Giphy

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

With no annotations provided, the description carries full burden for behavioral disclosure. It explains that the tool internally selects the best data source and fills arguments, which is a useful behavioral trait. However, it does not mention potential side effects, rate limits, or any constraints on what types of questions are supported. The description is honest but not fully 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 concise at three sentences plus examples. It front-loads the core purpose and then provides additional context and examples. It could be slightly more structured (e.g., a bullet list of examples), but overall it is efficient and informative.

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 (single required parameter, no output schema, no nested objects), the description provides sufficient context for an agent to decide to use it and how to invoke it. The examples clarify the expected input format. The description is complete enough for this tool's complexity.

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

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

The input schema has 100% coverage with a single parameter 'question' described as 'Your question or request in natural language'. The description adds value by elaborating that the question should be in plain English and provides examples. This enhances the schema's minimal description.

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

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

The description clearly states the tool accepts natural language questions and returns answers from the best available data source. It distinguishes itself from siblings by explicitly noting that Pipeworx handles tool selection and argument filling, so the user doesn't need to browse other tools. This effectively differentiates it from the other tools listed.

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 the tool: when you want to ask a question in plain English and get an answer without manually selecting tools or learning schemas. It provides examples of appropriate queries. However, it does not explicitly state when not to use it or mention alternatives, though the sibling tools are different enough (e.g., discover_tools, search_gifs) that 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 indicate read-only, open-world, non-destructive. The description adds rich behavioral details: resolves market, classifies bet type, fans out to relevant packs, 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 a single paragraph with front-loaded purpose. Every sentence adds useful information. Slightly lengthy but justified given the tool's complexity; 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?

Given the tool's complexity (classification, fan-out, comparison) and no output schema, the description adequately explains the return: evidence packet and market-vs-model comparison. It covers inputs, behavior, and outputs sufficiently for an agent to understand what the tool does.

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 3. The description adds value by explaining the market parameter accepts slug, URL, or question text, and the depth parameter's quick vs thorough options with concrete numbers (2-3 sources vs full fan-out).

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: research a Polymarket bet by pulling Pipeworx data. It specifies inputs (slug, URL, or question text) and outputs (evidence packet and comparison). This verb+resource combination is specific and distinct from siblings.

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?'. It also positions itself as the core demo product, implying preference over manual discovery, but does not directly compare with sibling tools like ask_pipeworx.

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

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

With no annotations provided, the description must fully disclose behavioral traits. It states the tool returns paired data and pipeworx:// URIs, and hints at read-only behavior by mentioning data sources. However, it lacks details on error handling, rate limits, or what happens with invalid inputs, leaving some ambiguity for an AI agent.

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

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

The description is extremely concise, with two sentences that cover purpose, use cases, input format, and return values. It is front-loaded with the main action and efficiently packs all necessary information without any 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 the absence of an output schema, the description explains that the tool returns 'paired data + pipeworx:// resource URIs', which gives a reasonable idea of the output structure. It covers the main functionality for two entity types and mentions the efficiency gain, but could be slightly more explicit about the exact format of the paired data.

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 the baseline is 3. The description adds value by explaining the meaning of the 'type' parameter and mapping it to specific data fields, and by providing concrete examples for the 'values' parameter (e.g., tickers or drug names). This goes beyond the schema's basic 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 defines the tool's purpose: comparing 2–5 entities side by side in one call. It specifies the two types (company and drug) and lists the specific data fields for each, such as revenue and net income for companies, and adverse-event reports for drugs. It also distinguishes itself by stating it replaces 8–15 sequential agent calls, setting it apart from similar 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 strong usage context by explaining that this tool replaces multiple sequential calls, implying efficiency gains. It also details the data sources (SEC EDGAR for companies, FDA for drugs) and required inputs (tickers/CIKs or drug names). However, it does not explicitly state when not to use it or mention alternative approaches beyond the sequential calls reference.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states it returns 'the most relevant tools with names and descriptions,' but does not detail what 'most relevant' means (e.g., ranking algorithm), whether results are ordered, or any side effects. For a search tool, this is adequate but not exhaustive; a 3 is appropriate as it does not contradict any annotations (none exist).

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

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

The description is two sentences long, with no wasted words. The first sentence states the action, the second provides crucial when-to-use guidance. Every sentence earns its place, and the structure is 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 tool's simplicity (2 parameters, no output schema, no nested objects), the description is fairly complete. It explains the tool's role as a discovery mechanism and provides usage context. However, it lacks details on output format or whether results are ranked, but with no output schema, the description could be more explicit. Still, for a search tool, it is nearly complete, hence a 4.

