Cataas

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

No annotations are provided, so the description must carry the behavioral burden. It describes the tool as selecting the right tool and filling arguments, implying it may call other tools. This is a significant behavioral trait. However, it does not disclose potential side effects, rate limits, or whether the tool can fail (e.g., if no data source matches). Score 3 because it adds some context beyond the schema but lacks full transparency.

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

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

The description is three sentences plus examples. It is front-loaded with the purpose and key behavior. The examples are helpful but add length. It is appropriately concise for the complexity of the tool.

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 one parameter, no output schema, and no annotations, the description covers the essential behavior and provides usage examples. It could mention what happens if the tool cannot answer (e.g., fallback behavior), but for a simple tool, it is reasonably complete.

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

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

Schema coverage is 100% with one required parameter 'question', whose description is 'Your question or request in natural language'. The tool description elaborates on what constitutes a good question via examples (e.g., 'What is the US trade deficit with China?'), which adds value. However, the schema already provides adequate description, so baseline is 3.

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

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

Description uses specific verbs ('Ask', 'picks', 'fills', 'returns') and clearly identifies the resource ('best available data source'). It explicitly distinguishes itself from sibling tools by explaining that the agent does not need to browse tools or learn schemas, which is a key differentiator from other tools like cat_by_tag or discover_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 provides clear guidance on when to use this tool: when the user has a natural language question and does not want to manually select tools. It gives examples of appropriate usage. However, it does not explicitly state when NOT to use it or mention alternatives, so it scores a 4.

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, openWorldHint=true, destructiveHint=false. The description adds behavioral details beyond annotations: it resolves the market, classifies the bet, fans out to appropriate packs based on classification, and returns a comparison. 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 moderately long but efficient, with front-loaded purpose and each sentence contributing useful information. It could be slightly condensed without losing clarity, but it remains well-structured.

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

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

Given no output schema, the description adequately covers output (evidence packet + market-vs-model comparison). It explains input, process, and use cases. Some internal details like specific packs are omitted, but the description is complete enough for an agent 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?

Schema coverage is 100% with descriptions for both parameters. The description adds meaning by explaining the depth parameter (quick vs thorough) and specifying default thorough, and elaborates on market input formats. This adds value 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 explicitly states the tool researches a Polymarket bet by pulling Pipeworx data, lists accepted input formats (slug, URL, question text), and describes output (evidence packet + market-vs-model comparison). It distinguishes from siblings by noting this is the core demo product that converts better than having agents discover 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 clear use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It does not explicitly state when not to use or list alternatives, but the context is sufficient for an agent to decide.

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 context that the tool returns image URL, cat ID, and tags, which is useful. Since annotations are empty, the description carries the burden, but it does not disclose any destructive potential or side effects (unlikely for a read tool). Could mention that the result is random among matching tags, but acceptable.

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. First sentence states purpose, second gives usage guidance, third describes return value. No wasted words, front-loaded with key action.

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 low complexity (1 parameter, no output schema, no nested objects), the description covers purpose, usage, and return value completely. No gaps for an agent to 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 has 100% coverage with detailed description for the single parameter, including example values and instruction to use list_tags. Description adds no additional parameter semantics beyond what schema provides. 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?

Clearly states verb 'get' and resource 'random cat image matching a specific tag'. Distinguishes from sibling tools like list_tags (which discovers tags) and random_cat (which likely returns any random cat without tag filter).

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 instructs to use list_tags first to discover available tags, preventing incorrect tag values. This provides clear when-to-use guidance and distinguishes the tool's workflow dependency.

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 explains the data returned and output format (paired data + URIs), but does not disclose side effects, authorization needs, or error handling. The behavioral context is adequate but not exhaustive.

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 concise with two main sentences plus type-specific details. It front-loads the core purpose and eliminates all unnecessary words, making it easy to parse.

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

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

Given no output schema and no annotations, the description covers the essential information: what data is returned for each type, the input format, and the benefit over multiple calls. It could mention error cases, but overall it's complete enough for correct tool invocation.

