catfacts

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 key behavioral traits: Pipeworx 'picks the right tool, fills the arguments, and returns the result,' which explains the automation process. However, it lacks details on limitations (e.g., data source availability, accuracy, rate limits) or error handling, leaving gaps for a tool with no 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 well-structured and front-loaded with the core functionality. Every sentence earns its place: the first explains the purpose, the second details the automation, and the third provides concrete examples. It's concise with no wasted words, effectively communicating key 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 (natural language querying with automated tool selection) and no annotations or output schema, the description does a good job explaining the process and providing examples. However, it lacks details on output format, potential errors, or data source constraints, which would be helpful for completeness in this 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 baseline is 3. The description adds value by emphasizing 'plain English' and 'natural language' for the 'question' parameter, and provides examples that illustrate expected input formats (e.g., queries like 'Look up adverse events for ozempic'). This enhances understanding beyond the schema's basic 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's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and distinguishes it from sibling tools by emphasizing natural language input without needing to browse tools or learn schemas. The examples further clarify the scope.

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

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

The description explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It implies alternatives (other tools that require schema knowledge) and provides clear context with examples like 'What is the US trade deficit with China?' to guide appropriate usage.

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

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

With no annotations, the description must carry the behavioral burden. It discloses return data (paired data, resource URIs) for each type, but does not state whether the tool is read-only, required permissions, rate limits, or error behavior. The safety profile is implied as non-destructive but not explicit.

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

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

Two sentences, front-loaded with the main purpose, then details on each type, and a final benefit statement. No filler, 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?

No output schema, so the description should compensate. It mentions 'paired data + pipeworx:// resource URIs' but does not detail the structure of the paired data, pagination, or error cases. For a tool combining data from two sources, more output expectations would help.

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

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

Schema coverage is 100% (both parameters described). The description adds significant meaning by explaining the data returned for each 'type' value and giving concrete examples for 'values' (tickers/CIKs for company, drug names). This goes beyond the schema's enum and array 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 compares 2–5 entities side by side, specifies two entity types (company and drug) with distinct data fields, and notes it replaces 8–15 sequential calls. This strongly distinguishes it from sibling tools like get_fact or list_breeds.

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 tells when to use (comparing 2–5 entities for financial or drug data) and implies when not to (use other tools for single entities or other types). It does not explicitly name alternatives or state exclusions, but the context is clear enough.

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

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

No annotations are provided, so the description carries the full burden. It discloses that this is a search operation that returns relevant tools, but doesn't mention behavioral aspects like rate limits, authentication requirements, error conditions, or how relevance is determined. The description adds some context about the catalog size but lacks operational 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 a distinct purpose: the first explains what the tool does, the second provides usage guidance. Every word earns its place with zero redundancy or wasted verbiage.

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 operation with 2 parameters) and 100% schema coverage but no output schema or annotations, the description does well by explaining the core purpose and strategic usage. However, it doesn't describe what the output looks like (format, structure) or potential limitations, leaving some gaps for an agent to understand the full 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 both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions 'describing what you need' which aligns with the query parameter but provides no additional semantic 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's purpose with specific verbs and resources: 'Search the Pipeworx tool catalog by describing what you need' and 'Returns the most relevant tools with names and descriptions.' It explicitly distinguishes this from sibling tools (get_fact, get_facts, list_breeds) by focusing on catalog search rather than data 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 provides explicit guidance on when to use this tool: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' It gives clear context (large catalog scenario) and a specific alternative approach (using this as the initial step).

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 the burden. It explains data sources and outputs (citation URIs) but does not explicitly state it's read-only or note any side effects. Adequate for a lookup tool.

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

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

Description is informative with a clear front-loaded statement and bullet-like details. Slightly lengthy but each sentence adds value. Good structure.

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 or annotations, the description covers inputs, data sources, output format (citation URIs), limitations (no names), and alternatives. Complete for the tool's purpose.

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

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

Schema coverage is 100% with parameter descriptions. The description adds value by explaining that type only supports 'company' for now, and value accepts ticker or CIK, not names, and directs to resolve_entity for names.

