domains

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 explains that Pipeworx 'picks the right tool, fills the arguments, and returns the result,' which clarifies the automated tool selection and parameter filling behavior. However, it lacks details on limitations, error handling, or response format, 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?

The description is front-loaded with the core purpose, followed by operational details and examples. Every sentence adds value: the first defines the tool, the second explains the automation, the third provides usage guidance, and the examples concretely illustrate application. No wasted words 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 complexity (automated tool selection with one parameter) and lack of annotations or output schema, the description is moderately complete. It covers purpose, usage, and parameter semantics well, but omits details on response format, error conditions, or data source limitations, which could hinder full contextual 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?

The schema description coverage is 100%, so the schema already documents the single 'question' parameter. The description adds semantic context by specifying it should be 'in plain English' or 'natural language,' and provides examples that illustrate the expected format, enhancing understanding beyond the schema's basic 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 from data source'), and distinguishes it from siblings by emphasizing natural language interaction versus tool-specific operations like 'discover_tools' or 'search_domains'.

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

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

The description explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It provides clear alternatives (implicitly suggesting other tools for browsing or schema-based queries) and includes concrete examples like 'What is the US trade deficit with China?' to illustrate appropriate use cases.

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?

Annotations indicate safe read-only operation. Description adds detail: resolves market, classifies bet, fans out to packs, returns market-vs-model comparison. No contradictions; sufficiently transparent about behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is detailed but front-loaded with purpose, then covering process and use cases. Each sentence adds information, though slightly verbose. Well-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?

Despite no output schema, the description explains outputs (evidence packet, comparison) and classification categories. For a demo product, it provides sufficient context for an agent to understand functionality.

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% description coverage. Description adds value by explaining 'quick' vs 'thorough' depth and reemphasizing market input formats, complementing the schema's enum 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 it researches Polymarket bets using Pipeworx data, specifies input types (slug, URL, question text), and describes outputs (evidence packet, comparison). It distinguishes itself from sibling tools by focusing on bet-specific research.

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?

Explicit use cases are provided ('should I bet on X?', 'what does the data say?', 'is there edge?'). It positions itself as the core demo product. No explicit when-not-to-use or alternatives are mentioned, but the context is clear.

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

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

With no annotations, the description discloses key behaviors: data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), specific metrics retrieved, and return of paired data with citation URIs. It could mention error handling or rate limits, but overall provides sufficient 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 concise and well-structured, front-loading the core purpose and then expanding on usage and data sources. Every sentence adds value without redundancy.

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

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

Given no output schema, the description covers return values (paired data, citation URIs) and provides detailed context for both entity types. However, it does not specify the exact output format or structure, which would improve 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?

Schema coverage is 100%, and the description adds significant meaning: explains the 'type' parameter with enum values and what each type retrieves (e.g., revenue, net income for companies; adverse events, approvals for drugs). It also clarifies 'values' parameter with examples and constraints.

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 that the tool compares 2-5 companies or drugs side by side, with specific verbs and resources. It distinguishes itself from sibling tools like entity_profile or validate_claim by focusing on comparisons.

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

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

The description provides explicit usage cues (e.g., 'compare X and Y', 'X vs Y', 'how do X, Y, Z stack up') and mentions use cases like tables/rankings. However, it does not explicitly state when not to use the tool or mention alternatives among 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it's a search operation that returns relevant tools, and it should be called first in certain contexts. 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 perfectly concise with two sentences that each earn their place. The first sentence explains the core functionality, and the second provides crucial usage guidance. No wasted words, and the most important information (what it does) comes 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?

Given the tool's moderate complexity (search functionality with 2 parameters) and no output schema, the description provides good context about what the tool does and when to use it. However, without annotations or output schema, it could benefit from more information about return format or error handling.

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 any parameter-specific information beyond what's in the schema. The baseline score of 3 is appropriate when the schema does the heavy lifting for 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 with specific verbs ('search', 'returns') and resources ('Pipeworx tool catalog', 'most relevant tools with names and descriptions'). It distinguishes from the sibling tool 'search_domains' by specifying it searches the tool catalog rather than domains.

