spacex

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

Without annotations, the description carries full burden and provides good behavioral context: it explains that Pipeworx picks tools and fills arguments automatically, handles natural language questions, and returns results. It doesn't mention rate limits, authentication needs, or error handling, but covers core functionality well.

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?

Perfectly structured with a clear purpose statement, usage guidance, and illustrative examples in three concise sentences. Every element adds value without redundancy, making it easy to 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?

For a single-parameter tool with no annotations or output schema, the description provides excellent context about functionality and usage. It could mention response format or error cases, but covers the essential aspects well given the tool's 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?

With 100% schema description coverage, the baseline is 3. The description adds meaningful context by explaining that the parameter should be a 'question or request in natural language' and provides concrete examples, enhancing understanding beyond the schema's basic type definition.

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 ('Ask a question', 'get an answer') and resources ('best available data source'), distinguishing it from sibling tools like data retrieval or memory tools by emphasizing natural language processing and automated 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?

It explicitly states when to use this tool ('No need to browse tools or learn schemas — just describe what you need') and provides clear examples of appropriate questions, effectively guiding users away from more specialized sibling tools.

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 discloses output type ('paired data + resource URIs') and source (SEC EDGAR for companies, FDA for drugs). However, it does not mention whether the operation is read-only, permissions needed, or rate limits, leaving gaps in behavioral understanding.

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?

Description is three sentences, front-loaded with main purpose, and every sentence adds value without redundancy. It is 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 no output schema, description explains return values (paired data + resource URIs) and lists specific fields for each type. It lacks detail on the structure of 'paired data' but is sufficient for a comparison tool. Missing depth but adequate.

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%, but description adds significant value by explaining what each enum value for 'type' yields (e.g., for company: revenue, net income, etc., from SEC EDGAR) and clarifies 'values' format per entity type. This enriches parameter meaning beyond 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?

Description clearly states verb 'compare' and resource 'entities' with a specific scope of 2-5 entities. It further differentiates from sibling tools by specifying it replaces 8-15 sequential calls, and sibling tools like ask_pipeworx, discover_tools, etc., are unrelated to comparison.

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?

Description provides clear when-to-use context: comparing 2-5 entities side-by-side. It implies efficiency by replacing sequential calls. While it doesn't explicitly state when not to use, the specificity of comparison against unrelated siblings makes guidance adequate.

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: it's a search operation that returns relevant tools based on a query, with an implied read-only nature (no mention of mutations). However, it lacks details on error handling, rate limits, or authentication needs, which would be beneficial for a tool discovery function.

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 usage guidelines. Every sentence earns its place by providing essential information without redundancy, making it highly 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 complexity (a search function with 2 parameters) and lack of annotations or output schema, the description is mostly complete. It covers purpose, usage, and basic behavior, but could improve by mentioning output format or error cases. However, it adequately supports tool selection and invocation in most contexts.

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 documents both parameters (query and limit) thoroughly. The description does not add any additional meaning or context beyond what the schema provides, such as examples of effective queries or usage tips. Thus, it 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 with specific verbs ('Search the Pipeworx tool catalog') and resource ('tool catalog'), distinguishing it from sibling tools that focus on space-related data (e.g., get_crew, get_rockets). It explicitly mentions what it returns ('most relevant tools with names and descriptions'), making the purpose unambiguous and distinct.

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: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This includes a specific condition (500+ tools) and a clear alternative scenario (finding tools for a task), with no misleading or vague 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?

No annotations are present, so the description carries the full burden. It discloses that the tool returns pipeworx:// citation URIs and bundles 10–15 calls into one. It notes a performance reason ('too slow to bundle' for federal contracts). Missing details like rate limits or authorization requirements, but overall transparent.

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 dense but not excessively long. Each sentence adds substantive information. The 'Replaces 10–15 sequential agent calls' sentence could be integrated, but it's still efficient. Slightly verbose but 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?

No output schema exists, so the description must explain return format (pipeworx:// URIs, done). It covers the main components (SEC, XBRL, patents, news, LEI) and constraints (only company). Could mention error handling or unknown entities, but adequate for the complexity.

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% with both parameters described. The description adds value beyond the schema by clarifying accepted value formats (ticker or zero-padded CIK) and referencing resolve_entity for name inputs. This compensates for the schema's brevity.

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 ('Full profile') and resource ('entity across every relevant Pipeworx pack'), lists concrete data types (SEC filings, XBRL, patents, news, LEI), and explicitly differentiates from siblings like usa_recipient_profile for federal contracts. This makes the purpose unmistakable.