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

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

Schema coverage is 100% and both parameters have descriptions. The description adds meaningful context by detailing what data is returned for each type and providing examples, going beyond the schema's basic 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 the tool compares 2-5 entities side by side, specifying two types with distinct data fields. It also highlights efficiency by replacing 8-15 sequential calls, making its purpose 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 provides clear context for when to use (comparing multiple companies or drugs) and specifies the data returned for each type. It lacks explicit when-not-to-use or alternative tools, but the context is sufficient for correct usage.

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 full burden. It discloses the tool's purpose and recommends calling it first, but does not mention potential behavioral traits like side effects (none expected), authentication requirements, rate limits, or performance characteristics (e.g., response time with large catalogs). The description is adequate but not rich in behavioral detail.

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 concise, consisting of three sentences that are front-loaded with the core purpose, then returning output, and finally usage advice. 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?

Given the tool's simplicity (search with query and optional limit, no output schema, no nested objects) and 100% schema coverage, the description is largely complete. It explains what the tool does, what it returns, and when to use it. One could argue it could mention that the output is a list of tools with names and descriptions (already stated), but it lacks mention of ranking or result format details, which are not critical for a search tool.

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

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

Schema coverage is 100%, so the schema already describes both parameters. The description adds context for the 'query' parameter by providing examples (e.g., 'analyze housing market trends') and for 'limit' by stating defaults and maximums. However, the description does not explain edge cases (e.g., empty results) or provide additional nuance beyond schema examples.

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

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

The description uses specific verb phrases: 'Search the Pipeworx tool catalog by describing what you need. Returns the most relevant tools with names and descriptions.' It clearly distinguishes itself from sibling tools, which are about memory, cats, tags, and a general Q&A, by explicitly stating its role as a tool discovery mechanism for a large catalog.

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

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

The description provides explicit guidance: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This tells the agent when to use it (first step for tool discovery) and implies it's not for direct task execution, which differentiates it from siblings like ask_pipeworx or cat_by_tag.

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

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

No annotations provided, so the description carries the full burden. It discloses that the tool returns pipeworx:// citation URIs for everything, indicating a read-only data aggregation behavior. It does not explicitly state permissions or side effects, but the nature of the tool implies non-destructive operation.

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

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

The description is a single paragraph that efficiently conveys purpose, data sources, return format, and usage alternatives. It could be more structured (e.g., bullet points) but remains concise and front-loaded with the main 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 no output schema, the description explains return values (pipeworx:// URIs). It covers the main data categories, parameter constraints, and when to use alternatives. For a complex bundled tool, it is 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%, so baseline is 3. The description adds meaning: type only supports 'company' today, and value can be ticker or CIK with an explicit note that names are not supported and to use resolve_entity. This provides critical usage context 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 provides a full entity profile across multiple Pipeworx packs, listing specific data sources (SEC filings, XBRL, patents, news, LEI). It distinguishes from siblings by noting it replaces 10–15 sequential agent calls and explicitly mentions an alternative for federal contracts.

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

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

The description provides explicit guidance: use for full entity profiles, avoid for federal contracts (use usa_recipient_profile). It also explains the value parameter accepts ticker or CIK, not names, and suggests using resolve_entity first for names.

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

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

No annotations provided, so description must carry full burden. It states deletion but lacks details on irreversibility, error handling (e.g., if key doesn't exist), or side effects.

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, no filler, front-loaded with action and resource.

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

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

Simple tool with 1 param and no output schema; description covers basics but lacks behavioral context for a destructive operation (e.g., permanence, error responses).

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

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

Schema has 100% coverage with description for 'key', but description adds no new semantics beyond 'Memory key to delete' already in 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 uses specific verb 'Delete' and resource 'stored memory by key', clearly distinguishing it from siblings like 'recall' (read) and 'remember' (create).

