gb-eli-mcp

Server Quality Checklist

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Latest release: v0.4.3
Disambiguation5/5
Each tool targets a distinct action: retrieve legislation, search case law, or fetch a specific case. There is no overlap in their purposes.
Naming Consistency5/5
All tools follow the consistent pattern `gb_verb_noun` (e.g., `gb_get_text`, `gb_search_case_law`), making them predictable and easy to understand.
Tool Count3/5
With only 3 tools, the set feels thin for a legal research server, especially since legislation and case law are treated asymmetrically. However, the focus is narrow enough to justify the count.
Completeness2/5
There is a notable gap: no search tool for legislation, while case law has both search and retrieval. This asymmetry could cause an agent to hit a dead end when trying to find specific legislation.
Average 4.1/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 11 commits in the last 12 weeks
- Last stable release on August 7, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
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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{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering the safety profile. The description adds that it fetches metadata from legislation.gov.uk by reference, but offers no additional behavioral context such as rate limits or response format. This is a minimal but non-contradictory addition, deserving a middle score.
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 11-word sentence that starts with the verb 'Fetch' and wastes no words. It is appropriately sized for the tool's simplicity.
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 one-parameter tool with a detailed schema and informative annotations plus an output schema, the description is sufficient to understand the tool's purpose and invocation. It does not explicitly discuss alternative tools or edge cases, but these are covered in other dimensions.
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's reference parameter is thoroughly described with example formats and optional date suffix, achieving 100% schema description coverage. The tool description's mention of 'by reference' adds no new meaning beyond this. Thus the baseline score of 3 applies.
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?
The description 'Fetch UK legislation metadata from legislation.gov.uk by reference' uses a specific verb and resource, clearly identifying the tool's function. It distinguishes from siblings like gb_search and gb_get_text by emphasizing 'metadata' and 'by reference', though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as gb_search or gb_get_text. It neither mentions exclusions nor suggests related tools for different needs, leaving the agent to infer usage from the tool's name and schema.
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?
Annotations already cover readOnly, idempotent, openWorld, and non-destructive hints. The description adds useful behavioral context: it maps to the Atom feed endpoint, lists how query parameters are used, and specifies that each item includes eli_uri, human_readable_citation, and source_url. This goes beyond the annotations, though the 'per Art. 4 CONSTITUTION' is cryptic but not misleading.
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 sentences and front-loaded with the core purpose. The second sentence packs technical detail about mapping and return fields. However, 'per Art. 4 CONSTITUTION' is obscure and might distract, slightly reducing clarity. Overall, it is fairly concise and well-structured.
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 tool's moderate complexity, existing annotations, and presence of an output schema, the description provides adequate context. It fully explains the tool's scope and the source mapping. It does not elaborate on pagination or error behavior, but these are not critical given the annotations and output schema.
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%, with the query object already documented. The description mentions the Atom feed query parameters (?text=...&year=...&page=...), but this largely mirrors the schema. It adds minimal meaning beyond what the schema provides, so the baseline of 3 applies.
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 'Search UK legislation on legislation.gov.uk' — a specific verb, resource, and platform. It distinguishes from sibling search tools like gb_search_case_law and gb_search_govuk by explicitly focusing on legislation.
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 context of use is clear (searching UK legislation), but there are no explicit exclusions or alternative recommendations. The description doesn't mention when to use this tool instead of sibling search tools, so usage guidance is implied rather than stated.
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?
Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and idempotency. Description adds the specific data source but does not elaborate on error handling, availability, or other behavioral details beyond what annotations convey.
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 main description is a single, concise sentence with no wasted words. The overall definition is well-structured with the necessary schema and annotations, making it easy to parse.
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 tool has a simple interface (1 parameter, no nested objects), the description combined with the output schema and annotations covers the essential aspects. However, it could mention what happens if the judgment is not found or confirm the output format explicitly.
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 single parameter 'reference' has a detailed schema description (100% coverage) that explains multiple input formats and provides a usage hint about pre/post April 2025 documents. This adds significant meaning beyond the parameter name and type.
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 verb 'Fetch' and resource 'a UK judgment' from a specific source 'The National Archives' Find Case Law service'. It distinguishes from sibling tools like gb_get_act and gb_get_govuk_content by targeting case law specifically.
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?
Main description does not explicitly state when to use this tool vs alternatives. However, the parameter description hints that for newer documents with opaque ids, gb_search_case_law should be used first, providing indirect guidance. Lacks explicit when-to-use or when-not-to-use context.
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?
Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) already indicate safety. The description adds valuable context about the source (legislation.gov.uk) and format options, enhancing transparency beyond annotations.
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 purpose, no wasted words. Each sentence earns its place, explaining purpose and technical details efficiently.
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 simple tool (2 params, output schema present, annotations thorough), the description covers the source, format options, and reference requirement. It omits return-value details, but output schema likely covers that.
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 provides context about the content-negotiation path but does not add new parameter meaning beyond the schema definitions for 'reference' and 'format'.
