Libretranslate

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

No annotations are present, so the description carries the full burden. It explains that Pipeworx picks the right tool and fills arguments, but it does not disclose what happens on failure, rate limits, or whether any actions are destructive. The behavioral description is adequate but not comprehensive.

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 focused paragraph that front-loads the core purpose. It includes examples and a list of data sources. While informative, it could be slightly more concise without losing clarity, but it is well-structured for an AI 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?

Given the tool's complexity (300+ sources) and the presence of only one parameter with no output schema, the description is quite complete. It explains the tool's role, when to use it, and provides relatable examples. It lacks information on error handling and response format, but these are less critical for such a simple interface.

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

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

The single parameter 'question' is described in the schema as 'Your question or request in natural language'. The description adds substantial value by providing examples of valid questions (e.g., 'What is the US trade deficit with China?'), which clarifies the expected format and scope 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: answering natural-language questions by automatically selecting the right data source. It distinguishes itself from sibling tools by covering 300+ sources and handling queries that would otherwise require picking a specific tool.

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 usage guidance is provided: 'Use when a user asks...and you don't want to figure out which Pipeworx pack/tool to call.' This clearly indicates when to use the tool. However, it does not mention specific alternatives among sibling tools 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?

The description adds significant context beyond annotations: it resolves market, classifies bet by type, fans out to relevant data packs (crypto+fred+gdelt, etc.), and returns a comparison. This explains the tool's complex behavior without contradicting annotations (readOnly, openWorld, non-destructive).

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 efficient but slightly long at ~100 words. It is front-loaded with the main action and each sentence serves a purpose. Minor reduction could improve conciseness without losing clarity.

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

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

Given no output schema, the description adequately explains the return (evidence packet + simple comparison) and the fan-out logic. It could detail output structure more, but it provides sufficient context for an agent to understand the tool's 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%, but the description adds meaningful context: details on how 'market' accepts slug, URL, or question text, and explains that 'depth' controls the number of sources (quick=2-3, thorough=full). This enriches the schema definitions.

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

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

The description clearly states it researches a Polymarket bet by pulling Pipeworx data. It specifies input types (slug, URL, question text) and output (evidence packet + market-vs-model comparison). This distinguishes it from sibling tools like 'ask_pipeworx' which is more general.

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 use cases ('should I bet on X?', 'what does the data say?', 'is there edge?') and positions it as the core demo product. It does not directly contrast with alternatives but implies this is the specialized tool for bet research.

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

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

Discloses data sources (SEC EDGAR/XBRL, FAERS, FDA) and types of data retrieved (revenue, net income, adverse events, approvals, trials). Missing explicit statements on idempotency or safety, but annotations are absent so description carries full burden.

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?

Compact paragraph with clear flow: purpose, usage cues, type-specific details, output indication. Every sentence adds value with no 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?

Provides sufficient context about supported entity types, data sources, and output format (paired data + URIs). Given no output schema, this is complete for an AI 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?

Schema descriptions already cover parameters at 100%; description adds value by contextualizing the data returned for each type, which is not in 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 compares 2–5 companies or drugs side by side, with specific verb 'Compare' and resource entities. It distinguishes from siblings like entity_profile by focusing on multi-entity comparison.

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

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

Provides explicit usage cues: 'when a user says compare X and Y, X vs Y, etc.' and implies when not to use by noting it replaces 8–15 sequential calls, suggesting single-entity cases go elsewhere.

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 mentions returning ranked language candidates with confidence, providing some behavioral context. However, with no annotations, it fails to disclose limitations like character limits, language coverage, or handling of ambiguous text.

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

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

Two sentences, front-loaded with the action, no wasted words. Every sentence earns its place.

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

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

For a simple tool with one parameter and no output schema, the description is fairly complete. It explains input, action, and output. Minor missing context: supported languages or error handling. Siblings like 'list_languages' suggest additional info could help.

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

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

The single parameter 'text' has 100% schema description coverage ('Text to analyze'). The description adds minimal meaning beyond the schema, meeting the baseline for high coverage.

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

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

The description clearly states the tool's purpose: detecting the source language of text and returning ranked candidates with confidence. It uses a specific verb ('Detect') and resource ('source language'), distinguishing it from siblings like 'translate'.