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 alternatives guidance, but the tool name and description imply it is for deleting a specific memory by key, which is distinct from other tools.

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

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

Annotations are empty, so description carries full burden. It clearly states the tool returns a list of tags, and implies it's a read-only operation (listing). No destructive or authentication concerns are needed for this simple listing.

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 concise sentences. The first states the action, the second provides usage guidance. Every word is useful and there is 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?

Given the tool has no parameters, no output schema, and no annotations, the description is complete. It explains what the tool does and how to use its results, which is sufficient for such a simple 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 has no parameters, so description does not need to add parameter details. It correctly notes that the tool has no required inputs.

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 all available cat tags and its purpose as a lookup for another tool (cat_by_tag). It uses specific verb 'list' and resource 'cat tags', and distinguishes from siblings like cat_by_tag and random_cat.

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 mentions using the returned tags with cat_by_tag, providing clear context for when to use this tool. However, it doesn't explicitly state when not to use it or mention 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 of behavioral disclosure. It discloses a rate limit ('Rate-limited to 5 messages per identifier per day') and that it is free. It also provides a privacy-related guideline about not including verbatim prompts. This adds value beyond the basic action of sending feedback.

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 long, starting with the core purpose, then usage guidelines, then a rate-limit note. It is front-loaded and every sentence provides essential information with no redundancy or 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 feedback tool with three parameters and no output schema, the description covers purpose, usage guidelines, rate limits, and message formatting. It is sufficient for an agent to use the tool correctly. Minor omissions (e.g., whether a response is expected) do not significantly impact completeness.

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

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

Schema coverage is 100%, baseline is 3. The description adds value by explaining the enum types in context ('Use for bug reports...') and by offering guidance on message content ('Be specific... 1-2 sentences typical, 2000 chars max'). This enriches the parameter semantics beyond the schema alone.

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

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

The description clearly states the tool's purpose: 'Send feedback to the Pipeworx team.' It lists specific use cases (bug reports, feature requests, missing data, praise) and includes a privacy guideline. This differentiates it from sibling tools, none of which serve a feedback function.

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 tells when to use the tool (for bug reports, feature requests, etc.) and provides guidance on what to include ('Describe what you tried in terms of Pipeworx tools/data') and what to avoid ('do not include the end-user's prompt verbatim'). It also mentions rate limits. While it does not explicitly state when not to use it, the context is clear.

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

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

Annotations already mark the tool as read-only and non-destructive. The description adds behavioral traits: it returns ranked opportunities with suggested trade direction and reasoning, and explains the two execution modes. This supplements annotations 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 moderately long but every sentence adds value: purpose, mode breakdown, example, return format. It is front-loaded with the main action. Minor redundancy ('TWO MODES' then separately listed) but not excessive.

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 output: ranked opportunities with direction and reasoning. It covers input parameters, two modes, and the logic (monotonicity checking). No gaps for an agent to determine correct usage.

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

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

Schema coverage is 100%, with both parameters described. The description goes beyond by explaining how each parameter corresponds to a mode (event slug for event mode, topic for cross-event), and gives usage examples. This adds semantic richness beyond the schema's one-line 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 opens with a clear verb and resource: 'Find arbitrage opportunities on Polymarket by checking for monotonicity violations across related markets.' It distinguishes two modes (event vs topic) and contrasts with sibling tools implicitly by specifying its unique cross-event capability, which other tools likely lack.

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 details when to use each mode: event mode for a single event's child markets, topic mode for cross-event discovery. It provides an example where single-event mode fails and cross-event succeeds, guiding the agent on selection. No explicit mention of alternatives like polymarket_edges, but the context is clear.

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

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

The description adds substantial behavioral context beyond the annotations. It details the V1 model (lognormal from FRED + live coinpaprika price), the process (scan markets, group by asset, fetch price history once, compute model probability, rank by |edge|), and the output format (top N with suggested trade direction). No contradictions with annotations (readOnlyHint, openWorldHint, destructiveHint).

