uselessfacts

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

With no annotations provided, the description carries full burden of behavioral disclosure. It reveals key behaviors: the system automatically selects tools and fills arguments, handles various data sources, and returns results. However, it doesn't mention limitations like response time, accuracy guarantees, or error handling. The description doesn't contradict any annotations since 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?

The description is efficiently structured: first sentence states core functionality, second explains the automation benefit, third provides usage guidance, and fourth gives concrete examples. Every sentence adds value without redundancy. The text is appropriately front-loaded with the most important information first.

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 output schema and no annotations, the description provides good context about what the tool does and when to use it. The examples help illustrate scope. However, without output schema, some information about return format or potential result structures would be beneficial. The description compensates well but doesn't fully address output expectations.

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 'question' well-documented. The description adds minimal semantic value beyond the schema, only reinforcing that questions should be in 'plain English' or 'natural language.' This meets the baseline of 3 since the schema already provides adequate parameter documentation.

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 like discover_tools or recall by emphasizing natural language interface rather than structured queries.

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: 'No need to browse tools or learn schemas — just describe what you need.' It contrasts with alternatives by suggesting this is for natural language queries versus more structured approaches. The examples further clarify appropriate use cases like factual questions or document retrieval.

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 must convey behavioral traits. It details returned data (revenue, net income, etc. for companies; adverse events, approvals, trials for drugs) and mentions URI output. However, it omits potential side effects like rate limits or authentication needs.

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?

Four sentences, front-loaded with purpose, no wasted words. Each sentence adds distinct value: purpose, type details, output, efficiency.

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 or annotations, description covers purpose, input details, and output (paired data + URIs). Mentions efficiency improvement. Missing details on error handling or pagination, but adequate for agent understanding.

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. Description adds context: clarifies 'type' enum meaning and 'values' format (tickers/CIKs vs drug names), enriching understanding 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 states it compares 2-5 entities side by side, specifies data for 'company' and 'drug' types, and highlights efficiency (replaces 8-15 calls). Distinct from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Describes when to use for side-by-side comparison, mentions efficiency gain. Does not explicitly state when not to use, but context implies it's for multi-entity comparison.

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: the tool searches by natural language description and returns relevant tools with names and descriptions. However, it lacks details on rate limits, error handling, or authentication needs, which are important for a search tool in a large catalog.

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 and concise, with two sentences that each earn their place: the first explains the core functionality, and the second provides critical usage guidance. There is no wasted text, making it efficient and 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 (search functionality with 2 parameters) and no annotations or output schema, the description is fairly complete. It covers purpose, usage context, and basic behavior, but could improve by addressing potential limitations or response format details, which are not fully compensated for by the structured fields.

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 adds minimal value beyond the schema, mentioning 'describing what you need' which aligns with the query parameter but doesn't provide additional syntax or format details. 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 distinguishes it from siblings by emphasizing its role in discovering tools among many options. It explicitly mentions '500+ tools available', which sets it apart from simpler sibling tools like random_fact and today_fact.

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 (use this before others), with no misleading or missing exclusions.

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 must disclose behavior. It mentions returning pipeworx:// citation URIs and implies read-only operation. However, it does not explicitly state safety guarantees, rate limits, or whether data is cached. Minimal transparency beyond input and output format.

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: purpose, detailed contents, and an alternative. No wasted words. Front-loaded with the main action and resource. Highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given complexity (multi-source aggregation) and no output schema, the description lists data types and mentions return URI format. It provides clear scope but does not detail the exact structure of the returned profile, which might be needed for agent understanding. However, it is adequate for most use cases.

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 good descriptions. The description adds value by reinforcing that only 'company' is supported and advising to use resolve_entity for names. The extra guidance about ticker/CIK and name unsupported exceeds schema info.

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 returns a full profile of an entity across all relevant Pipeworx packs in one call, listing specific data sources (SEC filings, XBRL, patents, news, LEI). It distinguishes from siblings by noting it replaces 10-15 sequential calls and directs federal contract queries to usa_recipient_profile.

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 when to use (for full entity profiles) and when not (for federal contracts, use usa_recipient_profile). Provides guidance on input: names not supported, use resolve_entity first. This helps the agent decide correctly.

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 indicates a destructive operation ('Delete') but lacks details on permissions, reversibility, error handling, or side effects. For a mutation 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 a single, efficient sentence with zero waste. It is front-loaded and appropriately sized for a simple tool, 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 destructive nature, lack of annotations, and no output schema, the description is incomplete. It fails to address critical aspects such as confirmation prompts, success indicators, or error responses, leaving gaps in understanding the tool's 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?

