Census

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

No annotations provided, so description carries full burden. It discloses the tool auto-selects sources and fills arguments, but lacks details on data sources or limitations.

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 and front-loaded with purpose, followed by examples. Every sentence adds value.

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

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

Given low complexity (1 param, no nested objects, no output schema), description is adequate but could mention scope of data sources or error handling.

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

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

Schema coverage is 100%, so baseline is 3. Description adds natural language usage context but does not exceed what the schema provides for the single parameter.

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

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

The description clearly states the tool accepts plain English questions and returns answers by selecting the best data source, with concrete examples distinguishing it from siblings.

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

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

The description implies usage via 'just describe what you need' but does not specify when not to use it or alternatives 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?

Annotations already indicate read-only and non-destructive. Description adds valuable behavioral details: resolves market, classifies bet, fans out to packs, returns comparison. No contradictions.

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

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

Front-loaded with purpose, then details. The description is thorough but slightly wordy; it could be trimmed without losing 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, but description explains return values (evidence packet + comparison) and covers input types and fan-out logic. The tool's complexity is well addressed.

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 both parameters. The description adds context for 'market' input types, but 'depth' is already well-described in schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data, with specific input examples (slug, URL, question) and output (evidence packet, market-vs-model comparison). It distinguishes itself from siblings like 'ask_pipeworx' by being specialized for bets.

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 use cases are provided: 'should I bet on X?', 'what does the data say?', 'is there edge?'. It implies when to use, but does not explicitly mention when not to use or suggest alternative tools.

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

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

Annotations are empty, so the description carries the full burden. It explains the data source and common variables but does not disclose behavioral traits like rate limits, authentication requirements beyond the API key, or whether the tool is read-only. It adds context beyond schema but lacks detail on API 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 concise, with a clear purpose and helpful examples. It is front-loaded with the main function and then provides variable codes. Could be slightly more structured with explicit parameter details, but overall 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 has 4 parameters, no output schema, and no annotations, the description covers the core purpose and key variable codes. It lacks information on return format or pagination, but for a simple data retrieval tool, it is largely complete. Sibling differentiation is implicit.

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 context about variable codes and geography examples, which is helpful but doesn't go beyond what a well-documented schema provides. No new meaning for parameters like _apiKey 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 gets ACS 5-year data from the U.S. Census Bureau, specifies it's for housing statistics, and lists common variable codes. It differentiates from sibling tools like census_building_permits or census_homeownership by focusing on core housing data.

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

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

The description implies use for housing-related ACS data but does not explicitly say when to use this over other census tools. It provides helpful variable codes but lacks direct comparison to sibling tools for exclusion criteria.

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 clarifies that no API key is needed (a key behavioral trait), and that the endpoint is for discovery, not data retrieval. It doesn't disclose response format or limits, but given the simplicity, this is adequate.

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

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

Three sentences, no wasted words. Each sentence serves a distinct purpose: action, requirement, and utility. Information is front-loaded with the verb.

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 zero required parameters, no output schema, and a simple list operation, the description covers the essential purpose and behavioral aspects. It could mention that the output is a list of dataset objects, but this is not critical.

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 does not add any parameter-specific meaning beyond what the schema provides. The _apiKey parameter is already well-documented in 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 uses specific verbs ('List') and a clear resource ('Census Bureau datasets'). It explains the tool's utility for discovering dataset identifiers, descriptions, and variables, distinguishing it from data-querying siblings like census_acs.

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 'No API key required' and positions the tool as a prerequisite step before querying specific data. However, it does not mention when not to use it or list alternative tools explicitly.

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

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

Annotations are empty, so the description must cover behavioral traits. It correctly indicates it is a data retrieval tool (not destructive), but does not disclose any rate limits, data freshness, or potential errors. However, it does clarify it tracks 'new privately-owned housing units authorized by building permits,' which sets expectations.

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 concise sentences that clearly state purpose and source. No redundant or filler content. Front-loaded with 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?

For a tool with 4 parameters (100% schema coverage) and no output schema, the description adequately states what data is returned (monthly building permits data) but does not mention response format or limitations. It is sufficient for basic understanding 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%, so each parameter is already described in the schema. The description adds no extra meaning beyond what the schema provides. Baseline 3 is appropriate.

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

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

Clearly states it gets monthly building permits data from the Census Bureau residential construction survey. The description specifies the resource (Census Bureau building permits) and the scope (tracks new privately-owned housing units authorized by building permits), distinguishing it from siblings like census_acs or census_homeownership.

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

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

No guidance on when to use this tool versus alternatives like census_housing_starts or census_available_datasets. Does not mention prerequisites or when not to use it.

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

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

Annotations are empty, so the description must carry behavioral transparency. It does not disclose any side effects, authorization requirements, or return format details. However, the tool appears to be a read-only data query, which is implied but not stated.

