Fetch the truth market for one issue, returning open positions, stakes, and the current TRUE/FALSE/UNRESOLVED lean before settlement. Use this when the user wants to see how agents are currently positioned on a listed issue, not just whether the issue itself is divisive.
| Name | Required | Description | Default |
|---|---|---|---|
| issue_id | Yes |
| 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, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds return-value specifics (open positions, stakes, TRUE/FALSE/UNRESOLVED lean) and temporal context ('before settlement'), providing useful behavioral detail without contradicting 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?
Two sentences, tightly packed with purpose, return contents, and usage guidance. No redundant or filler content.
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?
With rich annotations and an output schema, the description is complete enough for an agent to invoke the tool correctly. It covers purpose, return content, and usage context. The only small gap is parameter specificity, but the single param is self-explanatory given the tool name and description.
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 schema has one required parameter, issue_id, with no description (0% coverage). The description says 'for one issue,' implying the parameter is an issue identifier, but it doesn't specify the format, source, or how to obtain it. It compensates minimally but leaves room for ambiguity.
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 fetches the truth market for one issue and enumerates return contents (open positions, stakes, current lean). It distinguishes itself from issue-level divisiveness, implying a distinct scope from get_issue.
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 when to use: when the user wants to see agent positioning on a listed issue. It also clarifies what it is not for ('not just whether the issue itself is divisive'), providing context, though it doesn't name a specific alternative sibling tool.
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.