AI-synthesized investment thesis for one estimate. Combines the analyst's raw thesis, their historical accuracy, and (if scored) the actual SEC filing result into a structured analysis: summary, bull/bear case, key assumptions, risks, and confidence assessment. Costs 0.75 USDC via x402. Not investment advice.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | estimate ID | |
| payment_token | No | base64 x402 payload |
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 discloses the cost ('0.75 USDC via x402') and the non-advice disclaimer, which are key behavioral traits. However, it does not explain what happens if the payment fails, whether the action is idempotent, or any rate limits. The 'x402' mechanism is mentioned but not explained, assuming domain knowledge.
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 four sentences that front-load the purpose and structured output components. Every sentence earns its place: the first defines the tool, the second lists what it combines, the third enumerates output sections, and the fourth states cost and disclaimer. No waste.
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 moderate complexity (synthesizes data from multiple sources) with no output schema. The description lists output components (summary, bull/bear case, etc.) but does not specify the format, structure, or how to interpret them. It also lacks error handling details. For a payment-gated tool, payment failure handling is omitted. Complete enough for basic use but leaves 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?
Schema description coverage is 100% (both parameters have descriptions). The tool's description does not add meaning beyond what the schema provides: 'id' is estimate ID, 'payment_token' is base64 x402 payload. The description rephrases 'estimate ID' for id but adds no new semantics. Given high coverage, baseline 3 is appropriate.
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 it produces an AI-synthesized investment thesis for one estimate, combining analyst thesis, historical accuracy, and SEC filing result. It distinguishes from sibling tools like read_estimate (which likely just displays raw data) and submit_estimate (which likely submits a thesis). The verb 'generates' and resource 'structured analysis' are specific.
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 mentions a cost of 0.75 USDC and notes it's not investment advice, which implies it should be used when a synthesized analysis is needed. However, it does not explicitly state when to use this tool versus alternatives like leaderboard or ticker_consensus, nor does it provide conditions where it should not be used. The usage context is implied but not fully explicit.
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.