agify

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 effectively describes the tool's core behavior: it interprets natural language questions, selects appropriate data sources, executes queries, and returns results. However, it doesn't mention limitations like response time, accuracy guarantees, or data source constraints.

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

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

The description is efficiently structured with a clear purpose statement followed by behavioral explanation and concrete examples. Every sentence adds value: the first explains the tool's function, the second describes its automation benefits, and the third provides illustrative use cases without redundancy.

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

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

For a single-parameter tool with no annotations and no output schema, the description provides good contextual coverage of purpose, usage, and behavior. However, it lacks information about output format, error conditions, or limitations that would be helpful given the absence of structured output documentation.

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 the single 'question' parameter adequately. The description adds minimal additional semantic context beyond what's in the schema ('Your question or request in natural language'), providing only the baseline value expected 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 ('best available data source'), distinguishing it from siblings by emphasizing natural language processing rather than specific data operations. It explicitly contrasts with sibling tools by stating 'No need to browse tools or learn schemas'.

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 ('just describe what you need in plain English') versus alternatives (implicitly suggesting not to use sibling tools like discover_tools when you don't want to 'browse tools or learn schemas'). The examples further clarify appropriate use cases.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the search functionality and return format ('most relevant tools with names and descriptions'), but doesn't mention potential limitations like rate limits, authentication requirements, or error conditions. It adequately covers the basic operation but lacks deeper behavioral context.

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

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

The description is perfectly concise with two sentences that each serve a clear purpose: the first explains what the tool does, and the second provides crucial usage guidance. There's zero wasted language, and the most important information (when to use it) is appropriately front-loaded.

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

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

For a search tool with no annotations and no output schema, the description provides good context about when to use it and what it returns. However, it doesn't describe the format of returned results beyond 'names and descriptions' or mention any limitations. Given the 100% schema coverage and clear purpose, it's mostly complete but could benefit from more detail about output structure.

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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions the tool's purpose but doesn't elaborate on parameter usage or constraints beyond the schema's existing documentation.

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 ('returns the most relevant tools with names and descriptions'). It distinguishes this tool from its siblings (predict_age, predict_age_country) by focusing on tool discovery rather than prediction tasks.

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: '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 versus alternatives, including the specific condition (500+ tools) and priority (first).

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. While 'Delete' implies a destructive operation, it doesn't specify whether deletion is permanent, reversible, requires specific permissions, or what happens on success/failure. The description lacks crucial behavioral context for a destructive operation.

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

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

The description is extremely concise at just 5 words, front-loading the essential action ('Delete') and resource. Every word earns its place with zero redundancy 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?

For a destructive operation with no annotations and no output schema, the description is insufficient. It doesn't explain what constitutes a 'stored memory', what happens after deletion, error scenarios, or return values. Given the tool's destructive nature and lack of structured metadata, more context is needed.

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 the single 'key' parameter adequately. The description adds no additional semantic context beyond what's in the schema - it simply repeats that deletion is 'by key' without explaining key format, constraints, or examples.

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

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

The description clearly states the action ('Delete') and resource ('a stored memory by key'), making the purpose immediately understandable. It doesn't differentiate from sibling tools like 'recall' or 'remember', but it's specific enough to understand what the tool does.

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 'recall' (which likely retrieves memories) or 'remember' (which likely stores memories). There's no mention of prerequisites, error conditions, or appropriate contexts for deletion.

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 the prediction behavior and data source, but doesn't mention accuracy limitations, rate limits, or what happens with uncommon names. It adds some 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 a single, efficient sentence with zero waste. It's appropriately sized for a simple tool and front-loads the core purpose immediately.

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 prediction tool with one parameter and no output schema, the description is adequate but has gaps. It doesn't explain the return format (e.g., age value, confidence score) or handle edge cases. With no annotations, it could benefit from more behavioral context.

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 the single parameter. The description adds marginal value by reinforcing that it's a 'first name' for age prediction, but doesn't provide additional syntax or format details beyond what the schema provides.

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

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

The description clearly states the specific action ('predict the most likely age'), resource ('a person based on their first name'), and data source ('global data from agify.io'). It distinguishes from the sibling tool predict_age_country by specifying 'global data' without country filtering.