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

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

Schema description coverage is 100%, so the schema already documents both parameters. The description adds value by giving an example query format ('analyze housing market trends') which clarifies the intent of the 'query' parameter beyond the schema's description. However, the 'limit' parameter is not elaborated upon in the description; the schema already provides its default and max. With full schema coverage, the baseline is 3, and the added example raises it to 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 explicitly states the tool's purpose: 'Search the Pipeworx tool catalog by describing what you need. Returns the most relevant tools with names and descriptions.' The verb 'search' and resource 'tool catalog' are specific, and it clearly distinguishes itself from siblings like ask_pipeworx or search_gifs by focusing on tool discovery.

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

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

The description provides clear when-to-use guidance: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This advises the agent to use it as an initial step before invoking specific tools, implying it is a routing or discovery tool. No alternatives are explicitly named, but the context makes its role unique among siblings.

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 that it bundles multiple sources, returns citation URIs, and only supports company type with ticker/CIK. No annotations provided, but description carries full 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?

Concise yet packed with information. Front-loads purpose then lists contents. 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?

Covers return format, supported identifiers, limitations, and alternatives. Without output schema, description adequately explains what the agent can expect.

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

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

Adds meaning beyond schema by explaining value accepts ticker or CIK, and notes names are unsupported, pointing to resolve_entity. Schema coverage is 100% but description adds essential usage context.

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

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

The description clearly states it returns a full profile of an entity across multiple packs, listing specific data sources. It distinguishes itself from siblings like resolve_entity and usa_recipient_profile.

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

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

Explicitly says when to use it (replaces multiple sequential calls) and when not to (federal contracts use usa_recipient_profile). Also advises using resolve_entity 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 carries full burden. It states it deletes a memory, implying irreversibility, but lacks detail on confirmation, error handling, or impact on related data.

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, zero waste, front-loaded with verb 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 one required parameter, no output schema, no nested objects. Description is adequate for a straightforward deletion, but lacks detail on what happens upon success/failure.

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% (key parameter is documented in schema). Description adds 'stored memory' context but does not go beyond what schema already says about the key parameter.

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

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

The description clearly states the action 'Delete' and the resource 'stored memory by key', distinguishing it from sibling tools like 'remember' (store) and 'recall' (retrieve).

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

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

No guidance on when to use this tool vs alternatives like 'recall' or 'remember'. It does not mention prerequisites or side effects.

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. It discloses rate limiting (5 per day per identifier) and warns against including full prompts. But it lacks details on authentication, anonymity, or what happens after sending.

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 (4 sentences), front-loading the main purpose and use cases, then adding constraints. 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?

For a simple feedback tool without an output schema, the description covers purpose, input format, and rate limits. It is complete enough, though it could mention if a confirmation response is sent.

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 general advice (be specific) but does not provide additional meaning beyond the schema's parameter 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: sending feedback to the Pipeworx team, with specific use cases (bug, feature, data_gap, praise). It is distinct from sibling tools, which are mainly query or entity 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 usage scenarios (bug reports, feature requests, etc.) and includes a constraint (do not include user prompt verbatim) and a rate limit. However, it does not explicitly exclude cases where other tools might be more appropriate.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds behavioral details: it walks child markets, extracts dates/thresholds, sorts, and reports violations. 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?

The description is moderately long but well-structured: it starts with the purpose, explains the underlying logic, then details usage and output. Every sentence is informative, though minor tightening could be possible.

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 single parameter, no output schema, and moderate complexity, the description fully informs the agent about the expected input (event slug/URL) and output format (list of arbitrage entries). It leaves no crucial 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?

With 100% schema coverage and a single parameter 'event' having a description in the schema, the description adds value by explaining what constitutes an event slug or URL and the resulting process, going beyond the schema's basic definition.

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

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

Description explicitly states 'Find arbitrage opportunities within a Polymarket event by checking for monotonicity violations.' It specifies a unique verb+resource and clearly distinguishes itself from sibling tools like bet_research or polymarket_edges.

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 the tool: when an event has multiple 'by date' or 'by threshold' markets. It provides clear context by describing the expected input and the process, though it does not explicitly list scenarios where the tool should not be used.

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

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

Annotations already indicate read-only and non-destructive behavior. The description adds substantial transparency about the algorithm: uses lognormal model from FRED + live coinpaprika price, groups by asset, fetches price history once, computes model probability, and ranks by |edge|. No contradiction with annotations.

