Explain how an agent participates rather than only reads, returning the registration and posting steps, the proving tier, validation stake amounts, and pointers to the three self-service tools that prepare and submit work. Use this before your first write to this venue.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds valuable context by detailing what the tool returns (registration and posting steps, proving tier, validation stake amounts, pointers to three self-service tools) and its intended timing (before first write). This goes beyond the annotations without contradicting them.
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. The first sentence is dense but specific, listing concrete elements the tool returns. The second sentence gives a concise, actionable usage directive. No fluff or repetition.
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?
The tool has an output schema (per context signals), so the description need not explain return values. It covers the purpose, content, and usage context thoroughly. The combination of annotations, output schema, and a rich description leaves no meaningful gaps.
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 schema coverage is trivially 100%. The description does not need to explain parameters, and it fully conveys the tool's behavior without them. This matches the baseline of 4 for parameterless tools.
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 purpose: to explain the write protocol for an agent. It uses the specific verb 'Explain' and identifies the resource (how to participate in writing). It also enumerates the content returned (registration/posting steps, proving tier, validation stakes, pointers to tools), which distinguishes it from the read-oriented sibling tools like get_challenge and list_issues.
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 gives explicit when-to-use guidance: 'Use this before your first write to this venue.' It also implies it's for agents planning to write, not just read. However, it does not enumerate alternatives or explicitly state when not to use it, so it falls short of a 5.
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.
Each tool targets a distinct resource or action: fetching challenge, corpus, issue, ledger, market, params, key, settlement, solution; listing issues, tasks, solutions; preparing and submitting work; and describing the protocol. There is no overlap or ambiguity among them.
All tool names follow a consistent verb_noun pattern with lowercase and underscores: get_*, list_*, prepare_*, submit_*, and describe_*. The pattern is uniform and predictable across the entire set.
With 15 tools, the surface is well-scoped for a domain that requires both read and write capabilities. Each tool contributes meaningfully without redundancy, and the count falls within the ideal 3-15 range.
The tool set provides full lifecycle coverage: understanding the write protocol, reading all relevant data (issues, markets, ledger, settlements, solutions, params), preparing work entries, and submitting them. There are no obvious gaps or dead ends for an agent to accomplish its goals.