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 provides a full profile of an entity across multiple packs, listing specific data sources (SEC, XBRL, patents, news, LEI) and mentions replacing 10-15 sequential calls. It distinguishes itself from siblings like compare_entities and resolve_entity by focusing on comprehensive single-entity profiling.

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

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

Explicit guidance: use for comprehensive entity profiles; avoid for federal contracts (direct to usa_recipient_profile); if only a name, use resolve_entity first. Provides clear when-to-use and when-not-to-use context.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Delete' implies a destructive mutation, the description doesn't specify whether deletion is permanent, reversible, requires specific permissions, or what happens on success/failure. It provides minimal behavioral context beyond the basic 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 perfectly concise at 6 words, front-loading the essential information ('Delete a stored memory') with no wasted words. Every element earns its place, making it immediately understandable without 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 mutation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't address critical questions like: what happens after deletion? Is there confirmation? What errors might occur? What permissions are needed? The minimal description leaves too many behavioral aspects unspecified.

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 about the key format, valid values, or deletion behavior beyond what's in the schema. Baseline 3 is appropriate when 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. It doesn't differentiate from sibling tools like 'recall' or 'remember', but it's specific enough to know this is a deletion operation rather than retrieval or storage.

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 is provided about when to use this tool versus alternatives like 'recall' (which likely retrieves memories) or 'remember' (which likely stores them). The description only states what the tool does, not when it's appropriate to use it or what prerequisites might exist.

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 the tool returns a random cat fact, which implies a read-only operation, but doesn't clarify aspects like whether it's idempotent, if there are rate limits, or what happens on errors. The description is minimal and lacks behavioral 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 directly states the tool's purpose without any fluff. It's front-loaded and wastes no words, making it highly concise and well-structured for quick understanding.

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 (0 parameters, no annotations, no output schema), the description is adequate as a minimum viable explanation. It covers the basic action but lacks details on behavioral traits, usage context, or output format, which could be helpful for an agent despite the low complexity. It meets the baseline for such a simple tool but doesn't excel 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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, as there are none, which aligns with the schema's completeness. A baseline of 4 is applied since no parameters exist, and the description doesn't add unnecessary details.

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 ('Get') and resource ('a single random cat fact'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_facts' (which might return multiple facts) or 'list_breeds' (which deals with breeds rather than facts).

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 sibling tools like 'get_facts' or 'list_breeds', nor does it specify scenarios where fetching a random cat fact is appropriate versus other operations.

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 the tool retrieves 'random' facts, which is useful context, but doesn't describe the return format (e.g., structure of facts), potential rate limits, error conditions, or whether the randomness is seeded. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 is front-loaded with the core purpose ('Get multiple random cat facts'), making it easy to parse. Every part of the sentence contributes essential information, earning its place.

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

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

Given the tool's low complexity (one optional parameter, no annotations, no output schema), the description is minimally adequate. It covers the basic purpose and randomness aspect, but lacks details on output structure, error handling, or sibling differentiation. For a simple retrieval tool, it meets the minimum viable threshold but could be more 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 'limit' parameter fully documented in the schema (type, description, default). The description adds no additional parameter semantics beyond what the schema provides, such as range constraints or effects on randomness. With high schema coverage, the baseline score of 3 is appropriate as the schema handles 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 ('Get') and resource ('multiple random cat facts'), making the tool's purpose immediately understandable. It distinguishes itself from 'get_fact' (singular) by specifying 'multiple' facts, though it doesn't explicitly differentiate from 'list_breeds' which deals with a different resource type. The purpose is specific but could be more distinct from all 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 provides no guidance on when to use this tool versus alternatives like 'get_fact' or 'list_breeds'. It doesn't mention any prerequisites, constraints, or scenarios where this tool is preferred. Usage is implied by the name and description alone, with no explicit context for selection 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It states what data is returned but doesn't mention whether this is a read-only operation, if there are rate limits, authentication requirements, pagination behavior, or what happens when the limit parameter is used. For a list operation with zero annotation coverage, this leaves significant gaps.

