get_agent_briefReturn the plain-text site brief describing scope, assumptions, the honesty clause, and the API contract. Mirrors the /agent.txt document served by the website.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / $schemaPrevious value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, covering safety. The description adds the fact that it 'mirrors /agent.txt served by the website' and lists content categories, which is useful context beyond annotations. No contradictions.
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 with no filler. First sentence states the action and content, second adds provenance. Perfectly front-loaded and efficient.
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 tool with no parameters and no output schema, the description fully specifies what it returns and its source. It is complete for the agent to understand usage and expected output.
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, so the schema is trivially 100% covered. With 0 params, the baseline is 4. The description adds no parameter details since none exist, but it doesn't need to – it accurately describes a no-input tool.
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's function: 'Return the plain-text site brief describing scope, assumptions, the honesty clause, and the API contract.' It specifies both the action (return) and the resource (site brief), and it is distinct from siblings which are computational/list tools.
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?
While the description doesn't explicitly state 'use when you need the brief' or mention alternatives, the context makes its usage obvious. No sibling tool provides agent briefs, so the intended usage is implicit. It lacks explicit exclusions but is clear enough.
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 role: forward expectation, inverse required error rate, multi-hardware comparison, fault-tolerant resource estimation, quantum volume rate, plus separate list/get/fit tools for hardware, historic data, algorithms, timings, and qLDPC codes. Cross-references in descriptions make selection unambiguous.
Tool names follow a predictable verb_noun pattern: compute_* for estimation operations, list_* for catalogs, get_* for retrieving specific data, plus compare_hardware_scenarios and fit_historic_series as clear descriptive exceptions. The naming style is consistent and readable.
Twelve tools is well-scoped for a quantum computing estimation and hardware comparison server. Each tool covers a distinct capability without redundancy, and the count feels appropriate for the domain's breadth.
The tool surface covers forward computation, inverse computation, multi-hardware comparison, fault-tolerant resource estimation, historical trends, current hardware catalogs, algorithm examples, hardware timings, and error-correction code data. There are no obvious dead ends; tools compose naturally to answer 'what hardware do I need and when will it exist?'.