Search X with full operators (e.g. '$AAPL lang:en -is:retweet', 'from:handle').
General-purpose X search with sentiment scoring; use analyst_views when the
user cares about specific accounts. Summarize results; don't echo tweets verbatim.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| latest | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It adds value by disclosing that the tool performs sentiment scoring and supports full operators. However, it does not mention rate limits or authentication, but for a search tool these are less critical. The instruction to summarize is a notable behavioral directive.
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?
Three sentences: the first gives concrete examples, the second states purpose and differentiation, the third gives an agent instruction. Every sentence earns its place with no wasted words.
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?
An output schema exists, so return value explanation is unnecessary. The description covers core behavior, differentiation, and agent instruction. However, parameter documentation is incomplete, and the tool's scope relative to other sibling tools like 'ticker_social_sentiment' is not addressed.
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 coverage is 0%, so description must compensate. The query parameter is implied through examples, but the 'limit' (default 20) and 'latest' (default true) parameters are not explained at all. No parameter details beyond the query examples are provided.
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 'Search X with full operators' and provides specific examples like '$AAPL lang:en -is:retweet'. It also mentions sentiment scoring, distinguishing it from sibling tools like analyst_views. The verb 'search' and resource 'X' are explicit.
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 explicitly tells when to use this tool versus the alternative: 'use analyst_views when the user cares about specific accounts'. It also instructs the agent on how to handle output: 'Summarize results; don't echo tweets verbatim'.
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.
Most tools have clearly distinct purposes (e.g., analyst_views fetches views, analyst_debate compares them, analyst_track_record scores accuracy). Some overlap exists between sentiment tools (stocktwits_symbol, ticker_social_sentiment) but descriptions clarify boundaries. Overall, an agent can differentiate them.
Naming is mostly lowercase with underscores, but conventions vary: some use prefixes (analyst_, direction_review_), some are single words (quote, leaderboard), and others are verb_noun (score_ticker, screen_stocks). This inconsistency makes patterns less predictable, though prefixes help group related tools.
With 24 tools, the server is slightly above the ideal range of 3-15 for coherence. While each tool seems justified for the financial analysis domain, the volume could be overwhelming. Some tools (e.g., tweet_store_stats, direction_review_batch) are operator-only, reducing the surface for typical agents.
The tool set covers core workflows: fetching analyst views, tracking accuracy, SEC fundamentals, insider activity, material events, live quotes, social sentiment, and screening. Gaps like earnings calendar or portfolio management are minor given the focus on analyst-driven analysis. The operator tools for direction review add internal completeness.