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

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

The description is a single, efficient sentence that states the purpose and specifies the returned details. There's no wasted language or unnecessary elaboration. It's appropriately sized for a simple list operation.

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 list tool with one optional parameter and no output schema, the description is minimally adequate. It explains what data is returned but doesn't cover behavioral aspects like pagination, ordering, or error conditions. With no annotations and no output schema, more context about the return format would be 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?

The input schema has 100% description coverage, with the single parameter 'limit' well-documented in the schema itself. The description doesn't add any parameter-specific information beyond what the schema provides, which is acceptable given the high schema coverage. The baseline of 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 ('List') and resource ('cat breeds'), and specifies what details are included ('country, origin, coat, and pattern'). However, it doesn't explicitly differentiate this tool from its sibling tools 'get_fact' and 'get_facts', which appear to be different operations rather than alternative breed-listing methods.

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 its siblings 'get_fact' and 'get_facts'. It doesn't mention any prerequisites, alternatives, or exclusions. The agent must infer usage 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?

No annotations are provided, so the description carries the full burden. It discloses the rate limit (5 messages per identifier per day) and that it is free. It also instructs the agent to describe what was tried in terms of Pipeworx tools/data. This is sufficient for a feedback tool, though it does not describe the response format.

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, each serving a distinct purpose: stating the tool's function, giving usage guidelines, and noting constraints. It is front-loaded with the core purpose and is highly 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 tool's simplicity and that no annotations or output schema are provided, the description covers the essential aspects: purpose, usage, limitations. It could mention that feedback is a one-way submission, but overall it is complete enough for an agent to use correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the meaning of each type and the context fields, but most of that information is already in the schema's descriptions. It does not add significant new semantics 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: to send feedback to the Pipeworx team. It lists specific use cases (bug reports, feature requests, missing data, praise), and it distinguishes from all sibling tools, none of which are for sending feedback.

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 (for feedback) and includes important constraints (do not include user's prompt verbatim, rate limit). However, it does not explicitly state when not to use it or suggest alternatives, but the context makes it 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It explains the dual functionality (retrieve by key vs list all) and persistence across sessions, which is valuable context. However, it doesn't mention error handling (e.g., what happens if key doesn't exist), performance characteristics, or authentication requirements. For a tool with no annotations, this is adequate but leaves gaps.

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

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

The description is perfectly concise with two sentences that each earn their place. The first sentence explains the core functionality, and the second provides usage guidance. 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?

Given the tool's moderate complexity (dual functionality, session persistence), no annotations, and no output schema, the description does well. It covers purpose, usage, and parameter semantics adequately. However, without an output schema, it doesn't describe return values (e.g., format of retrieved memories or list structure), which is a minor gap for a retrieval tool.

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

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

The schema description coverage is 100%, so the schema already documents the optional 'key' parameter. The description adds meaningful context by explaining the semantic effect of omitting the key ('list all keys') and connecting it to the tool's dual functionality. This goes beyond the schema's technical specification to explain behavioral consequences.

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 siblings like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations. The description goes beyond the name 'recall' by explaining both retrieval modes.

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 vs alternatives: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' It also specifies when to omit the key parameter to list all memories, giving clear operational context. This directly addresses the agent's decision-making needs.

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

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

Despite no annotations, the description discloses key behaviors: parallel fan-out to multiple sources, return types (structured changes, total_changes count, pipeworx:// URIs), and the format of the 'since' parameter. It does not mention rate limits or authentication, but overall provides useful transparency.

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

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

The description is concise and front-loaded with purpose. Every sentence adds necessary detail about functionality, parameters, and return values. Could be slightly tighter, but it's well-organized 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?

The description covers the tool's behavior, inputs, and outputs adequately. No output schema is provided, but the description explains the return format. Siblings exist but comparison is implicit. For a tool with 3 parameters and moderate complexity, the description is sufficiently complete.