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

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

The description implies usage for identifying text language but lacks explicit guidance on when to use this tool versus alternatives (e.g., 'translate' or 'list_languages'). No exclusions or prerequisites are mentioned.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it returns 'the top-N most relevant tools with names + descriptions,' which is sufficient for a read-only search tool. However, it does not disclose ranking logic or any potential side effects, leaving some gaps.

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

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

The description is a single paragraph that front-loads the purpose, followed by usage guidance and examples. It is reasonably concise, but could be improved with clearer structure (e.g., bullet points for domains). No unnecessary repetition.

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

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

The tool has no output schema, but the description explains it returns 'the top-N most relevant tools with names + descriptions,' which is sufficient. It also provides guidance to call this first, making it contextually complete for a simple discovery tool. No critical gaps.

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

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

Schema description coverage is 100%, and both parameters (query, limit) have schema descriptions. The description adds examples for the query parameter (e.g., 'analyze housing market trends') but does not provide additional meaning beyond the schema for the limit parameter. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It specifies it returns the top-N most relevant tools with names and descriptions. It distinguishes itself from sibling tools (e.g., ask_pipeworx, compare_entities) which perform different tasks, making it clear this is the discovery tool.

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: 'Use when you need to browse, search, look up, or discover what tools exist for...' and provides specific domain examples. It also guides the agent to call this first when many tools are available, offering clear usage context and alternatives.

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

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

Without annotations, the description discloses the tool's read-only nature implicitly (returns data) and details output content (SEC filings, fundamentals, patents, news, LEI) and input constraints. It lacks explicit safety/error handling details but provides strong behavioral context.

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

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

The description is concise and well-structured: it starts with the core purpose, then lists use cases, output components, and input guidance. Every sentence adds value, though it could be slightly more structured (e.g., bullet points).

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 compensates by enumerating output components and citation format. It covers input constraints, usage context, and sibling relationships. Minor gaps (e.g., data freshness, error handling) prevent a perfect score.

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 detailed descriptions for both parameters. The tool description does not add additional parameter semantics beyond the schema, so a baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Get everything about a company in one call.' It lists specific use cases (e.g., 'tell me about X') and differentiates from siblings by noting it consolidates 10+ data sources, including `resolve_entity` for name resolution.

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 usage guidelines are provided: use when users ask for a company profile or when multiple data points are needed. It also specifies when not to use (if only a name is provided) and directs to `resolve_entity` as an alternative.

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 behavioral traits. It states 'Delete' implying mutation, but does not mention error handling (e.g., what if key doesn't exist), idempotency, or permission requirements.

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 very efficient sentences with no extraneous information. The key action and usage context are front-loaded.

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

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

For a simple tool with one parameter and no output schema, the description covers purpose, usage context, and relationship to siblings. However, it could provide more detail on expected behavior (e.g., silent failure vs error).

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 parameter 'key' described as 'Memory key to delete'. The description does not add additional meaning beyond what the schema already provides, so baseline 3 applies.

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

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

The description clearly states 'Delete a previously stored memory by key', which is a specific verb+resource. It distinguishes itself from sibling tools like remember and recall by being the deletion counterpart.

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: 'when context is stale, the task is done, or you want to clear sensitive data'. It also suggests pairing with remember and recall, offering clear usage context but missing 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?

The description implies a safe read operation but does not explicitly state non-destructiveness or other behaviors. With no annotations, it carries full burden but is minimally adequate.

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

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

Single sentence, 9 words, no filler. Action is front-loaded and every word earns its place.

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

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

No output schema exists, and the description does not specify return format (e.g., language codes or names). However, for a simple list tool with no parameters, it is minimally 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?

No parameters exist, so schema coverage is 100%. Description adds no param info, which is expected; 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 tool lists languages supported by a specific instance, using a clear verb and resource, 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?

No guidance on when to use or not use this tool relative to siblings like detect_language or translate. The use case is implicit.

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 fully discloses behavioral traits: rate-limited to 5 per identifier per day, free, doesn't use quota, team reads digests daily, and message constraints (2000 chars max). No contradictions.

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

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

The description is fairly concise but includes several details that could be streamlined. It front-loads the purpose and then elaborates. A minor reduction could improve readability.

