Gibraltar Prediction Markets MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Gibraltar Prediction Markets MCP ServerWhat must an application for authorisation include?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Gibraltar Prediction Markets โ MCP Server
Query the Prediction Market Regulations 2026 (LN.2026/176) in plain English from Claude Desktop, Claude Code, ChatGPT, Cursor, or any MCP client. Ask a question โ "What must an application for authorisation include?" โ and get back the exact, cited regulation text instead of a guess.
Built for lawyers and regulatory professionals who want the statute inside the tools they already use.
๐ Official text: Prediction Market Regulations 2026 (LN.2026/176) โ gibraltarlaws.gov.gi
โ๏ธ Not legal advice. This server reproduces Gibraltar legislation for research and reference only. It is not legal advice and not an official version of the law. Always verify against the official text and consult qualified Gibraltar counsel before relying on it.
Connect your client
Client | Guide |
Claude Desktop | |
Claude Code (CLI) | |
ChatGPT (custom connector) | |
Cursor & other MCP clients | |
Claude API / Agent SDK | |
The law & data model |
Related MCP server: US Regulations MCP Server
Quick start
The server is hosted at https://mcp.0xjb.dev/mcp โ nothing to install. Connect from any MCP client:
Claude Desktop
Open Settings โ Connectors (under your profile menu).
Click Add custom connector.
Name it (e.g.
Gibraltar Prediction Markets) and paste the URL:https://mcp.0xjb.dev/mcpLeave the OAuth Client ID blank โ no sign-in is required โ and click Add.
In a new chat, open the tools menu (sliders icon) and check the connector is enabled, then ask away.
ChatGPT
Enable developer mode: Settings โ Apps & Connectors โ Advanced settings โ toggle Developer mode (requires a paid plan).
Go to Settings โ Apps & Connectors โ Create (custom connector).
Name it, set MCP server URL to
https://mcp.0xjb.dev/mcp, authentication: None.Save, then enable the connector in a chat via the + / tools menu. ChatGPT retrieves through the server's
searchandfetchtools.
Claude Code (CLI)
claude mcp add --transport http gibraltar-pm https://mcp.0xjb.dev/mcpCursor / other MCP clients
Add a remote (streamable HTTP) MCP server with URL https://mcp.0xjb.dev/mcp โ see docs/cursor.md.
Local via npm (stdio)
Every client can also run the npm package locally โ no build, no paths:
{
"mcpServers": {
"gibraltar-prediction-markets": {
"command": "npx",
"args": ["-y", "gibraltar-prediction-markets-mcp"]
}
}
}# Claude Code CLI
claude mcp add gibraltar-prediction-markets -- npx -y gibraltar-prediction-markets-mcpFor local testing before publishing, see PUBLISHING.md and the per-client guides (node dist/index.js over stdio, or npm run start:http for ChatGPT/remote).
What you can ask
"List the arrangement of the Prediction Market Regulations 2026."
"What does regulation 12 say about approving contracts?"
"Give me the checklist for an authorisation application."
"Define 'prediction market contract'."
"Which contracts can the Authority prohibit under regulation 14?"
"Can operators accept stablecoins?" (regulation 22)
Tools
Ten tools; search + fetch also satisfy ChatGPT's connector contract.
Tool | Purpose |
| Ranked keyword search; returns ids for |
| Full verbatim text by id ( |
| A regulation, 1โ34, with its Part. |
| The full arrangement of provisions. |
| Every regulation in a Part, 1โ7. |
| A Schedule, 1โ3. |
| A defined term (reg 3); omit to list all. |
| Schedule 1 โ application requirements. |
| Schedule 2 โ core conditions. |
| Metadata, source and status of this server. |
Every response carries its source citation and a not-legal-advice reminder.
Data model
One authoritative file, data/regulations.json, holds the verbatim text. Each unit is an addressable document with a stable id โ reg-1โฆreg-34, schedule-1โฆschedule-3, and definitions (the 14 defined terms of reg 3). This single shape serves every tool. Full walkthrough in docs/concepts.md.
