jokes

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it explains Pipeworx 'picks the right tool, fills the arguments, and returns the result' - describing the agent's decision-making process. However, it doesn't mention limitations like rate limits, authentication needs, or potential failure modes, leaving some behavioral aspects unspecified.

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

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

The description is perfectly structured and concise: a clear purpose statement, explanation of the automation benefit, and three diverse examples - all in three sentences. Every sentence earns its place by providing distinct value: the what, the how, and concrete illustrations of appropriate use.

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

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

For a single-parameter tool with no output schema and no annotations, the description provides excellent context about what the tool does and how to use it. The examples effectively demonstrate the expected input format and scope. The only minor gap is lack of information about return values or error conditions, but given the tool's simplicity, this 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 description coverage is 100% with the single parameter 'question' well-documented in the schema. The description adds minimal parameter semantics beyond the schema, only reinforcing that questions should be in 'plain English' or 'natural language' through the examples. This meets the baseline 3 score when schema coverage is high.

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

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

The description clearly states the tool's purpose with specific verbs ('Ask a question', 'get an answer') and resources ('from the best available data source'), distinguishing it from sibling tools like joke-related or memory tools. It explicitly mentions Pipeworx handles tool selection and argument filling, which is unique functionality not implied by the name alone.

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

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

The description provides explicit usage guidance: 'No need to browse tools or learn schemas — just describe what you need' establishes when to use this tool versus alternatives. The three concrete examples ('What is the US trade deficit...', 'Look up adverse events...', 'Get Apple's latest...') further illustrate appropriate use cases, making it clear this is for natural language queries rather than structured tool calls.

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

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

Annotations (readOnlyHint=true, openWorldHint=true, destructiveHint=false) already indicate a safe read operation. The description adds rich behavioral details: market resolution, bet classification, fan-out to appropriate packs based on bet type, and return of evidence packet and comparison.

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 dense paragraph that packs significant information. It is front-loaded with the main action. Minor structural improvements could be made, but it remains efficient and not overly verbose.

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

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

Given the tool's complexity (multiple input types, classification, pack fan-out), the description covers inputs, processing, and outputs. The absence of an output schema is compensated by specifying returns ('evidence packet plus a simple market-vs-model comparison'). Lacks mention of potential errors or rate limits, but overall 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%, but the description adds value by explaining the 'depth' parameter (quick vs thorough, default thorough) and that 'market' can be a slug, URL, or question text. It also provides context on fan-out behavior beyond schema.

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

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

The description clearly states the verb 'Research' and the resource 'Polymarket bet', specifying it pulls Pipeworx data. It differentiates from siblings by focusing on bet research and mentions input types (slug, URL, question text) and output (evidence packet + comparison).

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

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

The description explicitly provides use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It implies this is the preferred tool for bet research over discovering packs individually, but does not explicitly state when not to use it.

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

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

No annotations are provided, so the description bears the full burden. It implies a read-only operation by stating 'Returns data', but it does not explicitly confirm idempotency, auth requirements, or potential side effects. The description is adequate for a straightforward query tool but lacks explicit behavioral disclosure.

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 exceptionally concise, using three sentences to cover all key points: the action, the two modes, and the benefit. Every sentence provides value, with no redundancy or filler.

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

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

Given the absence of an output schema, the description explains the return values by listing the data fields per type and mentioning resource URIs. It is mostly complete but could be more precise about the output structure (e.g., 'returns a JSON object with an array of comparison results').

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 description has limited room to add value. It contextualizes the parameters by explaining what data is returned for each type, but does not add detail beyond the schema's descriptions and examples. Baseline 3 is appropriate.

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

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

The description clearly states the tool compares 2–5 entities side by side, specifies two entity types with concrete data fields (e.g., revenue, net income for companies), and highlights that it replaces many sequential calls. This distinguishes it from siblings like resolve_entity, which handles single 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 provides clear context by listing the two use cases (company vs drug) and emphasizes efficiency by saying it replaces 8–15 agent calls. However, it does not explicitly state when not to use this tool or mention alternatives like resolve_entity for single entities.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'the most relevant tools with names and descriptions,' which adds useful behavioral context about the output format. However, it lacks details on performance (e.g., response time), error handling, or authentication needs, leaving some gaps in behavioral 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 appropriately sized and front-loaded: it starts with the core purpose, then specifies usage guidelines, all in two concise sentences with zero wasted words. Every sentence earns its place by adding critical information.

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

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

Given the tool's complexity (a search/discovery function with 2 parameters, no output schema, and no annotations), the description is mostly complete. It covers purpose, usage context, and output format, but lacks details on behavioral aspects like rate limits or error cases, which could be important for a discovery tool in a large catalog.