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 informative, with the first sentence front-loading the core purpose. It is slightly lengthy but every sentence adds value. A minor redundancy exists (mentioning 'top markets' twice) but overall 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 3 parameters, no output schema, and annotations present, the description covers the algorithm, data sources, and usage scenario comprehensively. It does not detail the exact return format beyond ranked edges, but the purpose is clear and sufficient for an agent to use correctly.

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

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

Schema coverage is 100% for all three parameters. The description adds minor context (e.g., default 10, max 25 for limit; default 1wk for window; default 0.5 for min_edge_pp) but these are already in the schema descriptions. The baseline is 3, and the description does not significantly enhance parameter understanding.

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 ('Scan'), the resource ('highest-volume Polymarket markets'), and the specific outcome ('return the ones where Pipeworx data disagrees most with the market price'). It distinguishes the tool by explicitly mentioning its purpose for discovering betting opportunities without paging through markets, which differentiates it from siblings 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?

The description provides clear context for when to use this tool: it is built for the 'what should I bet on today' question, indicating it is for opportunity discovery. However, it does not explicitly state when not to use it or mention alternative tools, though the sibling list implies alternatives like bet_research or polymarket_arbitrage.

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 burden of behavioral disclosure. It states the tool is a read operation (get) and returns specific fields. However, it does not mention external service dependency, potential latency, or rate limits. No contradictions 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?

Extremely concise: two sentences that clearly state purpose and output. No wasted words.

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

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

Given zero parameters and no output schema, the description adequately covers what the tool does and what it returns. It could mention that the image is a URL or that tags might be empty, but it's sufficient for a simple 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 has no parameters, so description correctly adds meaning by explaining the tool takes no input and returns random data. This is adequate and adds value beyond the empty schema.

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

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

Clearly states the tool returns a random cat image from CATAAS, listing what is returned (URL, ID, tags). While it distinguishes from 'cat_by_tag' implicitly, it does not explicitly differentiate from other cat-related 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?

No explicit when-to-use or when-not-to-use guidance. The description implies usage for random cat images but does not mention alternatives like 'cat_by_tag' for tagged cats. No exclusion criteria are provided.

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

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

No annotations provided, but description discloses that omitting key lists all memories. However, does not mention side effects (e.g., whether retrieval modifies memory) or access/rate limits.

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

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

Two concise sentences that front-load the purpose and usage. 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 one optional parameter and no output schema, the description sufficiently covers purpose and usage. Could mention return format briefly, but not essential.

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

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

Schema covers 100% of parameters. Description restates the key parameter's behavior (omit to list all), but does not add new details 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?

Description clearly states the tool retrieves a memory by key or lists all memories when key is omitted, distinguishing it from remember (save) and forget (delete).

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 (to retrieve context from earlier), and implies when not to use (for saving/forgetting). Differentiates from sibling tools remember and forget.

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 effectively discloses key behavioral traits: 'fans out to SEC EDGAR (filings since), GDELT (news mentions in window), USPTO (patents granted since), in parallel.' It also describes the return format (structured changes + total_changes count + URIs) and accepted date formats. While it doesn't cover rate limits or error handling, it provides substantial transparency.

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

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

The description is concise: two sentences. The first sentence establishes the primary purpose, and the second packs specific behaviors and return details. While it is front-loaded with a general statement, it quickly provides critical specifics. No wasted words.

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

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

Given high complexity (multi-source fan-out, flexible date parsing, structured return) and no output schema, the description covers key aspects: sources, parallelism, date formats, and return fields (structured changes, total_changes, URIs). It lacks pagination or error details but is fairly complete for typical use.

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

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

Schema coverage is 100%, so baseline is 3. The description adds some value by explaining the 'since' parameter formats with examples (ISO date and relative), but this largely echoes the schema description. For 'type' and 'value', it reiterates the schema content without additional meaning. Hence, marginal added 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?