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 tells when to use this tool (for a full multi-source profile) and explicitly when not to ('For federal contracts call usa_recipient_profile directly'). It also hints at a prerequisite step ('use resolve_entity first if you only have a name'). Clear 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool deletes a stored memory, which implies a destructive mutation, but doesn't clarify whether deletion is permanent, reversible, or requires specific permissions. For a mutation tool with zero annotation coverage, this leaves critical behavioral traits (like side effects or authorization needs) unspecified.

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 directly states the tool's purpose without unnecessary words. It is front-loaded with the core action ('Delete'), making it immediately clear. Every part of the sentence earns its place by specifying the resource and mechanism ('by key').

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 destructive nature (deletion), lack of annotations, and absence of an output schema, the description is incomplete. It doesn't address what happens on success or failure, return values, error conditions, or safety considerations. For a mutation tool with no structured safety hints, the description should provide more context to guide safe usage.

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%, with the single parameter 'key' documented as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format, examples, or constraints. Since the schema already fully describes the parameter, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to given complete 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 specific action ('Delete') and the target resource ('a stored memory by key'), making the purpose immediately understandable. It distinguishes this tool from sibling tools like 'recall' (likely for retrieving memories) and 'remember' (likely for storing memories), establishing a clear role in memory management 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 no guidance on when to use this tool versus alternatives. While it implies deletion of memories, it doesn't specify prerequisites (e.g., whether the key must exist), error conditions, or relationships to sibling tools like 'recall' or 'remember'. The agent must infer usage context from the tool name and purpose alone.

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 mentions the return fields (name, agency, etc.), which is helpful, but lacks details on behavioral traits such as rate limits, error handling, pagination, or data freshness. For a read operation with zero annotation coverage, this is a significant gap.

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 and efficiently lists return fields in a single, well-structured sentence. There is zero waste, and every part of the description 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?

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but has clear gaps. It explains what is returned but lacks context on data scope (e.g., all historical crew or current), limitations, or error cases, which could be important for an AI agent.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, earning a baseline score of 4 for not adding unnecessary information 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 specific action ('List') and resource ('SpaceX crew members'), distinguishing it from sibling tools like get_rockets or get_starlink. It provides a complete picture of what the tool does without being tautological.

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 when information about SpaceX crew members is needed, but it does not explicitly state when to use this tool versus alternatives or provide any exclusions. There is no guidance on prerequisites or context for selection among siblings.

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 return data structure (launch name, date, etc.) and media links, which is useful behavioral context. However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions.

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, well-structured sentence that efficiently states the action, resource, and return values. Every element earns its place with zero waste.

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 zero-parameter tool with no annotations or output schema, the description provides complete context on what it does and returns. It could be slightly improved by mentioning data freshness or source, but it's largely adequate.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on output semantics without redundant parameter info, earning a baseline 4 for zero-parameter tools.

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 a specific verb ('Get') and resource ('most recent SpaceX launch'), and distinguishes it from siblings like 'get_next_launch' and 'get_past_launches' by specifying 'most recent'.

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 'most recent' launch, which helps differentiate it from 'get_next_launch' (future) and 'get_past_launches' (historical). However, it lacks explicit when-not-to-use guidance or named 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 the full burden of behavioral disclosure. It mentions the return values (launch name, date, details, rocket id), which adds useful context beyond basic functionality. However, it lacks details on error handling, rate limits, or data freshness, leaving some behavioral aspects unclear.

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, well-structured sentence that efficiently conveys the tool's purpose, scope, and return values without any wasted words. It is appropriately sized and front-loaded with essential information.

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 (0 parameters, no output schema, no annotations), the description is mostly complete, covering purpose and return values. However, without an output schema, it could benefit from more detail on the format or structure of the returned data, slightly reducing completeness.

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 tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the lack of inputs. The description does not add parameter information, but this is acceptable as there are no parameters to explain, warranting a baseline score above the minimum.

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'), resource ('next upcoming SpaceX launch'), and scope ('next upcoming'), distinguishing it from siblings like get_latest_launch or get_past_launches. It explicitly identifies what makes this tool unique in the context of 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 implies usage context by specifying 'next upcoming' launch, which helps differentiate it from get_latest_launch (which might refer to the most recent completed launch) and get_past_launches. However, it does not explicitly state when not to use this tool or name alternatives, keeping it from a perfect score.

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 that results are sorted by date descending and includes default behavior (limit defaults to 10), adding useful context. However, it doesn't cover potential rate limits, error handling, or authentication needs, leaving gaps for a read operation.

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 core purpose and includes key details like sorting and return fields. There is zero waste, and every part earns its place by adding 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?

For a simple read tool with one optional parameter and no output schema, the description is mostly complete, covering purpose, behavior, and return format. However, it lacks details on pagination or error cases, which could be useful given the absence of annotations and output schema.