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 when to use this tool (for global age prediction based on first name) and when not to use it (when country-specific prediction is needed, as suggested by the sibling tool name predict_age_country). However, it doesn't explicitly name the alternative or state exclusions.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the prediction action and calibration, but lacks details on accuracy, limitations, data sources, or response format. For a prediction tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

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 function without unnecessary words. It's front-loaded with the core purpose and includes the key constraint, making it easy to parse and understand 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 tool's moderate complexity (prediction with calibration), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and calibration aspect, but lacks details on behavioral traits, output format, or sibling differentiation, leaving room for improvement 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?

Schema description coverage is 100%, so the schema already documents both parameters ('name' and 'country_code') with clear descriptions. The description adds minimal value beyond the schema by implying country calibration, but doesn't provide additional syntax or format 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 tool's purpose: predicting age based on first name with country calibration. It specifies the verb ('predict'), resource ('age'), and key constraint ('calibrated to a specific country'). However, it doesn't explicitly differentiate from the sibling tool 'predict_age', which likely lacks country calibration, so it misses full sibling distinction.

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 context by mentioning country calibration, suggesting this tool should be used when geographic localization is needed. However, it doesn't explicitly state when to use this tool versus the sibling 'predict_age' or provide any exclusions or alternatives, leaving the guidance incomplete.

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 retrieves or lists memories, including persistence across sessions ('previous sessions'), which is useful behavioral context. However, it doesn't mention potential errors (e.g., if a key doesn't exist), performance aspects, or data format of retrieved memories, leaving gaps in behavioral understanding.

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

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

The description is concise and well-structured in two sentences. The first sentence clearly states the tool's functionality and parameter usage, while the second provides usage context. There is no wasted language, and key information is front-loaded, making it efficient for an agent 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 (retrieve/list operations), lack of annotations, and no output schema, the description is adequate but incomplete. It covers the basic purpose and usage but omits details like error handling, memory format, or session persistence mechanics. For a tool with no structured behavioral hints, more context would be beneficial to ensure reliable agent operation.

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, with the parameter 'key' documented as 'Memory key to retrieve (omit to list all keys).' The description adds minimal value beyond this, only reinforcing the same conditional behavior. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't 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 tool's purpose: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' It specifies the verb ('retrieve'/'list') and resource ('memory'), making the functionality explicit. However, it doesn't distinguish from sibling tools like 'remember' (which presumably stores memories) or 'forget' (which likely removes them), so it doesn't fully differentiate from alternatives.

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 on when to use the tool: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' It also explains the conditional usage based on the key parameter ('omit key' to list all). However, it doesn't explicitly state when not to use it or name alternatives (e.g., 'remember' for storing), so it lacks full exclusion guidance.

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

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the persistence model (authenticated users get persistent memory, anonymous sessions last 24 hours) and the cross-tool context capability. It doesn't mention rate limits, error conditions, or memory size limits, but covers the essential operational behavior well.

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 earn their place. The first sentence states the core function, the second adds crucial behavioral context about persistence. No wasted words, and the most important information (what the tool does) 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?

For a simple 2-parameter tool with no annotations and no output schema, the description provides good contextual completeness. It explains what the tool does, when to use it, and key behavioral aspects (persistence model). It doesn't describe return values or error cases, but for this complexity level, it's nearly complete. A perfect score would require mentioning what happens on 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?

With 100% schema description coverage, the input schema already fully documents both parameters (key and value). The description doesn't add any meaningful parameter semantics beyond what's in the schema descriptions, which provide examples for 'key' and clarify 'value' accepts 'any text'. The baseline of 3 is appropriate when the schema does all the parameter documentation work.

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 ('store a key-value pair') and resource ('in your session memory'). It distinguishes from siblings by specifying its unique function of storing data, unlike 'recall' (retrieving), 'forget' (deleting), or prediction tools. The description goes beyond the name 'remember' to explain what kind of data is stored.

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 on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), giving practical examples. However, it doesn't explicitly state when NOT to use it or mention alternatives like using 'recall' for retrieval or 'forget' for deletion, which would be needed for a perfect score.

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