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

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

Input schema coverage is 100%. The description adds value beyond the schema by explaining acceptable formats for 'since' (ISO or relative), giving default suggestions ('30d' or '1m'), and providing examples for 'value' (ticker or CIK). It also clarifies that 'type' currently only supports 'company'.

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: showing what's new about an entity since a given time, with specific sources (SEC EDGAR, GDELT, USPTO). It also distinguishes itself from siblings by mentioning the fan-out behavior and specific use cases like 'brief me on what happened with X'.

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 suggests when to use the tool: for 'brief me on what happened with X' or change-monitoring workflows. It does not explicitly mention alternatives or when not to use, but the purpose is clear enough to guide the agent.

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 effectively describes key behavioral traits: the persistence differences between authenticated users ('persistent memory') and anonymous sessions ('last 24 hours'), and the scope ('session memory'). It does not cover aspects like rate limits, error conditions, or memory size limits, but provides sufficient context for basic use.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by usage guidance and behavioral details. Every sentence adds value—none are redundant or unnecessary—making it efficient and well-structured for quick comprehension.

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 persistence rules), no annotations, and no output schema, the description is largely complete. It covers purpose, usage, and key behavioral traits. However, it lacks details on return values (e.g., confirmation message or error handling), which would be needed for a score of 5, especially without an output schema.

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 fully documents both parameters ('key' and 'value'). The description does not add any parameter-specific details beyond what the schema provides (e.g., it doesn't explain key constraints or value formatting), meeting the baseline of 3 for high schema 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 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 provides concrete examples of what can be stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous.

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 this tool ('to save intermediate findings, user preferences, or context across tool calls'), providing clear context. However, it does not mention when not to use it or name alternatives (e.g., 'recall' for retrieval or 'forget' for deletion), which would be needed for a score of 5.

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 return values (ticker, CIK, company name, resource URIs) and that it's a single call. It also notes the current limitation (v1 only supports company). This is good transparency, though could mention idempotency or error behavior.

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 consists of three dense sentences. Each sentence adds essential information: purpose, input specifics with examples, and benefit. No unnecessary words or 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 simple tool with two parameters and no output schema, the description covers the main aspects: purpose, input format, and return fields. It could be improved by mentioning what happens if no match is found, but overall it is quite 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%, and the description adds significant value by providing concrete examples (e.g., 'AAPL', '0000320193', 'Apple') and clarifying that 'value' can be a ticker, CIK, or name, which is not fully captured in the schema description (only says 'Ticker, CIK, or company name').

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 resolves an entity to canonical IDs across Pipeworx data sources in a single call, specifying the action and resource. It distinguishes from siblings by focusing on entity resolution and giving concrete examples (ticker, CIK, name) that are not covered by other 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 mentions it replaces 2-3 lookup calls, implying when to use it for efficiency. However, it does not explicitly exclude scenarios where it should not be used or compare with sibling tools like get_fact.

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. It discloses the return format (verdict, extracted structured form, actual value with citation, percent delta) and the data sources (SEC EDGAR + XBRL). It does not mention any destructive actions or permissions, but the read-only nature is implied. More details on external API calls or rate limits would be beneficial.

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 (three sentences) with no redundant words. It front-loads the purpose, then provides domain scope, output details, and value proposition, all efficiently.

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 tool with a single parameter and no output schema, the description adequately covers purpose, usage parameters, return values, and data sources. It provides enough context for an agent to use the tool correctly without additional 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?

The input schema covers one parameter ('claim') with a description and example. Since schema description coverage is 100%, the baseline is 3. The description adds domain context (company-financial claims) and an example, but does not significantly extend beyond the schema's own 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's purpose as fact-checking natural-language claims against authoritative sources specifically for company-financial data of US public companies. It specifies the verb 'validate' and resource 'authoritative sources', and distinguishes itself from siblings by mentioning it replaces multiple sequential agent calls (e.g., NL parsing, entity resolution, data lookup).

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 supports company-financial claims' and that it 'replaces 4–6 sequential agent calls', providing clear context for when to use this tool versus combining other tools. However, it could be improved by stating when not to use it, such as for non-financial or non-US company claims.

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