The schema description coverage is 100%, with the parameter 'key' documented as 'Memory key to delete'. The description adds minimal value beyond this, merely restating the parameter's purpose without providing additional context like key format or examples.

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 resource ('a stored memory by key'), distinguishing it from sibling tools like 'remember' (create) and 'recall' (retrieve). It precisely communicates the tool's function without ambiguity.

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 like 'discover_tools' or 'random_fact', nor does it mention prerequisites or exclusions. It states what the tool does but not the context for its application.

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?

Discloses rate limiting (5 per day per identifier) and that feedback goes to the team. Since no annotations exist, this description carries full burden and adds useful constraints beyond the 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?

Concise single paragraph front-loading the purpose, followed by use cases, guidelines, and constraints. No unnecessary words or repetition.

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 all essential aspects: purpose, usage, parameter guidance, and rate limits. Lacks return value description, but for a feedback tool that is acceptable. No output schema exists, but the description sufficiently contextualizes 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 is 100%, but description adds value by specifying message format (1-2 sentences, 2000 chars max) and instructing to avoid embedding end-user prompts. Provides clarity beyond the schema's property 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?

Clearly states the tool sends feedback to the Pipeworx team and lists specific use cases (bug reports, feature requests, missing data, praise). Distinguishes from sibling tools like ask_pipeworx by focusing on feedback rather than queries.

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 when to use (bug reports, feature requests, etc.) and provides instructions (describe in terms of tools/data, avoid end-user prompt verbatim). Does not mention alternatives explicitly, but context makes the feedback purpose 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the fact is 'useless (but interesting)', which adds some context about content quality, but doesn't describe response format, potential errors, rate limits, or other behavioral traits. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

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 at 7 words, front-loading the core action ('Get a random...') with no wasted words. Every element ('useless (but interesting)') adds meaningful qualification 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 minimal. It covers the basic purpose and distinguishes from the sibling, but lacks details about return format, error conditions, or other behavioral aspects that would be helpful for an agent invoking this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the absence of parameters. The description appropriately doesn't add parameter information beyond what the schema provides, maintaining focus on the tool's purpose rather than unnecessary parameter details.

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 ('random useless (but interesting) fact'). It distinguishes the tool from its sibling 'today_fact' by emphasizing randomness rather than date-specific facts. However, it doesn't explicitly contrast with the sibling beyond the 'random' vs 'today' distinction.

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 when to use this tool (when you want a random fact) versus its sibling (when you want today's fact), but this is only through contextual inference rather than explicit guidance. No explicit 'when-not-to-use' or alternative scenarios are provided beyond the basic contrast.

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 stored memories across sessions, supporting both single-key retrieval and listing operations. It doesn't mention error handling, performance characteristics, or data format details, but provides solid operational context.

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 that are perfectly front-loaded: the first states the core functionality, the second provides usage context. Every word earns its place with zero redundancy or unnecessary elaboration.

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 and no output schema, the description provides excellent context about the tool's purpose, usage patterns, and behavioral characteristics. It doesn't describe the return format or error conditions, but given the tool's simplicity, it's reasonably 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 schema has 100% description coverage, so the baseline is 3. The description adds meaningful context by explaining the dual behavior: retrieving by key when provided, listing all memories when omitted. This clarifies the parameter's semantic impact beyond the schema's technical 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's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings like 'remember' (store) and 'forget' (delete) 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 distinguishes between two usage modes (retrieve by key vs. list all) and specifies when to omit the key parameter.

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 weight for behavioral disclosure. It transparently explains parallel fan-out to SEC, GDELT, and USPTO, the return structure (changes, count, URIs), and the read-only nature. Minor gaps exist in failure modes or rate limits, but overall the behavior is well described.

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 (5 sentences) with each sentence serving a purpose: purpose, special behavior for company, parameter details, return format, and use cases. No redundant text, and key 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 tool with no output schema, the description adequately explains the return value (structured changes, count, URIs). It covers the main use cases and parameter constraints. However, it does not detail the structure of changes or potential error conditions, leaving 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 coverage is 100%, so baseline is 3. The description adds value by clarifying 'since' format (ISO vs. relative with examples), 'value' as ticker or CIK, and the 'type' enum being limited to 'company'. This extra context goes beyond the schema without redundancy.

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 what's new about an entity since a given time. It details the specific behavior for company entities, including fanning out to three data sources. While it does not explicitly differentiate from sibling tools, the focus on temporal change is distinct enough.

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 usage context with examples like 'brief me on what happened with X' and change-monitoring workflows. It also explains the 'since' parameter formats. However, it does not offer guidance on when not to use this tool compared to siblings such as 'entity_profile' or 'compare_entities'.

