Squiggle

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

Annotations declare readOnlyHint, openWorldHint, and destructiveHint. The description adds behavioral details: routes to the right tool among 1,423+, fills arguments, and returns structured answers with stable citation URIs. This adds value beyond 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 a single paragraph that front-loads the key instruction. It contains many examples which are helpful but slightly lengthy. Overall, it conveys necessary information without being excessively verbose.

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 simple input schema (one required parameter) and annotations, the description is complete. It explains what the tool does, when to use it, what inputs to provide, and what output to expect (structured answer with citations). No 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% coverage for the single parameter 'question' with a description. The description adds nuance by listing example queries and clarifying the scope (current or historical data, authoritative structured data), which helps the agent formulate the parameter correctly.

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 answers factual questions by routing to specialized tools, which is the core purpose. It distinguishes itself from sibling tools by emphasizing preference over web search for authoritative structured data, and provides a comprehensive list of example 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?

Explicitly says 'PREFER OVER WEB SEARCH' and provides numerous examples of when to use (SEC filings, FDA data, etc.) and even mentions it can be used even if web search could answer, giving clear guidance on usage context. It does not explicitly state when not to use, but the positive guidance is strong.

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, openWorldHint=true, destructiveHint=false. The description adds behavioral details: 'resolves the market, classifies the bet, fans out to the right packs, returns an evidence packet plus a simple market-vs-model comparison.' It also explains the depth parameter behavior. 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 moderately long but front-loaded with the main action and then details. Each sentence adds value: purpose, accepted inputs, process, output, use cases. Slightly verbose but efficient for the complexity.

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, but the description explains the return: 'evidence packet plus a simple market-vs-model comparison.' It covers inputs, process (fan-out by classification), and output. Given the tool's moderate complexity, this is complete and allows an agent to understand and invoke correctly.

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

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

Schema coverage is 100% with both parameters documented. The description adds meaning for 'depth' by clarifying 'quick = 2-3 evidence sources, thorough = full fan-out. Default thorough.' For 'market', it reiterates accepted formats (slug, URL, question text) but does not add beyond schema. This is good enhancement.

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: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the resource (Polymarket bet) and distinguishes from sibling tools by calling it the 'core demo product' that allows agents to get bet-relevant context without discovering packs themselves.

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 use cases: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?".' It implies when to use this tool over alternatives like 'ask_pipeworx' but lacks explicit when-not-to-use or alternative tool mentions.

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 and openWorldHint. The description adds specific data sources (SEC EDGAR/XBRL for companies, FAERS/approvals/trials for drugs) and return format (paired data, citation URIs). No contradictions; adds useful context beyond 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 two well-organized paragraphs: first sentence states purpose and use cases, second provides data details. It's front-loaded and each sentence adds value, though the second paragraph is a bit list-heavy but still 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?

No output schema, so description explains return data (paired data + citation URIs) and sources. However, it omits limitations like geographic scope (US-centric?), data freshness, or date ranges. Adequate but leaves some ambiguity.

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

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

Schema coverage is 100%. The description adds meaning: explains values as tickers/CIKs or drug names, and what each type returns (financial metrics vs adverse events). This enriches the bare 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?

The description clearly states 'Compare 2–5 companies (or drugs) side by side in one call' with specific verb and resources. It distinguishes from siblings by noting it replaces many sequential calls, and no sibling tool overlaps this functionality.

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 lists trigger phrases like 'compare X and Y' and scenarios (tables/rankings). It also mentions efficiency gain. However, it does not explicitly state when not to use (e.g., single entity), though context implies entity_profile for single 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description doesn't need to reemphasize safety. The description adds value by stating it 'Returns the top-N most relevant tools with names + descriptions,' clarifying the behavior beyond the annotations. No contradiction.

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 main action and purpose. It includes a long list of domains, which is informative but slightly verbose. Overall, it's efficient and no superfluous 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?

