mcp-courtwatch

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool targets a distinct resource and action: case_detail fetches full text by ID, citation_lookup verifies citations, court_list lists courts, docket_lookup searches dockets, judge_lookup looks up judges, opinion_search searches opinions. No overlap in purpose.
Naming Consistency5/5
All tool names use snake_case with a consistent noun_verb pattern (case_detail is noun_noun but fits naturally). The naming is predictable and uniform.
Tool Count5/5
Six tools is a well-scoped set for a legal research server covering search, listing, lookup, and detail retrieval. Neither too few nor too many.
Completeness4/5
Core workflows are covered: search opinions/dockets/courts/judges, verify citations, and fetch case details. Minor gaps like docket detail or date filtering exist, but the tool surface is solid for the intended use.
Average 4.3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 16 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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 are provided, so the description must carry the full burden. It discloses the authentication need and outlines the response structure (cluster vs. opinion fields). However, it lacks details on error behavior (e.g., missing id, invalid type) and does not mention any side effects or rate limits. For a read-only fetch, this is adequate but not thorough.
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 extremely concise: three sentences covering purpose, auth, and usage. No unnecessary words or repetition. It is well front-loaded with the core action.
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 complexity (two resource types) and no output schema, the description manages expectations well by listing what each resource returns (cluster fields: name, citations, date, judges, opinion ids; opinion: full text). It could mention potential errors, but for a fetch tool, the core information is present.
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 covers both parameters with clear descriptions (id: numeric, type: enum). The description adds the context of using cluster_id from search results, but does not significantly enhance the understanding of the parameters themselves. With 100% schema coverage, the baseline is 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 'Fetch a full case by id' and distinguishes between two resource types: a cluster (the case) with its fields, or a single opinion. This specific verb-resource pairing and the differentiation from other tools like 'opinion_search' makes the purpose unambiguous.
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 tells when to use the tool (to get full details of a case or opinion), mentions the authentication requirement, and even suggests how to obtain the id ('Use the cluster_id from opinion_search results'). It does not explicitly list when not to use it, but the context and sibling tools imply alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries full burden. It discloses that the tool works without authentication and returns specific fields. However, it does not mention pagination behavior or any rate limits, which are relevant for a search tool.
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 core purpose and return values. Every sentence contributes value, with no fluff or repetition.
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?
The description covers the tool's purpose, parameters, and returned fields. However, it omits mention of pagination cursors in the return structure, which are referenced in the parameter description but not in the output summary.
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 input schema has 100% coverage with descriptions for all 5 parameters. The tool description adds a summary that aligns with parameters but provides no additional meaning beyond the schema definitions.
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 this tool searches court dockets by case name, free text, or docket number, with optional court scoping. It distinguishes itself from siblings like opinion_search or case_detail by focusing specifically on docket records.
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 (search dockets) but does not explicitly state when to use this tool versus siblings like citation_lookup or judge_lookup. No 'when not to use' guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It details the return fields (case name, court, date, citations, docket number, snippet, citation count, link) and notes the token requirement for rate limits. No destructive actions or contradictions.
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 two concise sentences, front-loading the core purpose and listing key return fields. It could use slight structuring, but it is efficient and clear.
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?
Despite no output schema, the description enumerates the return fields and explains pagination and ordering. It covers essential behaviors for a 7-parameter search tool, though error handling is omitted.
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%. The description adds valuable context like example queries for 'q', pagination details for 'limit' and 'cursor', and sorting options, going beyond the schema's basic parameter descriptions.
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 explicitly states 'Full-text search of U.S. case law / court opinions' with a specific verb ('search') and resource ('opinions'), and distinguishes itself from sibling tools like case_detail and citation_lookup by focusing on open-ended text queries.
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 notes it works without a token but recommends setting COURTLISTENER_API_TOKEN for higher rate limits, offering clear context. It does not explicitly contrast with siblings, but the purpose is sufficiently distinct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, but description explicitly lists return fields (id, name, dates, gender, positions count) and states no token required, providing good 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 concise sentences, front-loaded with purpose and return details, 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?
For a 3-parameter look-up tool with no output schema or annotations, description provides return field info and auth requirement, but lacks error handling or pagination details.
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 covers all 3 parameters with descriptions; description adds value by detailing return fields beyond schema, helping agent understand outputs.
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 looks up judges/people in CourtListener's judiciary database by name, distinguishing it from siblings that deal with cases, citations, courts, dockets, and opinions.
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 explicit when-to-use or alternatives, but sibling tools are unrelated so usage is clear. Mentions it works without a token, implying ease of use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. It discloses the required authentication token, character and citation caps, rate limit, and output flags (FOUND, NOT_FOUND, UNKNOWN_REPORTER, NOT_CHECKED_OVER_CAP). It does not cover error handling for invalid tokens, but the disclosed behavior is sufficient.
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 a single, well-structured paragraph of five sentences. It is front-loaded with the primary purpose, followed by usage instructions, then behavioral details and limitations. Every sentence adds value with no redundancy or fluff.
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 has a single parameter, no output schema, and no annotations, the description covers all necessary aspects: purpose, usage, authentication, limits, and output type. It is complete for an agent to decide when and how to invoke the 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?
The schema already provides a description for the 'text' parameter, achieving 100% coverage. The description adds significant value by explaining the two input forms (free text or single citation string), the maximum character length, and the citation cap behavior. This goes beyond the schema definition.
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: verifying legal citations against CourtListener's database to catch fabricated or mangled citations. It specifies the verb 'verify' and the resource 'legal citations', and distinguishes itself from sibling tools like case_detail or opinion_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 context on when to use the tool ('before relying on legal citations'), how to use it (pass free text or a single citation string), and operational limits (characters, citations, rate limit). It does not explicitly list when not to use it or mention alternatives, but the purpose is distinct enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses that it lists courts with ids, supports optional filtering, and requires no authentication. As a read-only list operation, this is sufficiently 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?
Description is concise (three sentences) and well-structured: first sentence states purpose, second explains filters, third explains jurisdiction codes. No filler or 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 no output schema, description adequately covers return (list of courts and ids). Covers all three parameters with clear context. Also connects to sibling tools (opinion_search, docket_lookup). Complete for a list tool.
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?
Adds meaning beyond 100% schema coverage: explains jurisdiction codes in detail, notes that 'q' is case-insensitive and matches multiple fields, and clarifies that limit controls page size. This helps the agent use parameters effectively.
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?
Description clearly states 'List CourtListener courts and their ids' and explains their use in other tools. Distinguishes from siblings by focusing on listing courts rather than searching opinions, dockets, etc.
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?
Explicitly says 'Works without a token' and explains optional filters. Implies usage for obtaining court ids before using opinion_search/docket_lookup. Could add explicit when-to-use vs alternatives, but context is clear.
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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