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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 describes the tool's behavior well ('Pipeworx picks the right tool, fills the arguments, and returns the result'), but doesn't mention limitations like rate limits, authentication needs, or what happens with ambiguous questions. The behavioral description is good but incomplete.

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, key behavioral explanation, and specific examples. Every sentence adds value, and it's appropriately sized for a single-parameter tool with no annotations.

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 context about how the tool works and what to expect. It could be more complete by mentioning potential limitations or the format of returned answers, but it's sufficient for basic understanding.

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. The description adds value by specifying the parameter should be 'in plain English' and providing concrete examples that illustrate the expected format and scope of questions.

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'). It distinguishes itself from siblings by emphasizing natural language processing and automated tool selection, unlike the more specific sibling tools like get_passage or get_verse.

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') and provides clear examples of appropriate questions. It implies when not to use it (when you need specific tool control or schema-based queries).

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral details: it resolves the market, classifies the bet, fans out to relevant packs, and returns an evidence packet with comparison. No contradictions; additional context on the fan-out logic is valuable.

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

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

The description is four sentences long, front-loaded with the core action. Each sentence adds value—no fluff or redundancies. It could be slightly trimmed but remains efficient and clear.

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, varying packs, no output schema), the description adequately covers the return format (evidence packet + market-vs-model comparison) and the fan-out logic. Missing details like size limits or pagination are not critical for this use case.

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 both parameters documented. The description adds meaning by clarifying the 'market' param accepts three input types (slug, URL, question) and explaining the 'depth' enum: quick = 2-3 sources, thorough = full fan-out. This goes 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 it researches Polymarket bets by pulling Pipeworx data, specifying input types (slug, URL, question) and output (evidence packet + market-vs-model comparison). It uniquely distinguishes this tool from siblings like ask_pipeworx or compare_entities, which serve different purposes.

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 use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It does not explicitly state when not to use or name alternatives, but the context is clear enough for an AI agent to choose appropriately.

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

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

No annotations provided, so description carries full burden. It discloses return type (paired data + URIs) and efficiency benefit, but does not mention safety aspects or auth requirements, though likely 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?

Three dense sentences with no fluff. Key information is front-loaded: purpose, entity-specific details, and efficiency claim. Every sentence earns its place.

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

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

No output schema, but description mentions return nature. It covers parameters well but lacks details on error handling or what 'paired data' precisely means. Adequate for a comparison tool with well-defined inputs.

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

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

Schema coverage is 100%, baseline 3. Description adds value by explaining how type governs the returned fields and how values differ per entity type, going beyond the 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?

Description clearly states the tool compares 2–5 entities side by side with specific fields for company and drug types. It distinguishes itself from siblings by noting it replaces 8–15 sequential calls, making its unique value obvious.

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 the tool (comparing entities) and implies efficiency over sequential calls, but does not provide explicit exclusions or alternatives, which is a minor gap.

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 behaviors: it's a search operation that returns relevant tools, suggests calling it first in large tool catalogs, and implies it's non-destructive (search/return). However, it doesn't mention potential limitations like rate limits, authentication needs, 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 perfectly concise and front-loaded: two sentences that each earn their place. The first sentence explains what the tool does, and the second provides crucial usage guidance. No wasted words or 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 moderate complexity (search operation with 2 parameters) and no annotations or output schema, the description provides good context about purpose and usage. It could be more complete by mentioning what the return format looks like (though no output schema exists) or any behavioral constraints, but it adequately covers the essential aspects for a search tool.

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

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

Schema description coverage is 100%, so the schema already documents both parameters (query and limit) thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter semantics.

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', 'returns') and resource ('Pipeworx tool catalog'), distinguishing it from sibling tools (get_passage, get_verse, random_verse) which appear to be unrelated to tool discovery. It explicitly mentions searching by describing needs and returning relevant tools with names and descriptions.

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.' This clearly indicates the primary use case and context, with no misleading or contradictory information about alternatives.

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

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

No annotations provided; description carries full burden. Discloses that the tool returns pipeworx:// citation URIs and mentions speed limitation ('too slow to bundle') for federal contracts. Does not contradict any annotations.

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

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

Single paragraph with efficient, information-dense sentences. No wasted words, though could be slightly more structured (e.g., bullet points) 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?

For a complex aggregation tool, the description covers what data is included, return format (pipeworx:// URIs), limitations (only company, no names), and alternative for federal contracts. No output schema, but return type is described.

