Submit an earnings estimate (opinion). Free; reputation is the stake.
| Name | Required | Description | Default |
|---|---|---|---|
| metric | No | ||
| thesis | Yes | ||
| ticker | Yes | ||
| analyst | Yes | 0x address (Base) | |
| predicted | Yes | ||
| confidence | No | ||
| price_usdc | No | ||
| fiscal_year | Yes | ||
| fiscal_period | 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?
No annotations are provided, so the description carries the full burden. It only mentions 'Free; reputation is the stake,' which hints at behavioral traits but does not disclose specifics like what 'reputation' entails, what happens after submission, or any limits.
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 extremely concise: two sentences with no wasted words. It efficiently conveys the core purpose and a key behavioral note.
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 has 9 parameters, 6 required, no output schema, and low schema coverage, the description is highly incomplete. It lacks return-value information, error handling, or constraints (e.g., uniqueness of ticker+period+analyst), leaving the agent without sufficient context for correct invocation.
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 very low (11%). The description adds no parameter explanations, leaving many required and optional fields (e.g., 'thesis', 'predicted', 'confidence', 'price_usdc') without additional meaning beyond their names and types.
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 action ('Submit') and the resource ('earnings estimate (opinion)'). It adds context about being free and reputation-based. However, it does not differentiate from sibling tools like 'read_estimate' or 'list_estimates', which have distinct 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?
No guidance is provided on when to use this tool versus alternatives, nor any prerequisites or conditions. The description lacks explicit context about appropriate scenarios.
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 has a clearly distinct purpose: listing tickers, estimates, submissions, consensus, AI thesis, leaderboard, and verdict. No two tools overlap in function; even 'ai_thesis' and 'read_estimate' are differentiated by the synthesis aspect.
Naming is mixed: some tools use verb_noun (list_estimates, submit_estimate) while others are bare nouns (leaderboard, verdict). This inconsistency could confuse an agent expecting a uniform pattern, though the names are individually clear.
With 8 tools, the server covers the core workflows of an earnings oracle (browsing, analyzing, submitting, evaluating) without excessive or insufficient tools. The count is well-scoped for the domain.
The tool surface appears complete: discovery (list_tickers), data retrieval (list_estimates, ticker_consensus, read_estimate, ai_thesis), contribution (submit_estimate), and post-earnings analysis (verdict) plus reputation (leaderboard). No obvious gaps.