Given no output schema, the description adequately explains what the tool returns: 'top-N most relevant tools with names + descriptions.' It covers when to use, what it does, and what to expect. For a discovery tool with good annotations, it is complete.

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

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

Schema coverage is 100% (both parameters have descriptions). The description does not add additional meaning beyond the schema; it merely references the parameters implicitly. Baseline 3 is appropriate.

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

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

The description explicitly states the tool's purpose: 'Find tools by describing the data or task.' It lists numerous specific domains and clearly contrasts with sibling tools that are likely data-specific. The verb 'discover' is distinct from other tools like 'ask_pipeworx' 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?

Provides explicit when-to-use: 'Use when you need to browse, search, look up, or discover what tools exist for...' and includes a strategic instruction: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This is exceptional 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?

Annotations already declare readOnlyHint=true, so the description adds value by specifying input format constraints (ticker or CIK) and the nature of the output (recent filings, fundamentals, etc.). 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 well-structured and concise despite comprehensive content. It front-loads the purpose, then usage examples, then output summary, then input hints. Every sentence contributes unique 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 complexity (multiple data sources) and lack of output schema, the description adequately summarizes the returned information (SEC filings, revenue, patents, news, LEI) and citation format. It could mention potential size/ordering but is sufficient for an AI agent to understand the tool's capabilities.

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 have schema descriptions, and the description adds significant value: it clarifies that type must be 'company', provides concrete examples for value, and explicitly states that names are not accepted, directing to resolve_entity.

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 provides specific example user queries and lists the data returned (SEC filings, revenue, patents, news, LEI), making it highly 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?

The description explicitly states when to use the tool (user asks for company info) and provides a clear exclusion: if only a name is available, use resolve_entity first. This offers excellent guidance on 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?

Annotations already indicate destructiveHint=true; description confirms deletion and adds context about clearing sensitive data, providing additional behavioral insight.

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

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

Two concise sentences, front-loaded with primary purpose, followed by usage guidance. No unnecessary 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 simple tool (one required parameter, no output schema, clear annotations), the description covers purpose, usage, and context completely.

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 provides full coverage for the single parameter 'key' with a clear description. Description does not add extra semantic detail beyond 'by key'.

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 ('previously stored memory by key'), and distinguishes from siblings by mentioning 'remember' and 'recall'.

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 ('context is stale, task is done, clear sensitive data') and provides pairing guidance ('Pair with remember and recall').

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 and destructiveHint=false. The description adds no behavioral context beyond what annotations provide, such as data scope or return 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?

Extremely short but under-specified. Not concise in an informative way; lacks essential details. Every word should add value, but these two words are insufficient.

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 3 optional parameters, no output schema, and a terse description, the tool is completely inadequate for an AI agent to understand what it returns or how to use it. No context on return values or filtering behavior.

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

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

Schema coverage is only 33% (only 'complete' has a description). The description does not elaborate on 'year' or 'round', nor does it explain how 'Fixture + results' relates to the parameters. Fails to compensate for low 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 'Fixture + results' is vague and does not clearly state a verb+resource. It is unclear what actions can be performed (e.g., list, get). Siblings like 'teams' and 'standings' suggest a sports context, but no differentiation.

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

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

No guidance on when to use this tool versus alternatives such as 'teams' or 'standings'. The description does not mention context, exclusions, or prerequisites.

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, openWorldHint, and destructiveHint, covering safety and mutability. The description adds 'per source' context, but does not explain projection logic, return format, or behavior beyond what annotations imply.

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?

At four words, the description is extremely brief and lacks structure. While concise, it sacrifices clarity and completeness. It is not a full sentence and provides minimal 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 no output schema, 0% parameter descriptions, and the complexity of a projected ladder tool, the description is severely incomplete. It fails to define what a ladder is, how parameters affect results, or what the output contains.

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 0%, so description must compensate. It only hints that 'source' is a filter, but does not explain the purpose or valid values of year, round, or source. Three parameters are essentially undocumented.

