launches

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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: the tool automatically selects data sources and fills arguments, handles natural language questions, and returns results. It doesn't mention limitations like rate limits, authentication needs, or error conditions, but provides substantial operational context beyond basic purpose.

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 efficiently structured: first sentence states core purpose, second explains the mechanism, third provides usage guidance, and final sentence offers concrete examples. Every sentence adds value with zero redundant information, making it easy to parse and understand quickly.

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 (natural language processing with automatic tool selection) and lack of annotations/output schema, the description provides strong context about what the tool does and how to use it. It could benefit from mentioning response format or error handling, but the examples help illustrate expected usage patterns adequately.

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% with one parameter ('question') well-documented in the schema. The description adds minimal parameter semantics beyond the schema, only reinforcing that questions should be 'in plain English' or 'natural language.' This meets the baseline for high schema coverage.

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: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool, fills the arguments'). It distinguishes from siblings by emphasizing natural language input versus structured tool selection.

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 when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It contrasts with sibling tools that likely require specific parameters or tool knowledge, providing clear guidance on using this as a high-level query interface versus lower-level tool invocation.

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?

Despite having no annotations, the description discloses the type-specific data returned (revenue, net income, etc. for companies; adverse-event counts, FDA approvals, etc. for drugs) and mentions the output includes paired data and resource URIs. It does not contradict the input schema.

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 extremely concise, using two well-structured sentences. The main purpose is front-loaded, and every sentence adds value 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?

For a tool with two simple parameters and no output schema, the description explains the entity types, data fields, and efficiency benefit. It could be more specific about the exact format of 'paired data', but it is largely 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?

The input schema already has 100% descriptive coverage for both parameters. The description restates the parameter constraints without adding new semantics beyond what the schema provides, so the baseline score of 3 applies.

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 compares 2–5 entities side by side in one call, specifying two entity types and the data fields returned for each. It also highlights that it replaces 8–15 sequential calls, distinguishing it from other tools on the server.

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 a clear usage context: use this tool to compare multiple entities efficiently instead of making many separate calls. However, it does not explicitly mention when not to use it or compare it to specific alternatives.

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?

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it's a search operation that returns relevant tools with names and descriptions, and it's intended for initial discovery. However, it lacks details on rate limits, error handling, or response format, which are important for a tool with no output schema.

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 appropriately sized and front-loaded, with two sentences that efficiently convey purpose and usage guidelines without wasted words. Every sentence adds clear value, making it highly 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 complexity (a search tool with 2 parameters, no output schema, and no annotations), the description is mostly complete. It covers purpose and usage well but lacks details on behavioral aspects like response format or limitations, which would be helpful since there's no output schema. It's adequate but has 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 100%, so the schema already documents both parameters (query and limit) thoroughly. The description does not add any parameter-specific information beyond what's in the schema, such as examples or constraints not covered. Baseline 3 is appropriate as the schema does the heavy lifting.

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 with specific verbs ('search', 'returns') and resource ('Pipeworx tool catalog'), and explicitly distinguishes it from sibling tools by emphasizing its role in finding tools among 500+ available options, which is distinct from the launch-related siblings listed.

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 explicit usage guidelines: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This clearly indicates when to use it (as an initial step for tool discovery in large catalogs) and implicitly suggests alternatives (sibling tools like search_launches for more specific queries) by context.

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?

No annotations provided, so description carries full burden. It describes returned data types and citation URIs, and notes it replaces 10-15 sequential calls. It does not disclose rate limits, authorization, or error conditions, but for a read-only aggregation tool, this is adequate.

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?

Two sentences, front-loaded with purpose. The second sentence is a bit long but packs key information. No wasted words. Some minor structural improvement possible, but overall concise.

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 tool with 2 parameters and no output schema, the description is highly complete. It explains what data is included, the return format (citation URIs), and sets expectations by noting it replaces 10-15 calls. Also provides an alternative use case for federal contracts.

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?

Input schema has 100% coverage with descriptions for both parameters. The description adds minimal extra value beyond the schema, except mentioning that 'value' can be ticker or CIK and noting the use of resolve_entity for names. Schema coverage is high, so 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 returns a full profile of an entity across Pipeworx packs, listing specific data types (SEC filings, revenue, patents, news, LEI). It distinguishes itself from siblings like compare_entities and resolve_entity by being a one-call aggregation.

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?

Explicitly says when to use (company profiles), when not (federal contracts – recommends usa_recipient_profile directly), and provides a prerequisite (use resolve_entity if you have a name). This gives clear context and alternatives.