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

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

Schema covers 100% of parameters with descriptions. Description adds value by explaining valid value formats (ticker or CIK) and that names are not supported, plus noting type is currently limited to '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 aggregates a full profile of an entity across multiple Pipeworx packs, listing specific data sources (SEC, XBRL, patents, news, LEI). It distinguishes from a related tool (usa_recipient_profile) for federal contracts.

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

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

Explicitly states when to use (need comprehensive profile) and when not (for federal contracts, use usa_recipient_profile). Also implies prerequisite: use resolve_entity if only a name is available.

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 'Delete' which implies a destructive mutation, but doesn't address permanence (e.g., irreversible deletion), permissions required, error conditions (e.g., what happens if the key doesn't exist), or side effects. This is inadequate for a mutation 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 with zero waste. It's front-loaded with the core action and resource, making it immediately scannable and appropriately sized for a simple tool.

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

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

For a destructive mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what constitutes a 'stored memory', how deletion affects the system, what the response looks like (e.g., success confirmation or error), or error handling. Given the complexity and lack of structured coverage, 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%, with the single parameter 'key' documented as 'Memory key to delete'. The description adds no additional meaning beyond this, simply restating 'by key'. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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

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

The description clearly states the action ('Delete') and resource ('a stored memory by key'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'recall' (which likely retrieves memories) or 'remember' (which likely stores them), missing explicit 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?

No guidance is provided about when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing an existing memory key), exclusions, or relationships with sibling tools like 'recall' or 'remember', leaving the agent to infer usage context.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does (fetch with translation) but lacks details on permissions, rate limits, error handling, or response format. For a read operation with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond basic functionality.

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

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

The description is a single, efficient sentence that front-loads the core purpose and follows with necessary translation details. Every element earns its place with no wasted words, 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?

Given the tool's moderate complexity (2 required parameters, no output schema, no annotations), the description covers the basic functionality and translation options adequately. However, it lacks information about return values, error cases, or behavioral constraints that would be needed for complete understanding, especially without annotations or 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?

Schema description coverage is 100%, so the schema fully documents both parameters. The description adds value by listing specific translation codes beyond the schema's examples, but doesn't provide additional semantic context like format constraints or usage examples beyond what's already in the schema 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 action ('Fetch') and resource ('Bible passage'), specifying it includes a translation parameter. It distinguishes from 'get_verse' and 'random_verse' by focusing on passages rather than single verses or random selection, though it doesn't explicitly contrast with 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 implies usage when a specific passage with translation is needed, but provides no explicit guidance on when to use this tool versus 'get_verse' or 'random_verse'. It lists supported translations, which offers some context, but lacks clear when/when-not instructions or alternative recommendations.

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 translation used (World English Bible) and that spaces are encoded automatically, which are useful behavioral traits. However, it doesn't mention error handling, rate limits, or authentication needs, 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 front-loaded with the core purpose, followed by key details (translation, encoding behavior). Both sentences earn their place by adding essential information without redundancy, making it appropriately sized and efficient.

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

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

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is mostly complete. It covers purpose, usage context, and key behavioral details. However, without annotations or output schema, it could benefit from mentioning response format or error cases for full 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 the single parameter 'reference' with examples. The description adds marginal value by reinforcing the parameter's purpose and providing additional examples, but doesn't explain semantics 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 ('Fetch'), resource ('Bible verse or verse range'), and distinguishes it from siblings by specifying it's for a 'specific' reference rather than random or passage-based retrieval. It provides concrete examples of valid references.

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 by specifying it fetches 'a specific Bible verse or verse range by reference,' which differentiates it from 'random_verse' (non-specific) and 'get_passage' (likely broader context). However, it doesn't explicitly state when NOT to use it or name 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?

Discloses rate limit (5 messages/identifier/day) and 'Free', but lacks details on how feedback is processed, stored, or if a response is expected. No annotations exist to offset this.

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

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

Three concise sentences with no fluff. Front-loaded with purpose, then usage tips, then behavioral note. Every sentence adds value.

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

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

For a simple feedback tool with no output schema, the description covers purpose, content guidelines, and constraints. Missing only a note on response/confirmation behavior.

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 detailed descriptions. The description summarizes the type parameter's options but does not add new semantic meaning beyond the schema.

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

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

The description explicitly states 'Send feedback to the Pipeworx team' and enumerates specific use cases: bug reports, feature requests, missing data, or praise. It clearly distinguishes from sibling tools like discover_tools or ask_pipeworx.