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 'Projected ladder per source' which conveys the basic purpose (projected ladder) and indicates a filtering dimension (source), but lacks a specific verb and resource. It is somewhat vague and could be misinterpreted.

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

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

No guidance on when or why to use this tool versus siblings like standings or teams. No prerequisites or context provided. The description does not help an agent decide between 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?

The description adds behavioral context beyond annotations: it states the tool is 'free' and doesn't count against quota, and is rate-limited to 5 per identifier per day. Annotations only indicate readOnlyHint=false, destructiveHint=false, and openWorldHint=false, but the description clarifies the write operation and 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 well-structured and front-loaded with the main action. It contains no unnecessary sentences, though it is slightly longer than necessary. However, every sentence adds value.

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

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

Given the tool has 3 parameters (one nested), no output schema, and moderate complexity, the description covers all aspects: purpose, usage cases, parameter details, behavioral limits, and formatting guidance. It is fully complete for an agent to select and invoke 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?

Although schema coverage is 100%, the description adds meaning beyond the schema. For 'message', it specifies '1-2 sentences typical, 2000 chars max'. For 'type', it reinforces the enum meanings. For 'context', it provides usage guidance on how to fill pack, tool, and vertical fields.

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: to report bugs, request features, highlight data gaps, or give praise. It specifies the actions ('broken, missing, or needs to exist') and the resource ('Pipeworx team'). It distinguishes from sibling tools like 'ask_pipeworx' which is for questions, 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?

The description explicitly lists when to use the tool: for bugs, feature/data gaps, and praise. It also tells what not to do ('don't paste the end-user's prompt') and mentions rate limits and quota. This provides clear guidance on alternatives and constraints.

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

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

Annotations already declare readOnlyHint, openWorldHint, destructiveHint. Description adds value by detailing the tool walks child markets, extracts dates/thresholds, sorts, and reports pairs. No contradiction.

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 well-structured, front-loaded, and concise. Every sentence adds value, explaining the concept, usage, and output efficiently.

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 the return format. Combined with annotations, the tool is fully documented and understandable.

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 a good description of the 'event' parameter. The main description reiterates this, adding minimal extra meaning. Baseline 3 is appropriate.

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

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

Description clearly states the tool finds arbitrage opportunities via monotonicity violations, with detailed explanation of the logic and example. It distinguishes itself from siblings like 'polymarket_edges' by focusing on a specific pattern within a single event.

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 the condition arises but does not explicitly state when to use this tool over alternatives or when not to use it. Guidelines are implied rather than direct.

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 (readOnlyHint, openWorldHint) are complemented by details on data sources (FRED, coinpaprika), grouping, computation, and ranking. No contradictions. Adds value beyond 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?

Front-loaded purpose in first sentence. Three sentences total, but could be slightly more concise. 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?

Complete explanation of model, data sources, process, and intended use. No output schema, but description implies return structure. Covers all necessary context 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 coverage is 100%, so baseline is 3. Description does not add significant new meaning beyond schema descriptions for the three parameters. No extra value.

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 it scans high-volume Polymarket markets for edges where Pipeworx data disagrees with market price, specifying the action, resource, and context. It distinguishes from sibling tools like polymarket_arbitrage.

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

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

Explicitly states it's for the 'what should I bet on today' question and for discovering opportunities without manual paging. Lacks explicit when-not-to-use or alternatives, but context is clear.

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

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

Description adds scoping detail ('Scoped to your identifier') beyond the readOnly and destructive hints from annotations, clarifying that the tool is safe and context-specific.

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 concise sentences, front-loaded with the core functionality, and every sentence provides valuable 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 the simple optional parameter, annotations confirming read-only safety, and the description covering usage and scope, the tool definition is fully complete for an agent to select and invoke 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?

The schema already fully describes the single optional parameter with 100% coverage. The description repeats the 'omit to list' behavior but adds minimal additional context about the 'remember' origin, so baseline 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 verb (retrieve/list) and resource (saved memory), distinguishing it from sibling tools like 'remember' and 'forget'. It explicitly covers both retrieving a specific value and listing all keys.