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 burden for behavioral disclosure. It states this is a deletion operation, which implies destructive behavior, but doesn't specify whether deletion is permanent, reversible, requires specific permissions, or has side effects. For a destructive tool with zero annotation coverage, this is insufficient.

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, efficient sentence with zero waste. It's appropriately sized for a simple tool and front-loads the essential information (delete operation).

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 destructive operation with no annotations and no output schema, the description is incomplete. It doesn't explain what happens after deletion (success confirmation, error handling), whether there are constraints on deletable memories, or how this tool relates to sibling memory tools. The minimal description leaves significant 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%, so the schema already documents the single 'key' parameter. The description adds minimal value by restating 'by key' but doesn't provide additional context about key format, validation, or examples beyond what the schema provides.

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 action ('Delete') and resource ('a stored memory by key'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'recall' or 'remember' which likely interact with the same memory system, so it doesn't reach the highest score.

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 no guidance on when to use this tool versus alternatives. There's no mention of prerequisites, when deletion is appropriate, or what happens to deleted memories. Given sibling tools like 'recall' and 'remember', some context about usage distinctions would be helpful.

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 the full burden of behavioral disclosure. It describes the return data (name, net time, etc.), which adds value beyond the input schema. However, it lacks details on error handling, rate limits, authentication needs, or whether it's a read-only operation, leaving gaps in behavioral context for the agent.

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 front-loaded with the core purpose in the first sentence, followed by a concise list of returned data. Every sentence earns its place by providing essential information without redundancy, making it efficient and well-structured for quick understanding.

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 low complexity (1 parameter, no output schema, no annotations), the description is mostly complete. It explains what the tool does and what data it returns, but without an output schema, it could benefit from more detail on the return format or error cases. The lack of annotations means the description should cover more behavioral aspects, which it partially does but not fully.

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 description coverage is 100%, so the input schema already fully documents the 'id' parameter. The description does not add any additional meaning or context beyond what the schema provides, such as examples of valid IDs or usage notes, resulting in a baseline score of 3.

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 specific action ('Get full details') and resource ('a specific launch by its Launch Library 2 ID'), and distinguishes it from siblings by focusing on individual launch details rather than lists or searches. It explicitly mentions the data returned, which helps differentiate its purpose from the sibling tools that handle multiple launches.

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 specifying 'by its Launch Library 2 ID', suggesting this tool is for retrieving details when you have a specific launch ID. However, it does not explicitly state when to use this tool versus alternatives like get_past_launches or search_launches, nor does it provide exclusions or prerequisites, leaving some ambiguity for the agent.

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?

No annotations are provided, so the description carries full burden. It discloses what data is returned (name, net launch time, etc.) but lacks behavioral context like rate limits, authentication needs, pagination behavior, error conditions, or whether this is a read-only operation. The description doesn't contradict annotations (none exist).

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?

Two concise sentences: one stating the action and source, another listing returned fields. Efficiently front-loaded with core purpose. Could be slightly improved by integrating return details more smoothly.

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 simple read operation with 1 optional parameter and no output schema, the description is adequate but has gaps. It specifies the data source and return fields, but lacks context on limitations, errors, or sibling tool differentiation. Without annotations or output schema, more behavioral transparency would be beneficial.

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%, so the schema already documents the 'limit' parameter with its default. The description adds no parameter-specific information beyond what's in the schema. Baseline 3 is appropriate when schema does the heavy lifting.

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 past rocket launches from Launch Library 2' with specific verb+resource. It distinguishes from 'get_upcoming_launches' by specifying 'past' launches, but doesn't explicitly differentiate from 'get_launch' (singular) or 'search_launches'.

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?

No guidance on when to use this tool versus alternatives. The description doesn't mention when to choose this over 'get_launch' (singular), 'get_upcoming_launches', or 'search_launches'. Only implicit context from 'past' in the name.

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 of behavioral disclosure. It mentions the return fields (name, net launch time, etc.) but lacks details on permissions, rate limits, pagination, or error handling. For a read operation with external data, this is a significant gap 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, efficient sentence that front-loads the purpose and key return data. It avoids redundancy but could be slightly more structured by separating usage context from output details.

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 annotations and no output schema, the description provides basic purpose and return fields but misses behavioral aspects like data freshness, source reliability, or error cases. It's adequate for a simple read tool but lacks depth for robust agent use.

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%, with the single parameter 'limit' documented in the schema. The description adds no parameter-specific information beyond implying a default behavior for upcoming launches, so it meets the baseline of 3 without compensating for any gaps.