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

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

The description provides explicit usage guidance: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear context about when to use this tool versus alternatives, including a specific threshold (500+ tools) and a recommended sequence (first).

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 must fully disclose behavioral traits. It states it returns data but does not mention that it is a read-only operation, any rate limits, authentication requirements, or potential costs. This lack of transparency leaves important behavioral context missing.

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 consists of two concise sentences: the first defines the action, and the second enumerates return fields and scope. It is front-loaded and contains no extraneous information, making it efficiently scannable.

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 an output schema, the description compensates by listing the specific fields returned. However, it misses mentioning potential limitations like registration status, timeout, or contrast with sibling tools. For a simple lookup, it is mostly complete.

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

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

The input schema has 100% coverage with a single 'domain' parameter described as a domain name. The description adds value by stating 'Works for any TLD,' which expands the semantic context beyond the schema's minimal description, earning above the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves full registration details for a domain, listing specific data fields like registrar, registrant, dates, nameservers, DNSSEC, and status flags. It distinguishes from the sibling tool 'domain_status' which implies a more limited scope.

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 includes 'Works for any TLD,' indicating wide applicability, but does not explicitly specify when to use this tool versus alternatives like 'domain_status' for quick status checks. The guidance is clear but lacks explicit exclusions or alternative naming.

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?

Annotations are empty, so the description carries full burden. It correctly identifies the tool as read-only and specifies returned fields, but does not elaborate on side effects, rate limits, or authentication requirements. Adequate for a simple tool but lacking depth.

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?

Single sentence that is concise and front-loaded. Could be slightly more structured with bullet points, but it efficiently communicates the tool's purpose and output.

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 one parameter and no output schema, the description sufficiently explains what is returned (registration status and expiration date). It may not cover edge cases (e.g., invalid domain format), but it is complete for a quick-check 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% and already describes the 'domain' parameter. The description adds no new semantics beyond the schema, earning the baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool checks if a domain is registered or available, with specific return values (status and expiration). The verb "check" and resource "domain" are explicit, and the term "Quick check" distinguishes it from potentially more comprehensive sibling tools like domain_lookup.

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 use for quick checks but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among siblings. The context is adequate but lacks 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?

Despite no annotations, the description fully discloses the tool's behavior: it returns SEC filings, fundamentals, patents, news, and LEI with citation URIs, and notes limitations like only supporting company type.

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?

Packed with information in a single paragraph; could benefit from slight restructuring but remains concise and effective.

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?

With no output schema, the description sufficiently explains the return types and sources, making the tool fully understandable 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?

Adds meaning beyond the schema: explains type enum currently only 'company', value accepts ticker or zero-padded CIK, and advises using resolve_entity for names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get everything about a company in one call.' It lists specific use cases and return types, distinguishing it from siblings like compare_entities and resolve_entity.

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 (e.g., 'tell me about X') and when not to (if only a name, use resolve_entity first), providing clear context.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Delete' implies a destructive mutation, it doesn't specify whether deletion is permanent, reversible, requires specific permissions, or has side effects. The description lacks crucial context about what 'stored memory' means operationally.

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 wasted words. It's appropriately sized for a simple deletion tool and front-loads the essential information (action + resource).

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

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

For a destructive tool with no annotations and no output schema, the description is inadequate. It doesn't explain what constitutes a 'stored memory', how deletion affects the system, what happens on success/failure, or return values. The minimal description leaves too many behavioral questions unanswered.

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 'key' documented as 'Memory key to delete'. The description adds minimal value beyond the schema, merely restating that deletion occurs 'by key' without explaining key format, scope, or validation rules.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Delete') and resource ('a stored memory by key'), making the purpose immediately understandable. It doesn't explicitly distinguish from sibling tools like 'recall' or 'remember', but the verb 'Delete' provides inherent differentiation from read 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 like 'recall' (which presumably retrieves memories) or other deletion mechanisms. It states what the tool does but offers no context about appropriate use cases, prerequisites, or 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, but description discloses rate limits, non-counting against quota, and that feedback affects roadmap. Lacks detail on feedback permanence or anonymity.