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 concise sentences that front-load the purpose and metric. No redundant information; every word 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?

The tool has only 2 parameters with 100% schema coverage and no output schema or nested objects. The description is minimally complete: it explains what the tool does and the metric, but lacks details like response format or error conditions, which 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?

Schema description coverage is 100%, so the description does not need to add param details. It adds no extra meaning beyond the schema, which is sufficient for a baseline score of 3.

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

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

The description clearly states the tool returns quarterly homeownership rates from a specific survey, with the exact metric defined as the percentage of owner-occupied units. This is a specific verb+resource combination that distinguishes it from sibling tools like census_acs or census_building_permits.

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 the tool is for accessing homeownership data from HVS, but does not explicitly state when to use it over other census tools or provide alternatives. No exclusions or context about data availability are mentioned.

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

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

Annotations are empty, so the description must cover behavioral traits. It mentions it gets data from the Census Bureau but does not disclose any API rate limits, required authentication beyond the API key, or potential response size. The description is neutral and not misleading.

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 sentence that is concise and front-loaded with the action. It includes key data types but could be slightly more 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 that the tool has 4 parameters, no output schema, and no annotations, the description is adequate but could mention output format or pagination. It is minimally viable.

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

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

Schema description coverage is 100%, so the schema already documents all parameters. The description does not add new meaning beyond listing the data types. Baseline 3 is appropriate.

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

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

The description clearly states it gets residential construction data from the Census Bureau, specifying three data types: housing starts, units under construction, and completions. It distinguishes itself from sibling tools like census_building_permits and census_homeownership by focusing on housing starts and construction activity.

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

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

The description implies usage for retrieving housing construction data but does not explicitly state when to use this tool versus alternatives like census_building_permits. It provides no guidance on prerequisites or data frequency.

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

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

No annotations are provided, so the description carries full burden. It describes the returned data fields for each type, mentions paired data and resource URIs, and implies a read-only operation. It lacks explicit safety or authorization details but is fairly comprehensive.

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

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

The description is extremely concise (under 60 words) with no redundant information. Every sentence adds value, clearly communicating the tool's purpose, types, data, and efficiency benefit.

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

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

With no output schema, the description adequately explains the return structure and content. It covers both entity types, specifies min/max entities, and mentions resource URIs. The agent can fully understand what to expect.

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

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

Schema description coverage is 100%, and the description adds significant meaning beyond the schema by detailing the specific metrics returned for each entity type and clarifying input formats (tickers/CIKs vs. drug 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 uses a specific verb 'Compare', specifies the resource 'entities', and clearly differentiates between 'company' and 'drug' types with distinct metrics. No sibling tool performs comparison, so it stands out.

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 states it replaces 8–15 sequential agent calls, implying efficiency, but does not explicitly state when to use it versus alternatives. However, since no sibling tool does comparison, the usage 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?

With no annotations provided, the description must cover behavioral traits. It discloses that it searches a catalog and returns relevant tools with names and descriptions, which is a read-only, non-destructive operation. The description implies it is a search tool, but does not mention any side effects or access restrictions. The lack of annotations raises the burden, but the description adequately conveys the search 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 three sentences, each earning its place: first states the core action, second states the output, third provides strategic usage advice. It is front-loaded and no fluff.

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

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

Given the tool's simplicity (2 params, no output schema, no nested objects) and the context of many sibling tools, the description is complete. It explains what it does, when to use it, and what it returns. No missing information.

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 baseline is 3. The description adds value by explaining that the query is a 'Natural language description of what you want to do' with examples, and for limit it provides default and max values. This enhances 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 clearly states the verb 'Search' and the resource 'Pipeworx tool catalog', and specifies the purpose 'by describing what you need. Returns the most relevant tools with names and descriptions.' This distinguishes it from sibling tools, which are specific data tools, as this is a meta-tool for discovering 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 explicitly says 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear guidance on when to use it (as a first step) and the context (many tools available), and implies it is an alternative to manually searching through sibling tools.

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

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

No annotations are provided, so the description must disclose behavioral traits. It mentions that the tool returns pipeworx:// citation URIs and replaces 10-15 sequential calls, which is useful but does not explicitly state whether the operation is read-only or has side effects. The description adds some behavioral context but is not fully 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?

The description is very concise, with two sentences and a brief list. It front-loads the core purpose and immediately provides actionable details. Every sentence earns its place, with no unnecessary words.

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

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

Despite having no output schema, the description provides a good overview of what the tool returns (data sources and URI format). It also explains its efficiency benefit. However, it lacks a detailed output structure description, which would improve completeness given the tool's complexity.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add significant new meaning beyond the schema; it lists data sources but that pertains to output, not parameters. The parameter descriptions in the schema already explain type and value in detail.