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 action ('Get upcoming rocket launches') and the data source ('from Launch Library 2'), distinguishing it from sibling tools like get_past_launches. It specifies the verb (get) and resource (upcoming rocket launches), though it doesn't explicitly contrast with search_launches in scope or method.

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?

No guidance is provided on when to use this tool versus alternatives like get_past_launches or search_launches. The description implies it's for upcoming launches but doesn't specify contexts, exclusions, or prerequisites, leaving usage decisions ambiguous.

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 the full burden. It discloses the rate limit (5 per day) and provides guidance on what to include (context of tools/data) and what to exclude (end-user prompt verbatim). This gives the agent clear behavioral expectations beyond just 'sending feedback'.

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, each delivering essential information: purpose, use cases, content guidance, and rate limit. No redundant text, and the most critical info (purpose) is first. Every sentence earns its place.

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 has 3 parameters (one nested object), no output schema, and no annotations, the description covers usage context, rate limits, and parameter semantics well. It's complete enough for an agent to use correctly, though it could mention if there's a response or confirmation after sending.

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 100% description coverage, so the parameters are well-documented. The description adds value by reinforcing that the message should be specific and typical length (1-2 sentences, 2000 chars max) and warns against including the user's prompt verbatim. This goes beyond the schema's field descriptions.

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 explicitly states 'Send feedback to the Pipeworx team' and lists specific categories (bug, feature, data_gap, praise). This clearly distinguishes it from sibling tools like ask_pipeworx or discover_tools, which have different purposes.

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 says 'Use for bug reports, feature requests, missing data, or praise' and mentions a rate limit of 5 messages per day. However, it does not explicitly state when not to use this tool or recommend alternatives, though the use cases are clear.

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 the full burden of behavioral disclosure. It effectively describes the tool's behavior: retrieving memories by key or listing all memories, with persistence across sessions. However, it doesn't mention error handling (e.g., what happens if key doesn't exist) or performance characteristics.

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 perfectly concise with two sentences that each serve distinct purposes: the first explains the core functionality, the second provides usage context. There's no wasted language 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?

For a simple retrieval tool with good schema coverage but no output schema, the description provides adequate context about what the tool does and when to use it. However, without an output schema, it doesn't describe the return format (e.g., structure of retrieved memories), leaving some ambiguity.

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 100% description coverage, so the baseline is 3. The description adds meaningful context by explaining the semantic difference between providing a key (retrieve specific memory) and omitting it (list all keys), which goes beyond the schema's technical specification.

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 with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes this tool from siblings like 'remember' (which stores) and 'forget' (which deletes) by focusing on retrieval operations.

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 explicit guidance on when to use this tool: 'to retrieve context you saved earlier in the session or in previous sessions.' It also specifies when to omit the key parameter ('omit key to list all keys'), giving clear operational instructions.

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 fully compensates by detailing the parallel fan-out behavior, accepted since formats (ISO date or relative), return structure (changes, total_changes, pipeworx:// URIs), and the fact that type is currently limited to 'company'. This exceeds the burden for a tool without annotations.

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 compact (4 sentences) but rich: it starts with purpose, then enumerates the fan-out, parameter formats, return fields, and use case. 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?

Given no output schema and no annotations, the description covers input, process, and output adequately. It explains what the tool does, how it does it (parallel fan-out), what parameters mean, and what the response contains. This is fully 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 coverage is 100%, but the description adds significant value: explains that 'since' accepts ISO date or relative ('7d', '30d', '3m', '1y') with recommended defaults, clarifies 'value' as ticker or CIK, and notes that 'type' only supports 'company' today. This enhances the schema descriptions.

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?

Description clearly states 'What's new about an entity since a given point in time' and specifies the entity type 'company' with fan-out to multiple sources (SEC EDGAR, GDELT, USPTO). This differentiates it from sibling tools like entity_profile (static profile) or compare_entities (comparison), giving a specific and distinct purpose.

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?

Explicitly recommends use for 'brief me on what happened with X' or change-monitoring workflows. It lacks explicit when-not-to-use guidance or alternatives, but the use case is clearly stated, providing adequate context for an agent to decide.

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 the full burden of behavioral disclosure. It does an excellent job describing key behavioral traits: it explains the persistence model (authenticated vs. anonymous), storage duration (24 hours for anonymous), and the cross-tool context capability. The only minor gap is lack of information about storage limits or potential errors, but overall it provides substantial behavioral context beyond basic functionality.

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 perfectly concise with just two sentences that each earn their place. The first sentence states the core functionality with examples, and the second provides critical behavioral context about persistence. There's zero wasted language, and the most important information is front-loaded.