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

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

The description is a single paragraph that fronts the main purpose, then explains V1 details. It is appropriately sized for the complexity, though a minor restructuring 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?

No output schema exists, but the description fully explains what is returned: top N markets ranked by edge magnitude with suggested trade direction. It covers the context of the tool for daily opportunity discovery, leaving 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 coverage is 100% with descriptive parameter names and defaults. The description adds minimal extra meaning beyond the schema, such as context that 'limit' controls the top N edges. 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 states a specific verb ('scan') and resource ('Polymarket markets'), clearly explaining that it finds markets where Pipeworx data disagrees with market price. It distinguishes from siblings like polymarket_arbitrage by emphasizing the comparison with Pipeworx model data.

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 tool is built for the 'what should I bet on today' question, clearly indicating when to use it for opportunity discovery. However, it does not explicitly state when not to use it or mention alternatives, though context signals provide sibling names for differentiation.

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

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

Annotations are empty, so the description carries the burden. It correctly indicates the tool is non-destructive (read-only). However, it does not disclose rate limits, API key requirements, or behavior when no GIF matches the tag (e.g., returns empty or error).

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 sentences, no wasted words. It front-loads the action and scope, then lists return fields. 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 low complexity (1 optional param, no output schema, no nested objects), the description is sufficient: it explains the tool's action, optional filtering, and return fields. No gaps remain for basic 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% (the only parameter 'tag' is described). The description adds context that the tag is optional and gives examples. This is clear and helpful, earning a score above baseline 3.

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

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

The description clearly states the tool retrieves a single random GIF from Giphy with optional tag filtering. It also lists the return fields, making the purpose specific and distinguishable from siblings like search_gifs and trending_gifs.

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 does not explicitly state when to use this tool versus siblings (e.g., search_gifs for non-random results, trending_gifs for popular ones). Usage is implied as 'for a random GIF,' but no alternatives 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?

The description mentions retrieving context from earlier sessions, implying persistence, but does not disclose any potential side effects or limitations. With no annotations, the description could provide more behavioral details such as whether retrieval is destructive or if there are 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?

The description is concise with two sentences, front-loading the main action and providing a usage tip. No unnecessary words.

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

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

Given the simplicity of the tool (single optional parameter, no output schema), the description adequately covers its purpose and usage. It lacks details about return format or error handling, but for a simple retrieval tool, this is sufficient.

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 describes the 'key' parameter, and the description adds context that omitting the key lists all memories. This adds value beyond the schema's description.

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

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

The description clearly states the tool retrieves a memory by key or lists all memories. It distinguishes itself from sibling tools like 'remember' and 'forget' by focusing on retrieval.

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 omit the key to list all memories, and when to provide a key for specific retrieval. It also mentions using it to retrieve context from earlier sessions, but does not explicitly say when not to use it or suggest 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 are provided, so the description carries full burden. It discloses that for type='company' the tool fans out to three sources in parallel, returns structured changes plus total_changes count and pipeworx:// URIs. This is thorough behavioral disclosure with 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 two sentences with excellent front-loading. The first sentence states the core purpose, the second adds key details (fan-out, date formats, return structure). 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?

Despite no output schema, the description explains return structure (structured changes + total_changes + URIs). Parameter coverage is 100% and the description covers purpose, usage, behavior, and parameter details. For a complex tool with multiple data sources, this is complete.

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

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

Schema coverage is 100%, baseline 3. The description adds value by explaining that 'since' accepts ISO dates or relative strings with examples, recommends '30d'/'1m', and clarifies that 'value' can be a ticker or CIK. This adds understanding beyond the schema.

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

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

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It provides specific examples for type='company' and details the parallel fan-out to SEC EDGAR, GDELT, and USPTO. This specificity distinguishes it from siblings like entity_profile or compare_entities.

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 'Use for "brief me on what happened with X" or change-monitoring workflows,' providing clear usage context. While it doesn't explicitly state when not to use or name alternatives, the use case is well-defined and 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?

With no annotations provided, the description carries the full burden. It discloses persistence behavior: authenticated users get persistent memory, anonymous sessions last 24 hours. This adds useful context beyond the basic schema.

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

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

Three sentences, front-loaded with the core action, then usage guidance, then persistence details. 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 no output schema and simple parameters, the description covers purpose, usage, and behavior. It could mention idempotency or whether overwriting an existing key is allowed, but overall it's fairly 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 description coverage is 100%, so the description adds only marginal value by providing usage examples for keys. 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 'Store' and the resource 'key-value pair in your session memory'. It distinguishes from siblings like 'forget' and 'recall' by specifying the action of saving, not retrieving or deleting.

