get_consensusWhether the independent actors this platform tracks — insiders, Congress, institutions, the model — agree on one ticker, with the per-dimension breakdown that produced the verdict.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | Ticker symbol, e.g. AAPL. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint=true already provided by annotations, the description adds context by explaining what 'consensus' means (agreement among insiders, Congress, institutions, the model) and that a per-dimension breakdown is included. It does not introduce contradictions and provides meaningful behavioral context beyond the annotation.
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 a single, concise sentence that front-loads the core purpose and uses no wasted words. It clearly communicates the tool's function and output in an efficient manner.
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 low-complexity tool with one required parameter, a read-only annotation, and no output schema, the description adequately conveys what the tool does and what it returns. It could specify exact response fields, but the high-level 'verdict and per-dimension breakdown' is sufficient for an agent to understand the tool's behavior.
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 input schema fully describes the only parameter (ticker symbol, e.g., AAPL) with 100% coverage. The description adds no additional parameter-specific semantics, so the baseline score of 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 the tool's purpose: checking whether independent actors (insiders, Congress, institutions, the model) agree on a single ticker, and it mentions the per-dimension breakdown. This distinguishes it from siblings like rank_consensus (which likely ranks across tickers) and get_signal (which likely returns a single signal).
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 implies usage for a single ticker through 'on one ticker,' but it does not explicitly state when to use this tool versus alternatives like rank_consensus or get_signal, nor does it provide exclusions or when-not-to-use guidance.
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.
get_composite and get_consensus both return per-ticker consensus with dimension breakdowns, creating ambiguity; agents might call the wrong one. Other tools are clearly distinct, but this overlap requires extra care.
All tools follow a verb_noun pattern: get_ for single entities, list_ for collections, and rank/search/screen as action verbs. The pattern is consistent and predictable across the entire set.
15 tools is well-scoped for a market intelligence server, covering single-ticker queries, lists, discovery, and market-level signals without unnecessary bloat.
The surface covers core workflows: ticker resolution, consensus, evidence, trades, positioning, screening, and market signals. Minor gaps exist (e.g., historical consensus or direct ticker comparison), but they are not critical.