iposignal

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the burden and does well: it discloses data sources (SEC XBRL, latest market close), the computation basis (TTM fundamentals), and that the response includes the computation convention. It lacks details on limitations or error conditions, but for a read-only valuation tool this is solid coverage.

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 long but information-dense, with each clause earning its place. It front-loads the core purpose, then lists sources, outputs, parameter usage, and an alternative endpoint. The use of dashes and parentheticals keeps related details together without fluff.

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 complexity (multiple metrics, no output schema), the description is exceptionally complete. It enumerates all key outputs (multiples, derivations, margins, growth), explains the data sources, states the computation convention is included in the response, and clarifies parameter usage. This is sufficient for an agent to select and invoke the tool correctly.

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 descriptions are absent (0% coverage), so the description compensates by explaining the two parameters: 'symbol (any SEC-mapped ticker)' and 'companyName (resolves Nasdaq/NYSE listings)' and their mutual exclusivity ('exactly one'). This adds meaningful guidance beyond the raw schema.

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: 'Get live valuation metrics for any US-listed company.' It specifies the verb (get), the resource (valuation metrics), and the scope (any US-listed company), and distinguishes from IPO-focused siblings by adding '(not just IPOs)' and noting it is used for peer benchmarking.

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 context by stating 'any US-listed company (not just IPOs)' and mentioning peer benchmarking, which suggests when to use it over IPO-specific tools. However, it does not explicitly name alternatives or state when not to use it, so it stops short of full guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the output type (sentiment score), range (-100 to +100), and trend data behavior ('for the last N days'). It does not mention rate limits or auth, but for a read-only market data tool, the core behavioral traits are well covered.

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 concise and front-loaded with the core purpose and output. The final sentence about the paid HTTP endpoint is slightly extraneous for an MCP tool selection context, but it does not significantly detract from clarity.

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 is simple (one optional parameter, no output schema, no annotations), and the description provides the necessary context: what the score means, range, trend, and intended use cases. It stops short of detailing exact response structure, but given the low complexity, it is sufficiently complete.

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 the description must compensate. It does by connecting the 'days' parameter to 'trend data for the last N days', giving meaning beyond the schema's default/min/max. Since there is only one parameter and the schema already defines its bounds, this is adequate.

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 identifies the tool as accessing IPOSignal's proprietary market sentiment score, with a specific verb ('Access') and resource. It distinguishes itself from the sibling get_ipo_social_sentiment by focusing on 'market sentiment' rather than social sentiment, and includes the -100 to +100 range.

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 provides clear use cases: identify favorable IPO windows, time investment entries, and assess market appetite. It implies when to use the tool but does not explicitly state when not to use it or name alternative tools, so it falls just short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses that results are 'when available' for borrow fee and AI-generated research, setting accurate expectations. It also mentions the paid HTTP endpoints as an alternative access method. It does not cover error handling or rate limits, but for a read tool the key behaviors around data availability and access are addressed.

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 two sentences, both dense with relevant information. The first sentence lists the comprehensive data set, the second gives usage constraints and alternative endpoints. While it is a wall of text without bullet points, it is still concise and every clause adds value.

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 complexity (a full IPO profile with many data fields) and the absence of an output schema, the description is remarkably complete. It lists specific content areas, notes optional data via 'when available', specifies the input constraint, and even provides HTTP endpoint alternatives. This gives an agent enough context to understand the tool's capabilities and limitations.

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 schema has three parameters with 0% description coverage, so the description must compensate. It does by stating that exactly one of companyId, symbol, or cik should be provided, clarifying they are mutually exclusive identifiers. It also maps symbol and id to alternative HTTP endpoints, giving extra context. However, it does not elaborate on the meaning or format of each identifier, leaving some ambiguity.

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 verb 'Retrieve' and the resource 'complete IPO company profile', enumerating specific content areas (deal terms, pricing range, SEC registration, etc.). This distinguishes it from sibling tools like get_company_metrics and get_ipo_sentiment, which focus on narrower data.

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 provides clear context on what the tool returns, allowing an agent to infer it for full-profile needs. It explicitly instructs 'Provide exactly one of companyId, symbol, or cik,' which is an important usage rule. However, it does not explicitly state when not to use this tool or name alternatives beyond implying their existence through sibling names.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full disclosure burden. It reveals the output composition, the aggregate-only behavior, and the one-of parameter constraint. This is strong transparency for a read-only sentiment tool, though it omits rate limits or error 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?

The description is three focused sentences: first introduces the function and output, second clarifies the scope of returns, third states the parameter requirement and alternative endpoint. Every sentence earns its place without 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?

In the absence of an output schema, the description adequately explains return values (score range, label, post tallies, summary). It also conveys the critical parameter constraint. It does not cover error handling or authentication, but these are less critical for a simple fetch operation.

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 the description's 'Provide exactly one of companyId, symbol, or cik' adds essential semantics beyond the bare schema. It clarifies that the three string parameters are alternative identifiers, even if it doesn't detail each parameter's format.

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 function: obtaining IPOSignal's X/Twitter social-sentiment reading for an IPO. It details the output metrics (score, label, tallies, summary) and explicitly notes it returns only the aggregate signal, not posts, which distinguishes it from sibling tools.

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 provides clear context for when to use the tool ('for a specific IPO') and instructs to 'Provide exactly one of companyId, symbol, or cik,' which is essential usage guidance. However, it does not explicitly mention alternatives or exclusion criteria relative to sibling tools like get_ipo_sentiment.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.