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 the full text of UK legislation' with a specific verb and resource. It distinguishes itself from siblings like gb_get_act and gb_get_case by focusing on text retrieval in multiple formats.
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 explains the mechanism (content-negotiation) but does not explicitly state when to use this tool versus alternatives like gb_get_act or gb_search. No when-not or exclusion criteria are 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?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as true/false. The description adds value by confirming the service is live, keyless, and uses Open Justice Licence, and notes the Atom dialect ('tna:' namespace). No contradiction.
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 (two sentences plus a mapping note), front-loaded with the main purpose, and contains no redundant information. Every sentence 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 the number of parameters (all optional) and the presence of an output schema, the description provides sufficient context about the service and its caveats (client-side date filtering). It is mostly complete, though it does not explain default behavior when no filters are applied.
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%. The description does not add new meaning beyond the schema; it only mentions the Atom feed mapping. Baseline 3 is appropriate since the schema already documents the parameters adequately.
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 'Search UK case law' with a specific verb and resource. It distinguishes from siblings by noting it targets The National Archives' Find Case Law service and is separate from legislation.gov.uk.
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 context about the service (keyless, Open Justice Licence) and how it differs from legislation.gov.uk, but does not explicitly specify when to use this tool versus alternatives or when not to use it.
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?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context beyond these by explaining the API mapping, the confirmed live status, and content-specific differences (PDF attachments vs. body_html). It does not cover error handling or rate limits, but the annotation coverage lowers the bar.
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 compact and purpose-first, using three sentences to convey the function, API endpoint, and important content-type caveats. Every clause adds information; there is 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?
Despite having only one parameter, the description covers the key behavioral nuances (attachments vs. body_html) and references the output schema implicitly by noting where content appears. With a detailed schema, output schema, and strong annotations, this is a complete and self-sufficient tool description.
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 description provides 100% coverage for the single 'path' parameter, including its source, format, and examples. The tool description itself adds no further parameter semantics, so with high schema coverage the baseline of 3 is appropriate.
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 uses a specific verb ('Fetch') with a clear resource ('one GOV.UK document') and enumerates the covered content types (tribunal decision, HMRC manual section, CMA case). This distinguishes it from sibling tools like gb_get_act or gb_get_case by scope and source.
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 provides clear context on when to use the tool (fetching a single GOV.UK document) and gives direction on obtaining the path from gb_search_govuk results. However, it does not explicitly name alternative tools or state when not to use it, so it falls short of full exclusion guidance.
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?
Annotations already declare readOnlyHint and destructiveHint=false, but the description adds a valuable behavioral trait: it explains that the public feed does not support a generic dateFrom filter, so the tool fetches the yearly feed and filters client-side. This discloses a limitation and implementation nuance that an agent should know, going beyond the structured hints.
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 three sentences, front-loaded with the main verb and resource. It includes necessary implementation details without fluff. Every sentence adds value: purpose, mapping to feed, and filtering rationale.
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?
With an output schema present and robust annotations, the description completes the picture by explaining the feed source, the client-side filtering rationale, and the optional scoping. This is adequate for an agent to select and invoke the tool correctly, including understanding the year-based limitation.
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% and the description adds meaningful semantics beyond the schema. It clarifies that 'since_iso' uses only the year to choose the feed and then filters to on/after the date, and that doc_type scopes the feed. This extra context helps the agent understand parameter behavior beyond the raw schema 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 opens with a specific action and resource: 'UK legislation published since since_iso (ISO 8601 date), newest-first.' This clearly identifies the tool as a date-filtered list of legislation, distinguishing it from siblings like gb_search which searches generally. The optional doc_type scoping further refines the purpose.
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: this is for retrieving legislation by publication date, optionally restricted to a document type. It explains the underlying feed mechanism and client-side filtering, but it does not explicitly name alternatives or state when not to use it. This is strong context without explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnly and idempotent annotations, the description discloses that the API is keyless, under the Open Government Licence v3.0, and confirmed live as of 2026-07-07. It also provides live-verified totals for legally weighty document types, adding concrete behavioral context about the upstream data source and filtering behavior.
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, then adds useful API mapping and document type details. The long list of type counts is valuable but somewhat verbose, so it earns a strong score rather than a perfect one.
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?
Annotations cover safety and idempotency, the schema covers all parameters, and an output schema exists, so the description only needs to add real-world context. It provides the exact endpoint, license, live verification date, and document type options, making it complete for agent selection and invocation.
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 documents all 7 parameters with descriptions (100% coverage). The main description adds a curated list of valid document_type slugs with raw counts, directly enriching the document_type parameter beyond the schema's "see above" note. Other parameters are not further elaborated, so it stays slightly above baseline.
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 "Search GOV.UK documents - tribunal decisions, HMRC manuals, CMA cases and more," specifying the verb and resource clearly. It distinguishes itself from siblings like gb_search_case_law by naming the GOV.UK API and listing concrete document types, making the tool's 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 clearly implies when to use this tool by listing supported content types and noting it maps to the GOV.UK Search API. However, it does not explicitly state when not to use it or mention alternative tools like gb_search_case_law, so it provides context but no exclusions.
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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