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?

It explicitly states to use this for saving intermediate findings, user preferences, or context across tool calls. However, it does not mention when not to use it or alternatives, but the context is clear and practical.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns ticker, CIK, company name, and pipeworx:// URIs. It does not discuss side effects, authentication, or errors, but for a read-like operation this is sufficient and consistent.

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: two sentences with no waste. The first sentence states the purpose, and the second adds critical details. It is front-loaded and efficient.

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

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

Given the low complexity (2 params, 1 enum, no output schema), the description covers the purpose, inputs, and outputs. It does not explain error handling or output structure, but for a simple tool this is nearly complete. Slightly incomplete but still good.

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 examples and confirms the 'company' enum value, providing marginal value beyond the schema. This slight addition justifies 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 resolves an entity to canonical IDs across Pipeworx data sources. It specifies the entity type (company) and accepts ticker, CIK, or name. It distinguishes from sibling tools implicitly by noting it replaces multiple lookup calls, and no other sibling does entity 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?

The description explicitly says v1 only supports 'company' type, and provides example inputs (ticker, CIK, name). It implies when to use: when needing canonical IDs. It does not explicitly list alternatives or when not to use, but given sibling tools are unrelated, this is adequate.

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 description carries full burden. It discloses return fields (title, URL, rating, image URLs), which adds behavioral transparency beyond the schema. However, it does not mention potential rate limits or authentication needs.

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

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

Two short sentences, no fluff, front-loaded with action and resource. Efficiently uses space.

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 only 2 simple parameters, the description provides a good overview of what the tool returns. Could mention pagination or ordering, but not essential for a simple search.

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

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

Schema coverage is 100%, so baseline is 3. Description does not add extra meaning beyond schema, as the schema already describes query and limit parameters clearly.

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

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

Description clearly states it searches Giphy for GIFs matching a keyword or phrase, listing the specific return fields. It distinguishes itself from sibling tools like random_gif and trending_gifs.

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?

Description implies usage for searching GIFs by keyword, but does not explicitly state when to use this vs. sibling tools like random_gif or trending_gifs, nor are there alternatives 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 provided, so description carries full burden. It states returns title, URL, rating, and image URLs, but does not disclose rate limits, whether this is a read-only operation, or data source (Giphy). However, 'trending' implies non-destructive, and the lack of annotations is not a 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?

Two sentences, concise and front-loaded. First sentence states purpose, second lists returned fields. No filler, each 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 a simple tool with one optional parameter and no output schema, description adequately covers purpose and return fields. Slight gap in not mentioning that results are from Giphy API, but 'on Giphy' is mentioned.

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 for the single 'limit' parameter, which is described in the schema. Description adds no extra meaning beyond the schema, so baseline of 3 is appropriate.

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

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

Description clearly states verb 'get', resource 'trending GIFs', and returns title, URL, rating, and image URLs. Distinguishes from sibling 'random_gif' and 'search_gifs' by specifying trending 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?

No explicit when/when-not or alternative tools mentioned, but context signals show a single optional parameter, implying simple usage. Sibling names like 'random_gif' and 'search_gifs' suggest alternatives, but description doesn't differentiate.

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

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

No annotations are provided, so the description must fully disclose behavior. It mentions the data sources and return types but fails to address error handling, safety, side effects (e.g., if this is a read-only operation), or behavior on malformed input. The description lacks behavioral details beyond functionality.

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 four sentences, front-loaded with the core purpose, then scope, return details, and advantage. Every sentence adds value without redundancy. It is concise and 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?

With a single parameter and no output schema, the description adequately covers input format, supported claim types, and output fields. However, it lacks discussion of error conditions (e.g., unsupported claim topic) and expected behavior for edge cases, which would improve completeness.

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

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

Schema coverage is 100% with a clear description and examples for the 'claim' parameter. The tool description adds meaningful constraints (specific financial metrics, US company scope, v1 limitations) that go beyond the schema, enhancing parameter understanding. Score is above baseline due to added context.

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

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

The description clearly states the tool fact-checks natural-language claims against authoritative sources, specifies the domain (company-financial claims for US companies) and data sources (SEC EDGAR + XBRL), and lists return fields. It distinguishes itself from siblings by describing it as a composite replacement for multiple sequential agent calls.

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

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

The description explicitly states supported claim types (revenue, net income, cash for public US companies) and explains it replaces a multi-step agent process, giving clear context. However, it does not explicitly state when not to use it (e.g., non-financial claims, non-US companies) or suggest alternative tools.

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