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It details the specific behavior for type=company, including fan-out to multiple sources. While it doesn't explicitly differentiate from siblings like entity_profile, the use cases ('brief me on what happened with X' or 'change-monitoring workflows') provide clear context.

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 directly states when to use the tool: 'Use for "brief me on what happened with X" or change-monitoring workflows.' It also explains the parallel fan-out to multiple sources. However, it does not mention when not to use or suggest alternatives, which would be helpful.

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 key behavioral traits: memory persistence depends on authentication (persistent vs 24-hour). No annotations provided, so description carries full burden; it addresses the most important aspect. Could mention that overwriting a key replaces the previous value, but not a major gap.

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

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

Three sentences, no fluff. Front-loaded with core action, then usage guidelines, then behavioral note. 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 tool simplicity (2 params, no output schema, no annotations), description fully covers purpose, usage, and behavioral context. 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?

Schema coverage is 100% with good descriptions. Description adds usage context (what kinds of values to store) but does not need to add more since schema already explains parameters well. Baseline 3, +1 for reinforcing practical examples.

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

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

Description uses specific verb 'store' and resource 'key-value pair' in session memory. It clearly distinguishes from sibling tools like 'forget' and 'recall' by naming them and explaining the scope.

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

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

Explicitly states when to use: to save intermediate findings, user preferences, or context across tool calls. Also notes persistence differences between authenticated vs anonymous users, guiding 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?

No annotations are provided, so the description must carry the full burden. It discloses accepted input types (ticker, CIK, name) and return fields (ticker, CIK, company name, URIs). However, it does not explicitly state that the operation is read-only or idempotent, and it omits behavioral traits like authentication needs or side effects.

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, no wasted words. The first sentence states the core purpose, the second adds specifics, and the third highlights the value proposition. Perfectly front-loaded and concise.

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 purpose, parameter semantics, and return fields adequately despite lacking an output schema. It does not address error cases or rate limits, but for a simple resolution tool, the information is sufficient. Sibling tools are unrelated, so no confusion arises.

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%, providing type and value descriptions. The description adds value with concrete examples (AAPL, CIK, Apple) and clarifies v1 supports only company. This provides practical guidance beyond the schema, justifying a score above the baseline of 3.

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

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

The description clearly states the tool resolves an entity to canonical IDs across Pipeworx data sources. It uses a specific verb ('Resolve') and resource ('entity to canonical IDs'), and distinguishes from unrelated sibling tools like ask_pipeworx or cat_by_tag.

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 that this tool replaces 2-3 lookup calls, implying efficiency use. However, it does not explicitly state when not to use it or provide alternative tools. Since siblings are unrelated, the guidance is adequate but not explicit.

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 the output (verdict with five possible values, structured form, actual value with citation, percent delta) and explains it replaces sequential agent calls. However, it does not explicitly state whether the tool is read-only or destructive, but the nature of fact-checking implies read-only. The description provides good 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 at 3-4 sentences, front-loaded with the main purpose, and each sentence adds unique value: purpose, scope, output, and efficiency benefit. 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 one parameter and no output schema, the description provides a good overview of the tool's capabilities and limitations. It explains the supported domain and output format but could be more explicit about unsupported claim types or potential edge cases. Overall, fairly complete for a v1 tool.

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

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

The single 'claim' parameter has a clear schema description with examples. The overall description reiterates the parameter's purpose and adds context about supported claim types. Since schema coverage is 100%, the baseline is 3, and the description adds value with examples and scope guidance, earning a 4.

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 claims against authoritative sources, specifies it supports company-financial claims for US public companies via SEC EDGAR + XBRL, and lists the types of metrics and verdicts. It also distinguishes itself by noting it replaces 4-6 sequential agent calls, making its purpose and scope very clear.

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 the supported claim domain (company-financial for US public companies) and the specific metrics (revenue, net income, cash). It implies when to use (fact-checking such claims) and when not (other types of claims). However, it does not explicitly mention alternatives or when-not-to-use beyond the scope.

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