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 (query and limit) thoroughly. The description does not add any parameter-specific information beyond what the schema provides (e.g., it doesn't explain query semantics further). Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resource ('tool catalog'), and explicitly distinguishes it from siblings by mentioning '500+ tools available' which contrasts with the joke-related sibling tools listed. It provides a concrete action and target.

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

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

The description provides explicit usage guidelines: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This clearly indicates when to use this tool (for discovery in large catalogs) versus alternatives (implicitly, the sibling tools are for specific joke operations, not discovery).

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 return format (pipeworx:// URIs) and efficiency (replaces 10-15 calls). No annotations provided. Could mention rate limits or pagination, but sufficient for a read 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?

Front-loaded with purpose, then details data sources and return format. One sentence about alternative tool. Efficient but slightly dense; could be split for 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?

Covers all necessary aspects: purpose, parameters, scope, alternatives, and return format. No output schema, but description mentions citation URIs. Adequate for a complex multi-source 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?

Input schema has 100% coverage; description adds context: 'type' is limited to 'company', 'value' explains ticker/CIK format and advises using resolve_entity for names. Adds value beyond schema.

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

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

Explicitly states verb ('get full profile'), resource ('entity'), and specific data sources (SEC, XBRL, patents, news, LEI). Clearly distinguishes from sibling tools like resolve_entity.

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

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

Provides explicit alternative: 'For federal contracts call usa_recipient_profile directly'. Also implies when to use this tool (comprehensive profile) and when not (federal contracts).

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool deletes a memory, implying a destructive mutation, but lacks critical details: whether deletion is permanent or reversible, what permissions are required, if there are rate limits, or what happens on success/failure. For a destructive operation with zero annotation coverage, this is insufficient.

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

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

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core action ('Delete') and immediately specifies the target, making it easy to parse quickly.

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

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

For a destructive tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects (permanence, permissions), error conditions, or return values, leaving significant gaps for the agent to operate safely and 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 description coverage is 100%, with the single parameter 'key' documented as 'Memory key to delete'. The description adds minimal value beyond this, merely restating 'by key' without explaining key format, source, or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the action ('Delete') and target resource ('a stored memory by key'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'recall' (likely for retrieving memories) or 'remember' (likely for storing memories), which would require explicit comparison for a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing memory to delete), exclusions, or relationships to sibling tools like 'recall' or 'remember', leaving the agent to infer usage context independently.

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 mentions 'safe mode' and filtering options, but lacks details on behavioral traits such as rate limits, authentication needs, error handling, or what 'random' entails (e.g., source, freshness). For a tool with no annotations, this is a significant gap in disclosure.

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

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

The description is appropriately sized and front-loaded, stating the core purpose first followed by optional features in a single, efficient sentence. Every part earns its place without waste.

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 low complexity (3 optional parameters, no output schema, no annotations), the description is minimally complete but lacks depth. It covers what the tool does and parameters, but without annotations or output schema, it should ideally include more on behavior or results to be fully helpful.

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 all parameters (type, category, safe_mode) with descriptions and defaults. The description adds minimal value by listing the parameters but does not provide additional meaning beyond what the schema specifies, meeting the baseline for high coverage.

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

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

The description clearly states the verb ('Get') and resource ('a random joke'), and distinguishes it from siblings by specifying it's for retrieving a single random joke rather than categories, flags, or search results. It's specific about the core functionality.

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

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

The description implies usage by mentioning optional filters (category, type, safe mode), but does not explicitly state when to use this tool versus alternatives like 'search_jokes' for non-random queries or 'get_joke_categories' for listing categories. No exclusions or clear context for sibling differentiation 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 are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool lists categories but does not describe any behavioral traits, such as whether it's a read-only operation, if there are rate limits, or what the output format might be. This is a significant gap for a tool with zero annotation coverage.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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 complexity of the tool (simple list operation) but lack of annotations and no output schema, the description is incomplete. It does not explain what the return values look like (e.g., format of categories) or any behavioral context, which is necessary for the agent to use the tool effectively without structured 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 tool has 0 parameters, and the schema description coverage is 100%, so there is no need for parameter details in the description. The baseline for 0 parameters is 4, as the description appropriately does not add unnecessary parameter information beyond what the schema already covers.