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 additional parameter details beyond what the schema provides, such as range constraints or examples, but doesn't need to compensate heavily given the high 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 with specific verbs ('Get recent past SpaceX launches') and resources ('SpaceX launches'), distinguishing it from siblings like get_latest_launch or get_next_launch by specifying 'past' launches. It also details the return format, making the scope 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 implies usage for retrieving past launches, which differentiates it from siblings like get_next_launch (future) or get_rockets (different resource). However, it lacks explicit guidance on when not to use it or direct alternatives, such as comparing to get_latest_launch for only the most recent launch.

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 mentions the return fields but doesn't cover important aspects like whether this is a read-only operation, potential rate limits, authentication needs, or error handling. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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 core purpose ('List all SpaceX rockets') followed by specific return details. Every word earns its place with zero waste 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 tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but has clear gaps. It explains what data is returned but doesn't address behavioral aspects like read-only nature or potential constraints. For a basic list tool, it meets minimum viable standards but could be more 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 tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and it doesn't need to compensate for any schema 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 specific action ('List all SpaceX rockets') and resource ('SpaceX rockets'), distinguishing it from sibling tools like get_crew or get_latest_launch. It provides a precise verb+resource combination that leaves no ambiguity about what the tool does.

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 offers no guidance on when to use this tool versus alternatives like get_past_launches or get_starlink. It simply states what the tool does without mentioning any context, prerequisites, or exclusions, leaving the agent to infer usage based on tool names alone.

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 states the tool returns sorted data and specifies the data fields (object name, launch date, decay date), but it doesn't cover important aspects like whether it's read-only, potential rate limits, error handling, or data freshness. This is a significant gap for a tool with no annotation 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 concise and front-loaded, stating the core purpose in the first sentence. Both sentences add value: the first defines the action and sorting, the second specifies the data returned. There's no wasted text, though it could be slightly more structured for 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?

Given the tool's low complexity (one optional parameter, no output schema, no annotations), the description is minimally adequate. It covers what the tool does and what data it returns, but it lacks context on usage versus siblings, behavioral details, and output structure. For a simple read operation, this is borderline complete but has clear 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?

The input schema has 100% description coverage, with the 'limit' parameter clearly documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides, such as default behavior or constraints. Since schema coverage is high, 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: retrieving Starlink satellite information sorted by most recently launched. It specifies the resource (Starlink satellites) and the action (get info), though it doesn't explicitly differentiate from sibling tools like 'get_past_launches' or 'get_latest_launch' beyond mentioning Starlink specifically.

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. The description mentions sorting by most recently launched and returning spaceTrack data, but it doesn't clarify if this is for Starlink only, how it differs from 'get_past_launches' or 'get_latest_launch', or any prerequisites. This leaves usage context unclear.

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 discloses rate limiting (5 per identifier per day) and a privacy guideline (no verbatim prompts). For a feedback tool, these are key behaviors; however, it does not mention whether feedback is stored or confirmed.

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, front-loaded with purpose, no redundancy. Every sentence serves a clear purpose: what the tool does, how to use it, and constraints.

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 (3 params, no output schema), the description covers purpose, usage, and key constraints. It could mention expected outcome (e.g., feedback submitted), but overall it is sufficient for a feedback submission 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?

Schema coverage is 100%, so the schema documents parameters well. The description adds value by providing usage examples for 'type', character count limits for 'message', and notes 'context' as optional. This goes beyond the schema definitions.

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?

Describes the tool as 'Send feedback to the Pipeworx team' and lists specific use cases (bug reports, feature requests, missing data, praise), clearly distinguishing it from sibling informational 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?

States when to use (feedback types) and provides important usage tips: describe attempts in Pipeworx terms, omit end-user prompts, and note rate limit. Does not explicitly mention alternatives but context implies non-feedback queries belong to tools like ask_pipeworx.

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 that memories can be retrieved from 'earlier in the session or in previous sessions,' indicating persistence across sessions. However, it doesn't mention rate limits, authentication needs, error conditions, or whether the operation is read-only (though 'retrieve' implies it).

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 sentence states the purpose and parameter logic, while the second provides usage context. Every phrase adds value with zero wasted words, making it easy to parse.

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 (retrieval with optional listing), no annotations, and no output schema, the description is adequate but has gaps. It explains what the tool does and when to use it, but doesn't describe the return format (e.g., structure of retrieved memories or list), error cases, or session persistence details.

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 baseline is 3. The description adds value by explaining the conditional behavior: 'omit key' triggers listing all memories, which clarifies the parameter's optional nature and its effect on tool behavior beyond what the schema states.