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 and does well by disclosing important behavioral traits: it explains persistence differences (authenticated users get persistent memory, anonymous sessions last 24 hours) and the cross-tool context capability. It doesn't mention rate limits, size limits, or error conditions, but provides substantial operational context.

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 sized at two sentences, front-loaded with the core purpose, and every sentence earns its place. The first sentence states what the tool does, the second provides important behavioral context about persistence differences. 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 moderate complexity (storage operation with persistence variations) and no output schema, the description provides good context about what the tool does and behavioral characteristics. It could benefit from mentioning what happens on duplicate keys or storage limits, but covers the essential operational context well for a memory storage 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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add meaningful parameter semantics beyond what the schema provides - it mentions key-value storage generally but doesn't elaborate on key naming conventions, value constraints, or storage limitations beyond what's in 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?

The description clearly states the tool's purpose with specific verbs ('store a key-value pair') and resource ('in your session memory'). It distinguishes from siblings by specifying this is for storage (vs. recall, forget, etc.) and mentions specific use cases like saving intermediate findings, user preferences, or context across tool 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 provides clear context for when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but doesn't explicitly mention when not to use it or name alternatives. It implies usage scenarios well but lacks explicit exclusions or sibling tool comparisons.

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

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

With no annotations, the description carries the full burden. It discloses that the tool returns ticker, CIK, company name, and pipeworx:// URIs for stable citation, and that it is a single call. It does not mention error handling, rate limits, or auth requirements, but for a simple lookup tool, this is sufficient.

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 with no wasted words. The first sentence front-loads the core purpose, and the second adds essential details. 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?

For a tool with 2 parameters and no output schema, the description covers input and output well. It could be improved by mentioning that it is a read-only operation or any limitations, but it is largely 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?

Schema coverage is 100% with descriptions. The description adds value beyond the schema by providing concrete examples ('AAPL', '0000320193', 'Apple') and clarifying that v1 only supports 'company' type, which is already in the enum but reiterated with context.

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 Pipeworx data sources. It specifies the action ('Resolve'), resource ('entity'), and provides concrete examples ('ticker (AAPL), CIK, or name'). This distinguishes it from sibling tools like ask_pipeworx or discover_tools, which serve 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 implicitly guides when to use by stating it 'replaces 2–3 lookup calls' and lists accepted input types. However, it does not explicitly state when not to use or compare with siblings, leaving some ambiguity for an AI 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'useless fact', which hints at the content type, but doesn't describe response format, error handling, rate limits, or any other behavioral traits. This leaves significant gaps in understanding how the tool behaves beyond its 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 a single, efficient sentence that directly states the tool's purpose without any fluff or redundant information. It's appropriately sized and front-loaded, making it easy for an agent to parse and understand immediately.

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 annotations and output schema, the description is incomplete. It doesn't explain what the return value looks like (e.g., format, structure), error conditions, or how it differs from 'random_fact'. For a tool with no structured metadata, the description should provide more context to ensure proper 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 tool has 0 parameters, and the schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, so it meets the baseline expectation for a parameterless tool without introducing confusion or unnecessary details.

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 ('today's useless fact of the day'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling 'random_fact', which might offer similar functionality but with different temporal constraints or randomness.

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 its sibling 'random_fact'. It lacks any context about alternatives, prerequisites, or specific scenarios where this tool is preferred, leaving the agent to infer usage based on the name 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 describes the return values (verdict, extracted claim, actual value, citation, percent delta) and the efficiency gain. However, it lacks disclosure of potential limitations such as authentication requirements, rate limits, or behavior for unsupported claim types. The 'v1' mention implies limited scope but could be more explicit.

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, each serving a purpose: first states the core function, second narrows scope and data sources, third details the return. Information is front-loaded with the verb, and there is no redundant or extraneous text.

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 required parameter and no output schema, the description covers tool purpose, supported domains, data sources, and return structure. It includes examples of input and output. Minor gaps: no mention of error handling, unsupported claim types, or response format details. Still, it is fairly complete for 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?

Schema coverage is 100% for the single parameter 'claim', with a clear description and example in the schema. The tool description adds value beyond the schema by explaining the output components (verdict, citation, delta), which helps the agent understand the return format. However, the parameter itself is well-documented, so the description's additional contribution is moderate.

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: fact-check a natural-language claim against authoritative sources, specifically company-financial claims. The verb 'Fact-check' is specific, and the resource is well-defined with supported domains and sources. It also distinguishes itself from siblings by noting it replaces 4-6 sequential agent 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 specifies the context of use: company-financial claims (revenue, net income, cash for public US companies) via SEC EDGAR + XBRL. It implies when to use this tool and notes it replaces multiple sequential calls, providing clear usage context. However, it does not explicitly state when not to use or provide direct alternatives.

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