iposignal
Server Details
Access premium IPO intelligence through AI agents. Retrieve detailed company profiles for upcoming and recent public offerings — including deal terms, SEC filings, AI-generated research with valuation models, competitor benchmarking, underwriter ratings, risk screening, and board analysis. Monitor overall market conditions with a proprietary daily sentiment score (-100 bearish to +100 bullish) with historical trend data to help time investment entries.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.8/5 across 3 of 3 tools scored.
Each tool has a clearly distinct purpose: get_ipo_sentiment provides a general market sentiment score for recent IPOs, get_ipo_snapshot gives a comprehensive profile of a specific IPO, and get_ipo_social_sentiment offers a social-media-based sentiment for a specific IPO. There is no overlap in functionality.
All tools follow a consistent 'get_ipo_<noun>' pattern using snake_case, which is predictable and easy to understand. The naming convention is uniform across the set.
With only 3 tools, the server is lightweight but covers core IPO data needs: general sentiment, detailed snapshot, and social sentiment. While it is slightly thin, it remains reasonable for a focused data provider.
The tool set covers sentiment and snapshot retrieval but lacks fundamental operations like listing upcoming IPOs or historical performance data. This gap may require agents to rely on external data for complete IPO research.
Available Tools
3 toolsget_ipo_sentimentAInspect
Access IPOSignal's proprietary market sentiment score — a daily signal quantifying how well recent IPOs are being received by investors. Ranges from -100 (extreme bearish) to +100 (extreme bullish) with trend data for the last N days. Use it to identify favorable IPO windows, time investment entries, and assess overall market appetite for new listings. Also available as a paid HTTP endpoint at /api/agent/ipo-sentiment.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It discloses the data type (daily signal, range, trend), but does not detail authentication or rate limits. However, it adds meaningful context beyond basic read behavior.
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 concise sentences front-load the core purpose and provide immediate actionable guidance. No filler or redundancy.
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 simple tool (one optional param, no output schema), the description covers the return type (score range) and trend data. It does not specify exact response structure but is sufficient for an agent to understand input and output scope.
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 only parameter 'days' has schema-compatible defaults and constraints, but the description only indirectly relates it to 'trend data for the last N days'. With 0% schema description coverage, the description partially compensates but lacks explicit linking of the parameter's effect.
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 specifies the tool returns a proprietary market sentiment score for IPOs with a defined range (-100 to +100) and trend data. It distinguishes itself from sibling tools (date management, snapshots) by focusing on sentiment analysis.
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 states use cases: identifying IPO windows, timing entries, assessing market appetite. It does not mention when not to use or alternatives, but the context of sibling tools makes the intended use clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_ipo_snapshotAInspect
Retrieve a complete IPO company profile — deal terms, pricing range, expected market cap, SEC registration and prospectus details, offering structure, lifecycle timeline, and IBKR indicative borrow fee rate when available. When available, includes AI-generated research with valuation models, competitor benchmarking, underwriter ratings, board analysis, and risk factors. Provide exactly one of companyId, symbol, or cik. Also available as a paid HTTP endpoint at /api/agent/ipo/{id} or /api/agent/ipo/by-symbol/{symbol}.
| Name | Required | Description | Default |
|---|---|---|---|
| cik | No | ||
| symbol | No | ||
| companyId | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully describes the returned data, including deal terms, pricing, market cap, SEC details, and AI-generated research. It is clearly a read operation with no destructive hints, though it lacks information on permissions or error handling.
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 paragraph that front-loads the purpose but includes some redundancy (e.g., 'when available' repeated) and additional HTTP endpoint info that could be separated. It is moderately concise but could be more efficient.
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 no output schema, the description thoroughly lists what the tool returns, including optional AI research. It covers the main use case well, though it omits response format or error scenarios. Overall, it provides sufficient context for an agent to use the tool.
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 has no descriptions (0% coverage), but the description adds meaning by stating the parameters are alternative identifiers and exactly one must be provided. However, it does not specify formats or types (e.g., CIK format, symbol conventions), leaving some semantic gap.
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 uses a specific verb 'Retrieve' and specifies a clear resource: 'complete IPO company profile'. It lists many specific components (deal terms, pricing, market cap, SEC details, AI research), distinguishing it from sibling tools that focus on sentiment.
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?
It explicitly states 'Provide exactly one of companyId, symbol, or cik', which is a clear usage guideline. However, it does not provide when-not-to-use or alternative tools for other use cases, but the sibling differentiation is implied by content difference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_ipo_social_sentimentBInspect
Get IPOSignal's X/Twitter social-sentiment reading for a specific IPO — an aggregate of how investors are discussing the stock on X, scored from -1 (bearish) to +1 (bullish) with a label, post tallies, and a one-line summary. Returns the aggregate signal only, not the underlying posts. Provide exactly one of companyId, symbol, or cik. Also available as a paid HTTP endpoint at /api/agent/social-sentiment.
| Name | Required | Description | Default |
|---|---|---|---|
| cik | No | ||
| symbol | No | ||
| companyId | No |
Tool Definition Quality
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 that the tool returns aggregate signal only, not underlying posts, and explains the score range. However, it does not mention safety aspects (e.g., read-only) or any potential side effects, rate limits, or authentication requirements, leaving gaps in transparency.
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 paragraph of about four sentences, front-loaded with the core purpose and output. It includes a brief mention of the paid HTTP endpoint, which is slightly tangential but not overly distracting. It is reasonably concise and well-structured.
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's simplicity and lack of output schema, the description covers the main return structure (score, label, post tallies, summary) and the input constraint. It is mostly complete, but it does not address error handling or the behavior when multiple identifiers are provided, which are minor 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 0%, so the description must compensate. It mentions that exactly one of companyId, symbol, or cik should be provided, which adds context to the three parameters. However, it does not specify the format of valid values (e.g., what constitutes a valid symbol or where to find companyId), leaving users with minimal guidance.
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 retrieves X/Twitter social sentiment for a specific IPO and explains the output format (score -1 to +1, label, post tallies, summary). However, it does not explicitly differentiate from sibling tools like get_ipo_sentiment, leaving some ambiguity about when to choose this tool over alternatives.
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 gives a key guideline: 'Provide exactly one of companyId, symbol, or cik.' It also notes that it returns aggregate signal, not underlying posts, implying that for detailed posts, another tool might be needed. However, it does not explicitly state when to use this tool versus siblings or what conditions are ideal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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