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: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' It specifies the verb ('retrieve'/'list') and resource ('memory'), but doesn't explicitly differentiate from sibling tools like 'remember' or 'forget' beyond mentioning 'context you saved earlier.'

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: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' It also explains the conditional logic: 'omit key' to list all memories. However, it doesn't explicitly state when NOT to use it or name alternatives among siblings like 'discover_tools' or 'get_*' tools.

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 effectively carries the burden of disclosing behavior. It explains that for type='company' it fans out to multiple sources (SEC, GDELT, USPTO) in parallel, and describes the return format (structured changes, total_changes count, URIs). This provides meaningful behavioral insight beyond what annotations might offer. Minor gap: no mention of error handling or rate limits.

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 (three sentences) and front-loaded with the core purpose. Each sentence adds essential information: main function, detailed behavior for 'company' type, and return format. No redundant or extraneous content.

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 three parameters, no output schema, and multiple sub-sources, the description covers purpose, usage, parameter details, and behavioral fan-out. It is fairly complete, though it could optionally mention that the tool is read-only (likely safe). Overall, it provides sufficient context for correct invocation.

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 covers all three parameters with descriptions, but the description adds significant value: it specifies that 'since' accepts both ISO dates ('2026-04-01') and relative formats ('7d', '30d', '3m', '1y'), providing concrete examples. For 'value', it clarifies ticker or CIK usage. This enhances parameter understanding beyond the schema alone.

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: 'What's new about an entity since a given point in time.' It uses the verb 'brief me' and identifies the resource as entity changes. This distinguishes it from sibling tools like 'entity_profile' (static profile) and 'compare_entities' (comparison), making the purpose distinct and actionable.

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 recommends usage for 'brief me on what happened with X' or 'change-monitoring workflows,' providing clear context. While it lacks explicit 'when-not-to-use' or alternative tool suggestions, the context is sufficient for an AI agent to infer appropriate usage scenarios.

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 key behavioral traits: the persistence mechanism (authenticated users get persistent memory; anonymous sessions last 24 hours) and the tool's role in maintaining state across tool calls. This goes beyond the basic 'store' action to explain how the memory works in practice.

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 usage context and behavioral details. Every sentence earns its place by adding value—no wasted words. It's appropriately sized for a tool with two parameters and no annotations.

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 (2 parameters, no output schema, no annotations), the description is largely complete. It covers purpose, usage, and behavioral context well. However, it lacks details on error cases (e.g., what happens if the key already exists) or return values, which would be needed for a perfect score without an output schema.

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 (key and value). The description does not add any additional semantic meaning beyond what the schema provides (e.g., it doesn't explain constraints or usage patterns for parameters). Baseline 3 is appropriate when 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 specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'recall' (retrieval) and 'forget' (deletion). It provides concrete examples of what can be stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous.

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'), providing clear context. However, it does not mention when not to use it or name specific alternatives among siblings (e.g., 'recall' for retrieval), which prevents a perfect score.

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 no annotations, the description discloses input formats, output fields (ticker, CIK, name, URIs), and that it's a single call. It does not explicitly state it's read-only, but that is implied by the resolve semantics.

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, well-structured, with the key purpose upfront. Each sentence adds value, and there is no redundant information.

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 lack of output schema, the description fully explains return values and provides enough context for an agent to use the tool correctly. It covers input, output, and versioning.

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 description adds concrete examples (AAPL, 0000320193, Apple) and clarifies the type parameter's v1 limitation, going beyond the schema's enum and description.

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 resolves an entity to canonical IDs across data sources in one call, with explicit input types and outputs. It distinguishes from siblings by noting it replaces multiple lookup calls.

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 explains it accepts ticker, CIK, or name and replaces 2-3 lookup calls, providing clear context for when to use it. However, it could more explicitly state when not to use or list 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. It details the return format (verdict, extracted form, actual value with citation, percent delta) and scope limitations (v1 supports only company-financial claims). No contradictions.

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 sentences, front-loaded with the core action, and every sentence adds essential information. It is concise and structured effectively.

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 a single parameter and no output schema, the description covers purpose, scope, output format, and limitations. It is fully complete for an agent to understand and 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?

Schema coverage for the single parameter 'claim' is 100%, and the description adds value by providing concrete examples of claim formats (e.g., "Apple's FY2024 revenue was $400 billion"), beyond the schema's generic description.

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 fact-checks natural-language claims against authoritative sources, specifically company-financial claims via SEC EDGAR + XBRL. It distinguishes itself from sibling tools by detailing its specialized function and outputs.

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 explains when to use the tool (for fact-checking financial claims) and notes it replaces multiple sequential agent calls. However, it does not explicitly state when not to use it or mention alternative tools, leaving some ambiguity.

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