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 description covers all relevant aspects: what to provide, how to structure feedback, constraints, and what happens (digest, roadmap impact). No output schema needed as feedback submission is straightforward.

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%, and the description adds further meaning by explaining enum values (e.g., bug = something broke), context fields (pack, tool, vertical), and message tips (be specific, 1-2 sentences). This enriches 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 is for providing feedback about bugs, missing features, data gaps, or praise to the Pipeworx team. It is distinct from sibling tools like ask_pipeworx or discover_tools, which serve query/discovery purposes.

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

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

The description explicitly says when to use this tool (bug, feature/data_gap, praise) and provides anti-guidance (don't paste end-user prompt). It also mentions rate limits and that it doesn't count against the tool-call quota.

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 declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context: it walks child markets, extracts dates/thresholds, sorts, and reports violations. No contradiction, and the description enriches the safety profile.

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 explanation, example, and output format. Each sentence earns its place with no 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?

Given one parameter, no output schema, and no nested objects, the description completely covers input, logic, and the structure of the returned list, making it fully self-contained.

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 sole parameter 'event' has 100% schema coverage. The description adds meaning by specifying it accepts a slug or URL and provides an example, which the schema alone does not convey.

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 within Polymarket events. It provides a specific verb-resource pair and distinguishes itself from siblings like 'polymarket_edges' by focusing on monotonicity-based 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?

The description explains when to use (for events with multiple date/threshold markets) and gives an example. It lacks explicit when-not-to-use or alternative tools, but the context is clear enough.

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 discloses the internal model (lognormal from FRED + coinpaprika), grouping logic, and that it returns top markets ranked by edge. It also notes V1 covers crypto-price bets, indicating scope limitations. Annotations already mark it as read-only, and the description adds substantial behavioral context without 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 a single paragraph that efficiently conveys purpose, methodology, and output. It is slightly verbose but well-organized and front-loaded. Minor improvement could be made by breaking into shorter sentences, but overall it is concise enough.

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 absence of an output schema, the description adequately explains the return value (top N ranked by edge magnitude with trade direction). It also explains the underlying model and constraints (V1 crypto-only). The three parameters are well-covered by the schema, so no gaps exist.

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?

All three parameters have descriptions in the input schema (100% coverage). The description does not add new information about the parameters beyond what is already in the schema, so a baseline score of 3 is appropriate.

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

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

The description clearly specifies that the tool scans Polymarket markets, identifies where Pipeworx data disagrees with market prices, and returns top N ranked by edge magnitude. It distinguishes itself from siblings like polymarket_arbitrage by focusing on disagreement magnitude rather than arbitrage opportunities.

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 states it is built for the 'what should I bet on today' question, implying when to use it. However, it does not explicitly mention when not to use it or provide alternatives. The context is clear enough for an agent to understand its primary use case.

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 covers key behavioral aspects: retrieval of saved values, scoping, and non-destructive nature. Missing details on error behavior (e.g., missing key) but sufficient for core use.

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

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

Three sentences, each adding essential information: action, use cases, scoping, and pairings. No superfluous text, front-loaded with the core purpose.

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 (1 optional parameter, no output schema), the description covers purpose, usage, scope, and relationships with siblings completely. No gaps identified.

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 already descriptively documents the 'key' parameter (100% coverage). Description adds value by explaining the omission behavior and giving usage examples beyond 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?

Description clearly states the tool retrieves a value saved via remember or lists all keys, with specific examples like 'target ticker, address, prior research notes'. It distinguishes itself from siblings (remember, forget) by describing its complementary role.

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 describes when to use (look up stored context) and how to use (omit key to list all). Also mentions scoping and pairing with remember/forget, providing practical guidance without ambiguity.

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

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

With no annotations, the description fully covers behavioral traits: fans out to three sources, returns structured changes + total_changes count + citation URIs. It does not disclose potential limitations like latency or empty results, but overall transparent.

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

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

The description is front-loaded with purpose and example queries. It is slightly verbose but each sentence adds value. Could be tightened, but 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?

Given no output schema, the description adequately explains return values (structured changes, total_changes, citation URIs). It covers all parameters and use cases. Missing details on pagination or limits, but sufficient for a monitoring 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% with detailed parameter descriptions. The overall description adds value with examples (e.g., 'Use 30d for typical monitoring') and clarifies since formats, but does not significantly extend 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 answers 'What's new with a company in the last N days/months?' and provides explicit example queries. It distinguishes itself from siblings like entity_profile by focusing on temporal changes across multiple 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?