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?

Single dense paragraph with no fluff, but could be structured with bullet points. Still concise and information-rich.

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

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

Given no output schema and nested parameters, description covers purpose, usage, limitations, and impact. Missing details on what the agent receives after submission (e.g., confirmation).

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 covers all params; description adds formatting guidance and explains enum values in context, e.g., 'broke or returned wrong data' for bug.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'tell' and the resource 'Pipeworx team', and contrasts with sibling tools like ask_pipeworx which are for querying, not feedback.

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 lists when to use (bug, feature, data_gap, praise) and what not to do (don't paste end-user prompt), plus rate limits and quota info.

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?

Annotations already declare readOnlyHint=true, so readonly is known. Description adds behavioral details: two modes, monotonicity checking, cross-event searching, and return type (ranked opportunities with reasoning). No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, well-structured, and front-loaded. Every sentence provides useful information without redundancy.

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

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

Given no output schema, the description explains what the tool returns (ranked opportunities with direction and reasoning). It covers both modes, examples, and the need for cross-event mode, making it complete for an agent to use correctly.

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

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

Both parameters are described in the schema, but the description adds substantial meaning: explaining modes, examples (like event slug and topic), and the distinction between single-event and cross-event. This goes well beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it finds arbitrage opportunities by checking monotonicity violations, with two modes (event and topic) explained. It distinguishes itself from sibling tools like polymarket_edges and bet_research.

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 each mode, with examples and a rationale for cross-event mode. It doesn't explicitly state when not to use or list alternatives, but the context is clear enough for an 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?

Annotations indicate read-only, open-world, non-destructive. Description adds significant behavioral details: V1 covers crypto-price bets, uses lognormal model from FRED and live coinpaprika price, fetches price history once for each asset, computes probability per market, ranks by edge. 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?

Single dense paragraph efficiently communicates purpose, model, and output. Could be slightly more structured, but no wasted words.

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?

With no output schema, description explains return format (top N ranked by edge magnitude with suggested trade direction). Covers model, data sources, intended use. Lacks error handling or constraints, but sufficient for typical use.

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

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

Schema coverage is 100%, so baseline 3. Description adds context about ranking by edge magnitude (mapping to limit) and volume window (window), but does not add significant new 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?

Clearly states the tool scans high-volume Polymarket markets, finds disagreements with Pipeworx model, and ranks by edge magnitude. Distinguishes from sibling tools like polymarket_arbitrage by focusing on opportunity discovery for betting.

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 built for the 'what should I bet on today' question, helping users avoid manual paging. Implicitly when to use, but no explicit when-not or alternative tool mention.

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 that memories can be retrieved from 'earlier in the session or in previous sessions', which adds useful context about persistence. However, it doesn't describe behavioral traits like error handling (e.g., what happens if key doesn't exist), return format, or performance characteristics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with two sentences that are front-loaded and efficient. The first sentence states the core functionality, and the second provides usage context. Every sentence earns its place with no wasted words.

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 (retrieve/list operations with one optional parameter) and no annotations or output schema, the description is partially complete. It covers purpose and usage well but lacks details on return values, error cases, or memory scope limitations, which would be helpful for an 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?

Schema description coverage is 100%, so the schema already documents the 'key' parameter. The description adds semantic context by explaining that omitting the key triggers listing all keys, which clarifies the parameter's optional nature and its effect on behavior. This goes beyond the schema's basic 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 with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings by specifying it's for accessing saved context, unlike 'remember' (store), 'forget' (delete), or 'discover_tools' (tool discovery).

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 ('retrieve context you saved earlier') and when to omit the key ('omit key to list all keys'). It distinguishes from alternatives by specifying it's for accessing saved memories, not for storing ('remember') or deleting ('forget') them.

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 must fully disclose behavior. It explains the parallel fan-out to three sources and the return format. However, it omits potential latency, rate limits, or authentication requirements, which would be helpful for an AI agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise at three sentences, with the most critical information front-loaded. Each sentence adds value: purpose and examples, data sources, parameter details, and output summary. No filler.