Source
Law: Prediction Market Regulations 2026, LN.2026/176 โ subsidiary legislation under the Gambling Act 2025 (ss. 34 and 159). Commencement 13 July 2026.
Official text: 2026s176.pdf ยท ยฉ Government of Gibraltar, gibraltarlaws.gov.gi.
License
MIT for the server code. The legislative text is ยฉ Government of Gibraltar, reproduced for reference.
Available Tools
10 toolsaboutAbout this serverA
Return metadata about the Prediction Market Regulations 2026 and this server: citation, enabling powers, commencement, structure and source.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description bears full burden. It explains what is returned but does not disclose behavioral traits like read-only nature, authorization, or idempotency. Adequate but not extra.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, well-structured, includes specific detail. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description fully explains the tool's purpose and return content. Complete for a metadata endpoint.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters (baseline 4). Description adds meaning about the return value, rendering parameter semantics irrelevant.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it returns metadata about the Prediction Market Regulations 2026 and the server, listing specific fields (citation, enabling powers, etc.). Distinguishes from siblings that handle specific queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implicitly indicates when to use (when metadata about server/regulations is needed). No explicit exclusions or alternatives, but context makes it clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fetchFetch a regulation, schedule or the definitions by idA
Retrieve the full verbatim text of a document by its id (e.g. 'reg-12', 'schedule-2', 'definitions'). Ids come from search. Returns the complete provision.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Document id, e.g. 'reg-19', 'schedule-1', 'definitions'. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must bear the burden. It states it returns 'the complete provision', indicating a read operation, but lacks details on error handling, permissions, or side effects. Adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no wasted words. The purpose is stated first, and the description is efficiently front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description only says 'Returns the complete provision,' which is vague. The tool is simple, but more detail (e.g., structure of response) would improve completeness. Sibling overlap also not clarified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage for the single 'id' parameter with a description. The tool description adds context (e.g., 'Ids come from search') and examples, but adds limited meaning beyond the schema. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Retrieve' and resource 'full verbatim text of a document by its id', with examples like 'reg-12'. However, it does not differentiate from sibling tools like get_regulation, get_schedule, and get_definition, which appear to serve similar purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage after search ('Ids come from search') but provides no explicit guidance on when to use this tool versus siblings, nor 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.
get_application_checklistGet the authorisation application checklistA
Return the matters that must accompany an application for a prediction market authorisation (Schedule 1, per regulation 7), as a checklist.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description must fully convey behavioral traits. It accurately states the output as a checklist, but does not mention read-only nature, permissions, or any side effects. The behavior is implied but not explicitly disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that immediately conveys the tool's purpose and scope. No wasted words, front-loaded with key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity (no parameters, no output schema) and sibling context, the description is largely complete. It provides the regulatory basis and what is returned. Minor improvement would be to hint at the return format, but it is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and schema description coverage is 100%. The baseline for zero parameters is 4, and the description does not need to add parameter details. It appropriately omits unrelated content.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a checklist of matters required for a prediction market authorisation, with specific regulatory references (Schedule 1, regulation 7). This distinguishes it from siblings like get_authorisation_conditions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide explicit guidance on when to use this tool versus alternatives, such as get_authorisation_conditions. However, given the tool has no parameters and is straightforward, the context is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_authorisation_conditionsGet the core authorisation conditionsA
Return the core conditions an applicant must meet for the Authority to grant a prediction market authorisation (Schedule 2, per regulation 8).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description discloses the tool returns conditions with legal reference, but no annotations exist and no further behavioral details (e.g., side effects, access restrictions) are provided.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with key information (what it returns, legal basis) front-loaded. No redundant text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 0-parameter, no-output-schema tool, the description is complete: it explains what is returned and the legal context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters, so schema coverage is 100% trivially. Baseline 4 for 0 parameters; description does not need to add parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool returns core conditions for authorisation, referencing specific schedule and regulation. Differentiates from sibling tools like get_schedule and get_regulation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied usage for retrieving authorisation conditions, but no explicit guidance on when to use this tool versus siblings 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.