The description gives explicit query patterns and states 'Use when a user asks...' with multiple examples. It lacks explicit 'when not to use' but provides strong contextual 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?

Despite no annotations, description fully discloses behavioral traits: data scoped by identifier, persistence behavior (authenticated permanent vs anonymous 24-hour), and write operation nature. 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?

Four sentences, each necessary. Front-loaded with purpose, then usage, then behavioral details. No redundant phrasing.

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

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

Covers key aspects: key-value storage, scoping, persistence. No output schema, but return behavior is implied (no retrieval). Could mention that tool does not read memory, but paired tools handle that.

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 value with concrete examples of valid keys (e.g., 'subject_property') and clarifies value is open-ended text, enhancing schema meaning.

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 explicitly states the tool saves data for reuse across conversations/sessions, with verb 'save' and resource 'data'. Distinguishes from siblings by mentioning recall and forget as complementary 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?

Clear guidance on when to use: 'when you discover something worth carrying forward'. Mentions scope (authenticated vs anonymous, 24-hour retention) and pairs with recall/forget as alternatives.

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

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

With no annotations, the description carries the full burden. It discloses what the tool returns (IDs plus pipeworx:// citation URIs) and gives example outputs. It does not discuss error handling, authentication, or side effects, but for a read-oriented lookup, the provided information is sufficient. No contradictions with annotations (none present).

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 covering purpose, usage, examples, and a directive. No filler or redundancy; every sentence earns its place.

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

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

Given the tool's complexity (2 params, no output schema, no annotations), the description is quite complete. It explains inputs, outputs, and usage context. It does not explain the pipeworx:// URI scheme, but that is likely domain knowledge. Overall, it provides enough information for effective 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?

The input schema has 100% description coverage, so baseline is 3. The description adds value by providing concrete examples for each parameter (e.g., 'AAPL', '0000320193', 'ozempic'), clarifying acceptable formats beyond the schema's enum and string descriptions.

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

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

The description clearly states the tool's purpose: to look up canonical/official identifiers for companies or drugs. It specifies the verb 'look up' and the resource (identifiers for entity types), and gives concrete examples of ID systems (CIK, ticker, RxCUI, LEI). It distinguishes itself by noting it replaces 2–3 lookup calls, setting it apart from sibling tools like compare_entities 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 states when to use: 'Use when a user mentions a name and you need the CIK...' and provides a clear directive: 'Use this BEFORE calling other tools that need official identifiers.' This guides the agent on ordering and context, though it does not explicitly list 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?

Without annotations, the description carries the full burden. It discloses auto-detection behavior and return values (translated text and detected source), which is sufficiently transparent for a simple translation tool.

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

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

Two sentences, front-loaded with the core purpose, no wasted words. Every sentence serves a clear function.

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 4 parameters, no output schema, and no annotations, the description covers purpose, input constraints, and return details. It lacks examples or error handling, but is largely complete for a straightforward translation 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 the description adds only minor redundancy (e.g., 'Source can be "auto"' already in schema). It does not meaningfully extend beyond what the schema provides.

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

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

The description clearly states 'Translate text' with a specific verb and resource. It also mentions auto-detection and return values, distinguishing it from sibling tools like 'detect_language'.

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 explicit when-to-use or when-not-to-use guidance is provided. The description is adequate but lacks exclusions or comparison to alternatives.

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

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

No annotations provided, but description details return values (verdict types, citation format, delta) and states it replaces multiple sequential calls. Transparent about scope (v1 US public companies).

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 informative yet not verbose. Sentences are focused, but could be slightly shorter without losing 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?

No output schema, but description fully explains return types and format. Provides operational context (replaces 4-6 calls) and domain constraints. Complete for a 1-param 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% for the single parameter, so baseline 3. Description adds examples but parameter description in schema already includes similar examples. No significant added value 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 it fact-checks claims, specifies supported domains (company-financial via SEC EDGAR+XBRL), and gives example queries. Unambiguous purpose.

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

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

Explicitly says when to use ('check true/false of user statement'), provides example patterns. Does not explicitly exclude other use cases or mention alternatives.

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