For a simple profile retrieval tool with one parameter and no output schema, the description covers purpose, return content (bio, education, etc.), and a conditional (contact info for claimed profiles). It is missing error scenarios, but overall fairly complete.
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?
The schema already describes the single parameter as 'The Law.AI lawyer profile ID' with 100% coverage. The description adds 'Law.AI ID' but this is redundant; it does not provide additional meaning 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 clearly states the verb 'Get', the resource 'full profile of a specific lawyer', and the identifier ('by their Law.AI ID'). It distinguishes from siblings like search_lawyers and find_lawyer_by_name by specifying retrieval by unique ID.
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 (need the lawyer ID) but does not explicitly state when not to use it or mention alternatives like find_lawyer_by_name for name-based lookup. The sibling list provides context, but the description lacks direct guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It mentions disambiguation by location but does not disclose what happens on not found, what fields are returned, or any behavioral traits like permissions or side effects.
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, front-loaded with the action, no wasted words. Efficient and to the point.
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?
For a simple lookup tool with two parameters and no output schema, the description covers the purpose and a use case. However, it lacks information about the return format or how to handle multiple matches, which is relevant given sibling tools like 'get_lawyer_profile'.
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 100% with good parameter descriptions. The description adds value by explaining the disambiguation by location, which relates to the state parameter, exceeding the baseline of 3.
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 verb 'Look up a specific lawyer by name' and distinguishes from sibling 'search_lawyers' by focusing on a single lawyer. It also adds context for verifying licensing and disambiguation by location.
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 a clear use case: verifying if a lawyer exists and is properly licensed. Implies when to use it but does not explicitly compare to alternatives like 'search_lawyers' or state when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool returns estimated fee ranges adjusted by complexity and region, and that complexity is the primary cost driver. However, it does not discuss limitations, authentication requirements, or the reliability of estimates.
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, no wasted words. The first sentence front-loads the purpose, and the second adds crucial detail about billing structures and cost drivers.
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 the description explains the inputs and general outputs, it lacks an output schema and does not fully specify the return format (e.g., structure of fee ranges). For a tool with 4 parameters and no output schema, more detail on the response would improve completeness.
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% and each parameter has a description. The description adds minimal extra value beyond the schema, only emphasizing that complexity is the primary cost driver. Baseline is 3 for high 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 tool's purpose: 'Get estimated legal costs for a type of legal issue.' It specifies outputs (fee ranges by billing structure) and distinguishes from sibling tools which focus on lawyer profiles and searches.
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 for when to use this tool (cost estimation) versus siblings (lawyer-related tasks). However, it does not explicitly state when not to use it or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 states that results are ranked with claimed/verified profiles first, which is useful. However, it does not detail other behaviors such as default sorting order, whether partial matching is applied, or pagination behavior beyond parameter definitions.
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 concise sentences that front-load the tool's action and resource, include key filtering options, and mention the ranking behavior. Every phrase adds value, with no redundancy or wasted words.
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 6 parameters, no output schema, and no annotations, the description covers the essential aspects: directory size, filtering options, and ranking. It lacks details on edge cases (e.g., empty results) or precise ordering, but for a search tool it provides sufficient context for an agent to understand its basic behavior.
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 baseline is 3. The description adds marginal value by mentioning specific filterable parameters and providing example values (e.g., 'Criminal Defense' for practice_area). It does not elaborate on offset/limit further, but the schema already defines them clearly.
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: searching the Law.AI directory of verified lawyer profiles. It explicitly lists filtering dimensions (practice area, state, city, bar status) and mentions ranking behavior, distinguishing it from siblings like get_lawyer_profile (individual profile lookup) and find_lawyer_by_name (name-based search).
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 usage context by specifying when to use the tool (when filtering by practice area, state, city, bar status is needed). It implies this is the primary search tool for directory lookups. However, it does not explicitly mention when not to use it or list alternatives, though sibling tools are available for reference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It implies a read-only listing operation but does not disclose potential limitations like data freshness or whether authentication is needed. Minimal behavioral insight.
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 short, front-loaded sentences with no redundant information. Every phrase adds 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 no parameters, no output schema, and no annotations, the description reasonably explains the tool's purpose and output. Could mention that it returns all jurisdictions (since no filters), but overall adequate for a simple listing tool.
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?
No parameters exist; description does not need to add param details. Baseline 4 is appropriate for zero-parameter tools.
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?
States a specific verb 'list' and a specific resource 'US states and jurisdictions with lawyer counts', and adds value by mentioning coverage indication. Clearly distinguishes from siblings that focus on individual lawyers.
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?
Implied usage as a coverage overview tool; given sibling tools focus on lawyer search, the differentiation is clear. However, lacks explicit when-to-use or when-not-to-use 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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