get_definitionLook up a defined termA
Return the interpretation (regulation 3) of a defined term, e.g. 'prediction market contract', 'authorised operator', 'settlement source'. Omit the term to list all defined terms.
| Name | Required | Description | Default |
|---|---|---|---|
| term | No | The term to define. Omit to list all defined terms. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description bears full burden. It implies a read-only retrieval but does not explicitly state safety, authorization needs, or any side effects. It adds minimal behavioral context beyond the operation itself.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences, front-loaded with the core action and examples, with zero wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description explains the primary action and optional usage to list all terms. It lacks details on return format or error handling but is largely complete given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (the parameter's description in the schema matches the tool description). The description adds no extra meaning beyond what the schema already provides, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns the interpretation (regulation 3) of a defined term, with concrete examples like 'prediction market contract'. This specific verb+resource combination distinguishes it from siblings like get_regulation (which returns full text).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly explains that omitting the term lists all defined terms, providing clear usage context. However, it does not mention when not to use it or suggest alternatives such as get_regulation for full regulation text.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_partGet an entire PartA
Return the full text of every regulation within a Part (1โ7) of the Prediction Market Regulations 2026.
| Name | Required | Description | Default |
|---|---|---|---|
| number | Yes | Part number, 1 to 7. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description must disclose behavior. It states the output (full text of every regulation) and parameter range, but does not mention permissions, rate limits, side effects, or error behavior (e.g., non-existent part). Since it is a read operation, the lack of destructive hints is acceptable, but more detail on output format would be helpful.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no filler. Front-loaded with verb and resource, immediately understandable. Every word serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (single parameter, no output schema), the description adequately covers what the tool returns and the valid input range. It could optionally hint at the output format (e.g., concatenated text), but completeness is high enough for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one integer parameter (number, 1-7). The description merely restates the range ('Part (1โ7)'), adding no new semantic meaning beyond the schema's description. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns the full text of every regulation within a Part (1โ7) of a specific regulation set. The verb 'return' and resource 'full text of every regulation within a Part' are specific, and the tool is distinctly different from siblings like get_regulation (which returns a single regulation) or get_definition.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like get_regulation or list_regulations. The description implies usage for retrieving all regulations in a Part, but does not provide when-not-to-use or mention prerequisites (e.g., knowing the Part number).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_regulationGet a regulation by numberA
Return the full verbatim text of a numbered regulation (1โ34) of the Prediction Market Regulations 2026, with its Part.
| Name | Required | Description | Default |
|---|---|---|---|
| number | Yes | Regulation number, 1 to 34. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that it returns full verbatim text and the associated Part. Without annotations, it adequately conveys the read-only behavior, though could mention error handling for invalid numbers.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, well-structured sentence of 24 words. Front-loaded with the action and resource, no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity and the presence of siblings, the description is sufficient. It explains the return value (full text with Part) but does not detail the 'Part' concept, though that is acceptable for a targeted tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the parameter description is already in the schema. The tool description adds context about the 2026 regulations but does not significantly enhance parameter understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns the full verbatim text of a numbered regulation (1-34) with its Part. It specifies the range and regulation name, distinguishing it from siblings like list_regulations or get_part.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The description does not mention when not to use it or suggest siblings like list_regulations for browsing numbers.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_scheduleGet a ScheduleA
Return a Schedule of the Prediction Market Regulations 2026. Schedule 1 = matters to include in an application; Schedule 2 = core authorisation conditions; Schedule 3 = modified application of the Gambling Act 2025.