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

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

Given no output schema, the description adequately covers the return structure (structured changes, total_changes count, URIs). It addresses the core functionality for a moderately complex tool. Minor gaps include lack of error handling or pagination 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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the 'since' parameter formats (ISO date vs relative shorthand) and recommending '30d' or '1m' for monitoring. It also clarifies that 'value' accepts ticker or CIK, and 'type' is restricted to 'company'.

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 recent changes for a company over a specified period. It provides example queries and lists data sources (SEC, GDELT, USPTO), making the function unmistakable. Sibling tools are distinctly different, so no confusion.

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 gives use cases ('what's happening with X?', 'any updates on Y?', monitoring) and example queries. While it doesn't provide 'when not to use' or alternatives, the context is clear and sufficient given the sibling tools are unrelated.

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 differences between authenticated users ('persistent memory') and anonymous sessions ('last 24 hours'), and the tool's purpose for cross-tool context. It doesn't mention error conditions or rate limits, but covers the essential operational behavior 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?

The description is perfectly concise and well-structured in two sentences. The first sentence states the core purpose with examples, and the second adds crucial behavioral context about persistence. Every word earns its place with no redundancy or fluff.

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

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

For a tool with 2 parameters, 100% schema coverage, and no output schema, the description provides good contextual completeness. It explains the tool's purpose, usage context, and persistence behavior. The main gap is the lack of information about return values (since there's no output schema), but otherwise it covers what an agent needs to know to use this tool effectively.

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 schema already fully documents both parameters (key and value). The description doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain key constraints or value formatting). This meets the baseline expectation 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 'forget' (remove) and 'recall' (retrieve). 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 doesn't specify when not to use it or mention alternatives (e.g., using 'recall' for retrieval or 'forget' for deletion), 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?

With no annotations provided, the description carries full burden. It discloses that the tool returns IDs plus pipeworx:// citation URIs and mentions replacing multiple lookup calls. However, it does not describe behavior on no match or multiple matches, nor any potential side effects. The description adds some value but lacks full 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 concise, with three well-structured sentences: purpose, examples, and usage advice. Every sentence adds information without redundancy, 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 no output schema, the description explains the return values (IDs and citation URIs) and covers the two entity types and input formats. It is complete for a single-lookup tool, though it could mention error handling for unmatched inputs.

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

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

Schema coverage is 100%, but the description adds valuable context beyond the schema. It explains the valid formats for the 'value' parameter (ticker, CIK, name for companies; brand/generic for drugs) and provides examples. This helps the agent understand acceptable inputs beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: looking up canonical/official identifiers for companies or drugs. It specifies the ID systems (CIK, ticker, RxCUI, LEI) and provides concrete examples (e.g., 'Apple' → AAPL). This distinguishes it from sibling tools like domain_lookup or entity_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?

The description explicitly instructs to use this tool before calling other tools that need official identifiers. It provides context for when to use it (user mentions a name needing the ID) and implies alternatives (other tools require these IDs). However, it does not explicitly state when not to use it or list exclusion criteria.

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, but description explains scope (v1 supports company-financial claims via SEC EDGAR+XBRL) and return types (verdict, actual value, citation). However, it does not disclose limitations like only public US companies or specific financial metrics, which would improve 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?

Five sentences, front-loaded with purpose, no fluff. Every sentence adds value: purpose, usage, scope, replacements, and output details.

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

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

Despite no output schema, description explains return verdict types, extract structured form, actual value with citation, and percent delta. Scope limitations are noted (v1, company-financial). Complete for a single-parameter 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 covers 100% of parameters (1 required claim), and description adds meaning with examples of natural-language claims and explanation of input type, going beyond the schema's basic 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?

Description starts with strong action verbs 'Fact-check, verify, validate' and specifies the resource 'natural-language factual claim or statement against authoritative sources'. It distinguishes from sibling tools like ask_pipeworx and compare_entities by focusing on claim verification.

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: 'When an agent needs to check whether something a user said is true' and provides example queries. It also mentions it replaces 4-6 sequential calls, giving clear context.

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