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?

Provides explicit examples of when to use (e.g., target ticker, address, research notes) and explains the pairing with 'remember' and 'forget' for complete context management.

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 indicate read-only and non-destructive behavior. The description adds value by detailing the fan-out to three sources (SEC EDGAR, GDELT, USPTO) and the return format (structured changes, count, citation URIs).

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 purpose, then use cases, then behavior. Every sentence adds value with 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?

The tool has no output schema, but the description adequately explains the return structure (structured changes, total_changes count, citation URIs). It also covers the fan-out behavior and parameter details, making it sufficiently complete.

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

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

Schema coverage is 100%, but description adds practical guidance: explains the 'since' parameter's accepted formats (ISO date or relative shorthand) and recommends defaults ('30d' or '1m'). Also clarifies that 'type' only supports '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: returning recent changes for a company. It provides specific verb 'What's new' and resource 'company', and gives multiple example queries that distinguish it from other tools like 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 lists example user queries that should trigger this tool and says 'Use when...'. It provides clear context but does not mention when to use alternatives or when not to use it.

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 sparse (readOnlyHint=false, destructiveHint=false). Description adds value: scoped by identifier, persistent vs 24-hour retention, and write operation context. 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?

Moderately concise two-sentence block. Front-loaded with primary purpose, but could be slightly more structured. 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?

For a simple tool with 2 required params and no output schema, the description covers purpose, usage, scoping, persistence, and sibling relationships. Complete and actionable.

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 100% with good descriptions. Description adds context ('key-value pair scoped by your identifier') and examples of what to store, enhancing 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?

Clear verb 'save' with specific resource 'data the agent will need to reuse later' and scope 'key-value pair scoped by your identifier'. Distinguished from siblings recall and forget.

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 guidance on when to use ('when you discover something worth carrying forward') with examples. Mentions siblings recall and forget for retrieval and deletion, but lacks explicit 'when not to use'.

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 mark the tool as read-only and non-destructive. The description adds valuable behavioral context: it returns IDs plus pipeworx:// citation URIs, and it replaces multiple lookup calls. This gives the agent a clear understanding of the tool's behavior and output without contradicting 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, using a single paragraph of 4-5 sentences. It front-loads the core purpose and then adds examples and usage guidance. Every sentence contributes meaningful information without redundancy or 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?

For a tool with two parameters and no output schema, the description is complete. It explains the purpose, when to use it, what IDs are returned, and provides examples. It also mentions the citation URIs, which is a key output detail. No essential information is missing.

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 covers both parameters with descriptions, but the tool description adds significant value by providing concrete examples for each parameter (e.g., 'AAPL' for ticker, '0000320193' for CIK). It also explains that for drugs, both brand and generic names are acceptable, enhancing the agent's understanding 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 the tool's purpose: to look up canonical/official identifiers for companies or drugs. It specifies the ID systems (CIK, ticker, RxCUI, LEI) and provides concrete examples, making the tool's function unambiguous. It also differentiates from siblings by noting it should be used before other tools that require identifiers.

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 the tool: when a user mentions a name and an official identifier is needed. It also advises to use this tool before calling other tools that require identifiers, giving clear usage direction. Examples illustrate common scenarios, further guiding the agent.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds no extra behavioral context. It does not mention what the output contains, possible data formats, or any side effects, making it redundant and insufficient beyond what annotations provide.

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?

While very short (three words), the description is not effectively concise because it fails to convey essential information. Conciseness should not sacrifice clarity; here, the brevity results in a lack of actionable guidance for the agent.

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 and no parameters, the description bears the full burden of explaining the tool's purpose and return value. It does not clarify what 'registered tipping sources' are or what the tool returns (e.g., a list, an object), leaving the agent with insufficient context to use the 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?