| Name | Required | Description | Default |
|---|---|---|---|
| number | Yes | Schedule number, 1 to 3. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description focuses only on purpose. It does not disclose behavioral traits such as read-only nature, auth requirements, or side effects, though the tool appears to be a simple read.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, clearly structured with the primary purpose first, then details about each schedule. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description provides sufficient context about each schedule's content, making it complete for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with a description for the 'number' parameter. The tool description adds meaning by mapping schedule numbers to their content, going beyond the schema's basic range.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies 'Return a Schedule of the Prediction Market Regulations 2026' and details each schedule's content, clearly distinguishing it from sibling tools like get_authorisation_conditions or get_regulation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a specific schedule is needed but does not explicitly state when to choose this over alternatives like get_authorisation_conditions, which could overlap with Schedule 2.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_regulationsList all regulations (arrangement of provisions)A
Return the full arrangement of the Prediction Market Regulations 2026: every regulation number and title, grouped by Part, plus the three Schedules.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the return structure but does not mention whether the tool is read-only, has side effects, or limitations. Adequate but could be more transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, clear sentence that front-loads the purpose. No wasted words; every part earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and no output schema, the description adequately explains the output. However, with many sibling tools (e.g., search, get_part), it could mention that this returns all regulations, differentiating from search. Still, it is fairly complete for a listing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters, and schema description coverage is 100%. The description adds meaning by specifying exactly what is returned (full arrangement, grouped, schedules), compensating fully for the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Return the full arrangement... every regulation number and title, grouped by Part, plus the three Schedules.' This is a specific verb (list) and resource (regulations), and it distinguishes from siblings like get_regulation (single) or get_part (single part).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for getting the full list but does not explicitly state when to use it vs alternatives like search or get_part. No guidance on exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchSearch the Prediction Market Regulations 2026A
Keyword search across every regulation, schedule and defined term of the Gibraltar Prediction Market Regulations 2026 (LN.2026/176). Returns ranked results with id, title, citation and a snippet. Use the returned id with fetch to read the full text.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 10). | |
| query | Yes | Search terms, e.g. 'safeguarding client money' or 'settlement source'. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description implies a read-only search operation. It mentions returning ranked results, which is expected behavior. No contradictory or missing behavioral details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences with no filler. Front-loaded with the core purpose, followed by return value and usage hint.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Complete for a simple search tool: two parameters fully described, return value specified, and integration with sibling tool `fetch` explained. No output schema needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds value by giving example search terms for the `query` parameter. It does not repeat schema but enriches understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it performs keyword search across a specific regulation document, and describes the return fields (ranked results with id, title, citation, snippet). Distinguishes from sibling `fetch` by indicating how to use the returned id.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use the returned id with `fetch` for full text, providing a clear workflow. No explicit when-not or alternatives, 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.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
10 tool updates
v1.0.3- First observed
about - First observed
fetch - First observed
get_application_checklist - First observed
get_authorisation_conditions - First observed
get_definition - First observed
get_part - First observed
get_regulation - First observed
get_schedule - First observed
list_regulations - First observed
search
TDQS
Scored across 10 tools
Each tool serves a distinct purpose: metadata, search, list, or retrieval of specific components (regulations, parts, schedules, definitions, checklists, conditions). No two tools overlap in functionality.
Most retrieval tools follow a consistent 'get_' prefix pattern, but 'about', 'fetch', 'search', and 'list_regulations' break this convention. While still readable, the naming is not perfectly uniform.
10 tools cover the domain of a regulatory document server comprehensively without being excessive. Each tool earns its place.
The tool set covers all necessary operations for a static legal document: metadata, search, listing, and retrieval of any component. There are no obvious gaps for the stated purpose.
Maintenance
Related MCP Connectors
Polymarket rule research. ChatGPT/Claude OAuth or MCPize; approved API access required.
- docs2mcpOAuthcom.docs2mcp
Query your own PDFs and documents from any MCP client. Every answer cites the page it came from.
Judged, citation-checked policy corpus over MCP. Keyless public reads; API key for AI tools.
Experimental GDPR grounding: rules, preconditions, exceptions, exact quotes, and citation checks.
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