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 clear guidance on when to use (e.g., bug reports, feature requests) and what to include (describe what you tried, not the end-user's prompt). Mentions rate limit, but does not explicitly warn against misuse or suggest alternatives.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds rich behavioral context: walking child markets, extracting dates/thresholds, and reporting arbitrage pairs, all consistent with read-only 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?

Concise but informative; front-loaded with core concept, then details. Every sentence adds value, though slightly lengthy; still highly readable.

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 analytical complexity, the description covers input, logic, output format (list of entries with fields), and rationale. No output schema, but description sufficiently details the return 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?

Input schema has 100% coverage for the single parameter 'event'. The description enhances meaning by explaining how the parameter is used (e.g., accepting slug or URL) and giving an example, adding value beyond the schema.

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

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

Clearly states the tool finds arbitrage opportunities via monotonicity violations in Polymarket events, with an illustrative example that differentiates it 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?

Describes exactly when to use (same event with multiple related markets) and the underlying logical rule. Could explicitly mention alternatives, but the specialized function is clear.

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

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

The description provides detailed behavioral traits beyond annotations: it scans top markets, groups by asset, fetches price history once, computes model probability using a lognormal model from FRED and coinpaprika, ranks by edge, and returns top N with trade direction. Annotations confirm read-only and non-destructive nature.

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

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

The description is a single paragraph of about five sentences, front-loading the core purpose in the first sentence. Every sentence adds value, explaining methodology and use case without unnecessary verbosity.

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

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

Given no output schema, the description adequately explains return values (top N with edge magnitude and trade direction) and sets expectations (V1, crypto-price bets). It covers sources, model, and intended use, making it complete for agent understanding.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description does not add significant extra meaning beyond the schema, merely contextualizing the parameters (e.g., min_edge_pp implies filtering). Baseline of 3 is appropriate.

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

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

The description clearly states the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price, with a specific verb-resource combination. It distinguishes itself from sibling tools like polymarket_arbitrage by focusing on edge discovery for betting opportunities.

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 targets the 'what should I bet on today' question, indicating when to use it. It implies use for discovering opportunities without manual browsing, but lacks explicit comparisons to alternatives like polymarket_arbitrage.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return format ('reference, text, and translation'), which adds value beyond the input schema. However, it doesn't cover aspects like rate limits, error handling, or whether the randomness is seeded, leaving gaps in 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 a single, efficient sentence that front-loads the core action and output without any wasted words. Every part of the sentence contributes essential information, making it highly concise and well-structured.

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

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

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate as a minimum viable explanation. It covers the basic purpose and return format, but lacks details on usage guidelines and full behavioral traits, which could be important for an AI agent in a context with sibling tools.

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 with 100% schema description coverage, so the schema fully documents the input requirements. The description doesn't need to add parameter details, and it appropriately focuses on the tool's purpose and output, earning a baseline score above 3 for clarity in a parameter-less 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 action ('Fetch') and resource ('a random Bible verse'), specifying what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_passage' or 'get_verse' in terms of scope or selection method, which prevents 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 like 'get_passage' or 'get_verse'. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name 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 provided, the description carries the full burden. It discloses the tool's dual behavior (retrieve by key or list all) and persistence across sessions. However, it doesn't mention error handling (e.g., what happens if key doesn't exist), performance characteristics, or format of returned memories. The behavioral disclosure is adequate but lacks depth.

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 functionality, and the second provides usage context. There's zero wasted language, and the information is front-loaded with the primary purpose.

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

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

Given the tool's moderate complexity (dual retrieval modes, session persistence), no annotations, and no output schema, the description does well but has gaps. It covers purpose, usage, and parameter semantics adequately. However, it doesn't describe the return format (e.g., structure of memories) or error cases, which would be helpful for an agent.

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.' This clarifies the optional parameter's behavior beyond what the schema states ('Memory key to retrieve'), elevating the score.

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 this from sibling tools like 'remember' (which stores) and 'forget' (which removes). The phrase 'by key' adds specificity about the retrieval mechanism.

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 gives clear conditional usage: 'omit key' to list all memories, use with key to retrieve specific ones. This distinguishes it from sibling tools like 'get_passage' or 'get_verse' which likely retrieve different types of content.

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

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

No annotations provided, so description carries full burden. Describes parallel fan-out to three sources, and explains return format (structured changes, total_changes count, pipeworx:// URIs). Missing whether it is read-only, but that is implied by its query nature.

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?

Four sentences, dense with information, front-loaded with purpose. Every sentence adds value—use cases, input formats, output structure. Excellent conciseness.

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

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

Given no output schema and no annotations, the description fully explains inputs, behavior (parallel queries), output, and use cases. Enables correct tool selection and invocation without ambiguity.