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 2-parameter tool with no annotations and no output schema, the description provides excellent context about what the tool does, when to use it, and key behavioral characteristics. The only minor gap is the lack of information about return values or potential errors, but given the tool's relative simplicity and the comprehensive behavioral disclosure, this is nearly 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 description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema. However, it does provide context about what types of values are appropriate to store ('intermediate findings, user preferences, or context'), which gives semantic meaning to the 'value' parameter. Baseline 3 is appropriate when schema does the heavy lifting.

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 specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'recall' (likely for retrieval) and 'forget' (likely for deletion). It provides concrete examples of what can be stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous and well-differentiated.

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 when to use this tool ('to save intermediate findings, user preferences, or context across tool calls') and provides clear context about persistence differences ('Authenticated users get persistent memory; anonymous sessions last 24 hours'). This gives the agent specific guidance on appropriate use cases and important behavioral constraints.

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?

No annotations provided, so description carries full burden. Discloses return fields and that it's a single call, but does not explicitly state it is read-only or address potential side effects, 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?

Extremely concise (three sentences) with all key information front-loaded. Every sentence serves a purpose: goal, input formats, output contents, and benefit.

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?

Covers purpose, inputs, and outputs well given no output schema. Mentions return fields and resource URIs. Could improve by noting error handling or partial match 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?

Schema has 100% coverage with descriptions for both parameters. Description adds value by explaining accepted formats for 'value' and clarifying that 'type' is limited to 'company'. Examples further clarify usage.

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?

Description uses specific verb 'resolve an entity to canonical IDs' and clearly identifies the resource (Pipeworx data sources). It distinguishes itself from sibling tools which are all different (e.g., ask_pipeworx, get_launch).

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?

Explicitly states it replaces 2-3 lookup calls and gives specific input examples (ticker, CIK, name). Missing explicit when-not-to-use but context is clear.

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 burden. It discloses the return format (matching launches with specific fields) but lacks critical behavioral details: it doesn't mention pagination, rate limits, authentication needs, error handling, or whether results are sorted. For a search tool, this is a significant gap 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 efficiently structured in two sentences: the first states the purpose and search scope, the second specifies the return format. Every word earns its place with no redundancy or fluff, making it easy to parse quickly.

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 moderate complexity (search with two parameters), no annotations, and no output schema, the description is partially complete. It covers the basic purpose and return fields but omits behavioral aspects like result limits, sorting, or error cases. It's adequate for minimal use but lacks depth for robust agent 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 description coverage is 100%, so the schema already documents both parameters fully. The description adds minimal value beyond the schema: it mentions searchable fields (rocket name, mission name, agency) which helps interpret 'query', but doesn't provide additional syntax or format details. This meets the baseline for high schema coverage.

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: 'Search launches by keyword' with specific searchable fields (rocket name, mission name, agency, etc.). It distinguishes from sibling tools by focusing on keyword search rather than retrieving specific launches (get_launch) or time-based lists (get_past_launches, get_upcoming_launches). However, it doesn't explicitly name these alternatives for full differentiation.

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 through 'Search launches by keyword' and the examples, suggesting it's for finding launches matching search terms rather than retrieving by ID or time. However, it lacks explicit guidance on when to use this versus siblings like get_past_launches for chronological listings or get_launch for specific IDs, leaving some ambiguity.

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 full burden. It discloses the return elements (verdict, structured form, actual value with citation, percent delta) and notes it replaces sequential calls, indicating it is a composite tool. It does not mention permissions, rate limits, or edge cases, but the covered aspects are sufficient for a read-only fact-check tool.

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 with no filler. The first sentence front-loads the core purpose, and the second adds crucial details about supported domains, return values, and efficiency gain. Every sentence earns its place, making it highly 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 low complexity (one parameter, no nested objects, no output schema), the description covers the main use case, input examples, and return format. It could be more complete by mentioning any prerequisites or limitations (e.g., US public companies only), but it is sufficiently complete for its simplicity.

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 single parameter 'claim' has 100% schema description coverage, and the tool description adds further context about acceptable claim types (e.g., revenue, net income) and format examples. This exceeds the schema's minimal description, providing good meaning beyond what the schema offers.

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 fact-checks natural-language claims against authoritative sources, specifically company-financial claims from SEC EDGAR+XBRL. It lists the possible verdicts and differentiates from siblings by noting it replaces 4-6 sequential calls, making the purpose highly specific and distinguishable.

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 the supported domain (company-financial claims for public US companies) and gives example claims, providing clear when-to-use guidance. However, it does not explicitly state when not to use this tool or mention alternatives among siblings, which would improve clarity.

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