There are zero parameters, and schema description coverage is 100% vacuously. The description has no need to add meaning for parameters, so a baseline of 4 is appropriate. It does not attempt to explain parameter semantics, which is acceptable given their absence.

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 'Registered tipping sources.' is a noun phrase that restates the tool name with a qualifier ('tipping'), lacking a verb indicating the action. It does not clearly state whether the tool lists, searches, or retrieves sources, making it vague and not helpful for distinguishing from sibling tools like 'tips'.

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 compared to alternatives. Sibling tools include 'tips', 'teams', and 'ladder', but the description fails to offer any context or exclusion criteria for selecting 'sources' over 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?

Annotations already indicate readOnlyHint and non-destructive behavior. However, the description adds no behavioral details such as what data is returned, any rate limits, or prerequisites. It fails to add value beyond 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?

While extremely short (one word), this is under-specification rather than conciseness. The description should provide more substance to be useful.

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 and minimal description, the tool definition is completely inadequate. The agent cannot determine what the tool returns, how to call it correctly, or how it relates to sibling tools.

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 0%, and the description does not explain what 'year' or 'round' represent, their valid ranges, or any defaults. The agent cannot infer parameter usage from the 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 'Ladder.' is tautological and fails to specify what type of standings (e.g., sports league, competition ladder, etc.). It does not distinguish from similar sibling tools like 'ladder'.

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 or when to prefer alternatives like 'ladder' or 'teams'. The description is too minimal to infer appropriate usage 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?

Annotations already mark it as read-only and non-destructive. The description adds no additional behavioral information such as data scope, freshness, or side effects. It fails to leverage the openWorldHint to clarify data sources.

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

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

The description is extremely short but at the expense of essential information. It is not appropriately sized for a tool with one parameter and ambiguous context. 'Conciseness' requires efficiency with completeness, not mere brevity.

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 and zero parameter coverage, the description is severely incomplete. It does not explain what teams are listed, how the optional year parameter filters, or what the output structure looks like. Sibling tool names provide some context, but the description itself is inadequate.

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

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

The single optional parameter 'year' is undocumented in the schema (0% coverage) and not mentioned in the description. The description provides no guidance on the parameter's meaning, expected values, or impact on results.

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 'Team list.' is a noun phrase, not an action-oriented statement. It vaguely indicates a list of teams but lacks specificity about context or scope, making it unclear what exactly the tool does. It does not differentiate from sibling tools like 'games' or 'standings'.

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?

There is no guidance on when to use this tool versus alternatives like 'games', 'standings', or 'ladder'. The description provides no context about 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?

Annotations already indicate a safe read operation, but description adds no behavioral detail (e.g., how tips are computed, any date range constraints).

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

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

Extremely short but under-specified; conciseness is not beneficial when essential information is missing.

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, yet description fails to explain return format or behavior; leaves significant gaps for an agent to interpret 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?

Only 33% of parameters have schema descriptions; the description mentions 'source' and 'game' but does not clarify year or round, leaving meaning incomplete.

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 'Tips per game per source' is vague; it does not specify what kind of tips (e.g., averages, totals) and fails to distinguish from sibling tools like games or sources.

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

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

No guidance on when to use this tool versus alternatives; no context on prerequisites or typical 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 already indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds valuable behavioral context: returns verdict, extracted form, actual value with citation, and percent delta. No contradiction.

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?

Every sentence adds value: purpose, usage, scope, return details, efficiency. 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 single-parameter tool with no output schema, the description fully covers what the tool does, when to use it, and what it returns. Annotations complement well.

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 explains the parameter. The description adds natural-language examples and clarifies acceptable formats, which is helpful but not essential 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 the tool fact-checks natural-language factual claims against authoritative sources, specifies its domain (company-financial claims via SEC EDGAR), and distinguishes it from siblings by noting it replaces 4-6 sequential 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?

Explicitly provides usage guidance with examples ('Use when an agent needs to check whether something a user said is true'), describes supported claim types, and implies when not to use (e.g., non-financial claims).

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