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%, but description adds significant value: explains 'since' formats (ISO date, relative like '30d'), gives example ticker or CIK for 'value', and confirms 'type' only supports 'company'. Baseline 3, extra details justify 4.

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

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

Clearly states 'What's new about an entity since a given point in time', specifies entity type 'company', and lists data sources (SEC EDGAR, GDELT, USPTO). Distinguishes from siblings like entity_profile by focusing on temporal changes.

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. Provides examples of valid 'since' values and notes supported type. Could be improved by explicitly contrasting with siblings like entity_profile for static data.

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 successfully describes key behavioral traits: the tool performs a write operation (store), has different persistence characteristics based on authentication state, and operates within a session memory system. It doesn't mention error conditions, rate limits, or specific constraints on key/value formats, but covers the essential operational 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 perfectly concise with two sentences that each earn their place. The first sentence states the core functionality, the second provides important usage context and behavioral details. There's zero wasted language, and the most critical information (what the tool does) appears first.

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 2-parameter write tool with no annotations and no output schema, the description provides good coverage of the tool's purpose, usage context, and key behavioral characteristics. It doesn't describe what happens on success/failure or return values, but given the tool's relative simplicity and the clear parameter documentation in the schema, the description is reasonably complete. The authentication-based persistence detail is particularly valuable 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?

The schema description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. It mentions the types of data that can be stored ('findings, addresses, preferences, notes') which loosely relates to the 'value' parameter but doesn't provide additional semantic context beyond the schema's 'any text' 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 with specific verbs ('store a key-value pair') and resource ('in your session memory'). It distinguishes from sibling tools like 'forget' (which likely removes) and 'recall' (which likely retrieves) by focusing on storage. The description goes beyond the name 'remember' by specifying what type of memory system is involved.

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 save intermediate findings, user preferences, or context across tool calls.' It also distinguishes usage contexts between authenticated users (persistent memory) and anonymous sessions (24-hour memory), giving clear operational boundaries. No explicit alternatives are named, but the context makes its role 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, the description carries the full burden. It discloses the v1 limitation to company type, lists input and output fields, and implies it is a non-destructive read operation. It does not cover edge cases like ambiguous names or rate limits, but the core behavior is clear.

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

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

The description is three sentences long with no extraneous information. It front-loads the core action, then provides specific details in a structured manner. Every sentence serves a purpose.

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

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

For a simple two-parameter tool, the description covers the purpose, inputs, and outputs. It lacks an output schema but lists return fields. It could mention handling of ambiguous names, but overall it is sufficiently complete for usage.

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

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

The input schema has 100% coverage with descriptions, but the description adds significant value by providing examples (ticker, CIK, name) and explaining the v1 type restriction. This enriches parameter understanding 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 explicitly states the tool resolves entities to canonical IDs, specifies supported entity type (company), input formats (ticker, CIK, name), and output fields. It also distinguishes itself by noting it replaces 2-3 lookup calls, clearly indicating its purpose and value.

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 the tool (for canonical ID resolution across Pipeworx sources) and that it replaces multiple lookups. However, it does not explicitly state when not to use it or provide alternatives, though sibling tools are unrelated, reducing confusion.

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

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

No annotations provided, so description carries full burden. It explains the tool's behavior: returns verdict types, extracted structured form, actual value with citation, and percent delta. It does not mention side effects, read-only nature, or permissions, but for a validation tool, the description is sufficiently transparent.

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 concise at ~70 words across 4 sentences. Purpose is front-loaded, then scope, outputs, and efficiency comparison. No unnecessary detail.

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 only one parameter with good schema description, no output schema, and no annotations, the description is fairly complete. It covers purpose, supported domain, return values, and efficiency. Missing details on error handling or edge cases, but adequate for the tool's 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?

Schema coverage is 100% with a clear description of the 'claim' parameter, including an example. The tool description adds no additional parameter guidance beyond the schema, so baseline score of 3 is appropriate.

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

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

Description clearly identifies the tool as a fact-checker for natural-language claims, specifies the domain (company-financial for US public companies) and sources (SEC EDGAR + XBRL), and lists return values (verdict types, structured form, citation, delta). It also distinguishes from siblings by stating it replaces 4–6 sequential agent calls.

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

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

Description explicitly states supported claim types (company-financial, revenue/net income/cash, US public companies) via 'v1 supports...'. This provides clear context for when to use it. However, it does not explicitly state when not to use it or mention alternative tools for other domains.

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