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 ('List all available joke categories') and the resource ('supported by JokeAPI'), making the purpose specific and understandable. However, it does not explicitly differentiate this tool from its siblings (like 'get_joke' or 'search_jokes'), which would require mentioning that this tool retrieves categories rather than jokes 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 no guidance on when to use this tool versus alternatives. It lacks context about prerequisites, such as whether authentication is needed, or comparisons to sibling tools like 'get_joke' or 'search_jokes' that might also involve categories. This leaves the agent without explicit usage instructions.

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 implies a read-only operation ('List') but doesn't disclose behavioral traits such as rate limits, authentication needs, or response format. The description is minimal and lacks context beyond the basic action.

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

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

The description is a single, efficient sentence that front-loads the key information ('List all available joke flags') without any wasted words. It's appropriately sized for a simple tool with no parameters.

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 (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on usage context, behavioral traits, or output, which could be helpful for an agent despite the simplicity.

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 tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, aligning with the schema. A baseline of 4 is applied as it compensates adequately for the lack of parameters.

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

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

The description clearly states the tool's purpose with a specific verb ('List') and resource ('all available joke flags'), and identifies the domain ('JokeAPI'). It doesn't explicitly differentiate from sibling tools like 'get_joke_categories', but the resource specificity makes the purpose 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 provides no guidance on when to use this tool versus alternatives like 'get_joke' or 'search_jokes'. It mentions the resource but doesn't explain the context or prerequisites for retrieving joke flags versus jokes themselves.

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 rate limit (5 messages per identifier per day) and notes it is 'Free'. No other behavioral traits are mentioned, but given the simplicity of a feedback tool, this is adequate.

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

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

Three sentences, front-loaded with purpose, followed by use cases and important constraints. Every sentence earns its place with no wasted words.

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

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

The description covers basic usage and constraints. However, it lacks details about the return value or confirmation, which would be helpful but not critical for a feedback 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 description adds only marginal value beyond the schema. The description advises on message content but 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 'Send feedback' and the resource 'Pipeworx team', and lists specific use cases (bug reports, feature requests, missing data, praise). It is distinct from sibling tools.

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

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

The description explicitly states when to use (bug reports, feature requests, etc.) and provides guidance on what to include and what not to include (end-user prompt). It also mentions the rate limit. However, it does not explicitly exclude any use cases or mention alternatives.

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

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 describes the core functionality (retrieve by key or list all) and persistence across sessions, but doesn't mention error handling, performance characteristics, or what happens when a non-existent key is requested. It provides basic context but lacks comprehensive behavioral details.

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

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

The description is perfectly concise with two sentences that each serve distinct purposes: the first explains the core functionality, the second provides usage context. There is zero wasted language, and information is front-loaded appropriately.

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

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

For a single-parameter tool with no output schema and no annotations, the description provides adequate context about what the tool does and when to use it. However, it doesn't describe the return format (what a 'memory' looks like when retrieved) or error conditions, leaving some gaps in 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?

The schema has 100% description coverage, so the baseline is 3. The description adds meaningful context by explaining the semantic effect of omitting the key parameter ('omit to list all keys'), which clarifies the tool's dual behavior beyond what the schema alone provides. This elevates the score above baseline.

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

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

The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from sibling tools like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations.

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

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

The description provides explicit guidance on when to use this tool: 'to retrieve context you saved earlier in the session or in previous sessions.' It also specifies when to omit the key parameter ('omit key to list all keys'), giving clear operational instructions.

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, description carries full burden. Discloses parallel fan-out to multiple sources, parameter formats, and return structure. Does not discuss limitations or error cases, but covers core behavioral traits adequately.

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 – three sentences covering purpose, behavior, and return value. No wasted words. Information is front-loaded.

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

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

Takes advantage of full schema coverage. Describes return value despite no output schema. All necessary context for usage is provided. Could mention error handling but not essential for selection.

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

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

Schema covers all 3 parameters (100% coverage). Description adds useful context: explains 'since' accepts ISO or relative formats, gives default recommendation '30d' or '1m', and clarifies 'value' accepts ticker or CIK.

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

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

Clearly states it returns recent changes for an entity since a given time, with specific behavior for type='company' (fans out to multiple sources). Distinguishes from siblings like entity_profile which likely provides a static 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 recommends use for 'brief me on what happened with X' or change-monitoring workflows. Does not provide explicit when-not-to-use or alternative tools, but the context is sufficient.

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 and adds valuable behavioral context beyond the basic storage action. It discloses persistence traits ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), which are critical for understanding data longevity and session management. However, it doesn't cover potential limitations like storage size or error conditions.

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

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

The description is appropriately sized and front-loaded, with two concise sentences that directly convey the tool's purpose and key behavioral details. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.

