List all available prompts.
Returns JSON with prompt metadata including name, description, and optional arguments.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that the tool returns JSON with prompt metadata, but provides no additional behavioral details such as pagination or rate limits. Minimal added value beyond the annotations.
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, front-loaded with the core action 'List all available prompts', and includes a brief note on return format. There is no filler or redundancy.
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 zero-parameter list tool with comprehensive annotations and an available output schema, the description is complete. It states the purpose and the general shape of the return value, without needing to list fields.
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 tool has zero parameters and schema description coverage is 100%, so the schema fully documents parameters. Per the rubric, 0 params yields a baseline of 4. The description needs no further parameter explanation.
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 'List' and resource 'all available prompts', which distinguishes it from sibling get_prompt. It also specifies the return format (JSON with metadata), leaving no ambiguity about the tool's function.
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 phrase 'List all available prompts' provides clear context: use this when you need to enumerate all prompts. It does not explicitly mention alternatives like get_prompt, but the scope is unambiguous for a simple list operation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Every tool has a clearly distinct purpose, with detailed descriptions that prevent ambiguity. Tools covering similar domains (e.g., multiple parliament search tools) are carefully differentiated by their search approach and input requirements.
All tools follow a consistent snake_case verb_noun pattern (e.g., bills_get_bill, case_law_search, citations_resolve). Even generic tools adhere to this style. No mixing of conventions.
With 35 tools, the count is on the higher side but appropriate for the wide-ranging domain of UK legal research. Each tool contributes to specific sub-domains (bills, case law, citations, committees, HMRC, legislation, parliament, votes) without unnecessary duplication.
The tool surface covers nearly all essential aspects of UK legal research: searching and retrieving bills, case law, and legislation; parsing and resolving citations; exploring committee evidence; accessing Hansard debates and member interests; and checking HMRC guidance and VAT rates. Gaps like full judgment text retrieval are mitigated by paragraph-level access and indexing.