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

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

Given the tool's moderate complexity (storage with session-based persistence), no annotations, and no output schema, the description is mostly complete. It covers what the tool does, usage context, and persistence behavior, but lacks details on return values or error handling. For a tool with no structured output, more on expected responses would enhance 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 description coverage is 100%, so the schema already documents both parameters ('key' and 'value') with examples and types. The description does not add any additional meaning or semantics beyond what the schema provides, such as constraints on key formats or value encoding. The baseline score of 3 reflects adequate but no extra parameter insight.

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

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

The description clearly states the specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'recall' (retrieval) and 'forget' (deletion). It explicitly mentions what gets stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous and distinct.

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 ('to save intermediate findings, user preferences, or context across tool calls'), but it does not explicitly mention when not to use it or name alternatives. For example, it doesn't contrast with 'recall' for retrieval or specify if this is the only way to store data in this system.

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 carry the full behavioral burden. It details the inputs and outputs but does not disclose error handling, rate limits, or potential side effects. It implies a read-only operation but does not state this explicitly.

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 well-structured sentences. The first sentence states the overall purpose, the second gives specific format details, and the third highlights the benefit. No redundant information.

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

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

Given the tool's simplicity (one entity type, no output schema), the description covers the key aspects: input formats, output fields, and the efficiency advantage. It could be more complete by describing the output structure or potential errors, but it is adequate for its 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?

Schema description coverage is 100%, so the baseline is 3. The description adds value by providing concrete examples (e.g., 'AAPL', '0000320193', 'Apple') and clarifying that v1 only supports type 'company', which enhances understanding beyond the schema's enum.

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: resolving an entity to canonical IDs. It specifies the supported type (company), input formats (ticker, CIK, name with examples), and output fields. It also distinguishes itself from siblings by noting it replaces 2–3 lookup 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 indicates when to use this tool (when you need canonical entity IDs) and highlights its efficiency over multiple lookups. However, it does not explicitly mention when not to use it or compare with specific alternative tools among the 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the search functionality but fails to describe key behaviors: whether results are paginated, sorted, or limited; what happens if no matches are found; if there are rate limits; or what the return format looks like (e.g., list of joke objects). This is a significant gap for a search tool with zero annotation coverage.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without redundancy. It is appropriately sized for a simple search tool, front-loaded with the core functionality, and contains no wasted words or unnecessary elaboration.

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

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

Given the tool's moderate complexity (search with filtering), lack of annotations, and no output schema, the description is incomplete. It omits critical context: behavioral traits (e.g., result limits, error handling), output format, and usage distinctions from siblings. For a tool with three parameters and no structured output documentation, this leaves the agent under-informed.

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 fully documents all three parameters (query, amount, category) with their types, defaults, and constraints. The description adds no parameter-specific information beyond what the schema provides, such as search semantics (e.g., case-sensitivity) or category details. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the verb ('search') and resource ('jokes') with a specific scope ('containing a specific keyword or phrase'). It distinguishes from sibling tools like 'get_joke' (which likely fetches a single joke) and 'get_joke_categories' (which lists categories). However, it doesn't explicitly differentiate from 'get_joke_flags' (which might retrieve joke metadata), leaving slight ambiguity.

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

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

The description implies usage for keyword-based searches, but provides no explicit guidance on when to use this tool versus alternatives like 'get_joke' (e.g., for random jokes) or 'get_joke_categories' (e.g., for browsing categories). It lacks any 'when-not-to-use' statements or prerequisites, leaving the agent to infer context from tool names alone.

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 reveals the sources (SEC EDGAR + XBRL), version limitations (v1), and return structure. It does not indicate any destructive side effects, which is appropriate for a read-like operation. Slightly improved if it explicitly stated it is read-only.

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, with the first sentence immediately conveying purpose. The second sentence adds specificity, and the third explains efficiency benefits. Slightly verbose with the replacement note, but still 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?

Given the absence of an output schema, the description sufficiently explains what the tool returns (verdict, structured form, value, citation, delta). It explicitly states the domain and version limitations. However, it could mention error cases or supported claim formats for 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?

The input schema has 100% description coverage for the single parameter 'claim', with a clear example. The tool description adds context about the expected format and scope (e.g., company name and financial metric), which adds value 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 it fact-checks natural-language claims against authoritative sources, specifically company-financial claims using SEC EDGAR + XBRL. It lists the return types (verdict, value, citation, delta) and distinguishes itself from sibling tools like ask_pipeworx and 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 provides clear context about the tool's domain (company-financial claims for US public companies) and states it replaces sequential agent calls, implying efficiency. However, it does not explicitly mention when not to use the tool or suggest alternative tools for non-financial claims.

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