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

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

The description clearly states the tool retrieves a full profile of an entity, listing specific data sources (SEC filings, XBRL, patents, news, LEI). It distinguishes itself from siblings by mentioning an alternative tool (usa_recipient_profile) and implies use cases. The verb 'Full profile' and resource entity are specific and actionable.

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 this tool (for company profiles) and when not (for federal contracts, call usa_recipient_profile instead). The input schema's parameter description also advises using resolve_entity if only a name is available, providing clear guidance on prerequisites and alternatives.

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

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

No annotations are provided, so the description must fully disclose behavioral traits. It states deletion but does not mention whether deletion is permanent, requires confirmation, or affects related memories. This is insufficient for a destructive operation.

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

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

The description is a single sentence, efficient and front-loaded. However, it could benefit from a brief behavioral note (e.g., permanence) without becoming verbose.

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

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

Given the lack of annotations and output schema, the description is too brief. For a single-parameter deletion tool, it should clarify irreversibility, scope (e.g., current user only), and any confirmation needed. The current description leaves 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%, and the description does not add meaning beyond the schema's 'key' parameter description. The parameter description in the schema is adequate, so baseline 3 is appropriate.

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

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

The description 'Delete a stored memory by key' uses a specific verb ('Delete') and resource ('stored memory by key'), clearly distinguishing it from siblings like 'remember' (store) and 'recall' (retrieve).

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

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

The description does not explicitly state when to use this tool vs alternatives, but its name and purpose imply deletion. The sibling tools 'recall' and 'remember' suggest complementary operations, but no guidance is provided on prerequisites or scenarios.

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 discloses rate limiting (5 per day per identifier) and indicates the tool is free, providing basic behavioral context. However, without annotations, it does not elaborate on data handling, privacy, or confirmation behavior, which is acceptable for a simple feedback 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?

The description is 4 sentences, all essential: action, use cases, message guideline, and rate limit. No redundancy or fluff, highly 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 feedback tool's simplicity (3 params, no output schema), the description covers purpose, message formatting, and constraints. Missing confirmation or response details, but not critical for this tool type.

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 prompting users to describe their Pipeworx actions in the message and enumerating the purpose of each 'type' enum value, going slightly beyond 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 explicitly states 'Send feedback to the Pipeworx team' (clear verb+resource) and lists specific use cases (bug reports, feature requests, etc.), distinguishing it from all sibling tools which focus on data retrieval or memory.

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 guidance on when to use (feedback types) and how to write messages (describe Pipeworx context, no user prompt verbatim), and mentions rate limits. It lacks explicit 'do not use' conditions, but the positive instructions are strong.

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, and destructiveHint=false. The description adds valuable behavioral context, explaining that the tool 'walks the child markets, extracts dates / thresholds from each question, sorts them, and reports any pair where the cheaper-side-should-be-cheaper rule is violated.' This goes beyond annotations, though the safety profile is already covered.

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 (about 100 words) and front-loaded with the core purpose. Every sentence adds value, explaining the concept, input, process, and output without fluff.

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

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

Despite lacking an output schema, the description specifies the return format: 'Returns a list of {market_a, market_b, gap_pp, suggested_trade} entries.' Combined with explanations of input and behavior, it provides complete context for an agent to use this tool effectively.

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

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

The single parameter 'event' has 100% schema description coverage (Polymarket event slug or URL). The description adds context on how the parameter is used ('pass a Polymarket event slug or URL; the tool walks the child markets...'), which is helpful 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: 'Find arbitrage opportunities within a Polymarket event by checking for monotonicity violations.' It uses a specific verb ('find') and resource ('arbitrage opportunities'), and the detailed explanation distinguishes 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?

The description provides explicit guidance on when to use the tool (for events with multiple 'by date' or 'by threshold' markets) and explains the underlying monotonicity rule. It implies appropriate contexts and gives an example ('P("BTC hit $150k by May 31") ≤ P("BTC hit $150k by Jun 30")').

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 goes well beyond annotations by detailing the underlying model (lognormal from FRED + live coinpaprika price), the process (groups by asset, fetches history once, computes probability), and the output (top N ranked by edge magnitude with suggested direction). This fully discloses behavioral traits such as read-only nature and open-world dependency without any contradictions.

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

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

The description is front-loaded with the main action and provides necessary technical details without excessive verbosity. It is structured logically (action, method, output, use case). However, a minor reduction in technical jargon could improve clarity for some agents.

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?

Without an output schema, the description should specify the exact return structure. It mentions 'top N ranked by edge magnitude with suggested trade direction' but omits field names or data types. For a tool returning ranked markets, this is a notable gap, though the overall purpose and behavior are well covered.

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 does not add additional parameter meaning beyond what the schema already provides, e.g., 'limit' is described as 'Top N edges' in both. While the description reinforces usage context, it does not enhance understanding of the parameters themselves.

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

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

The description states a specific verb ('scan'), resource ('highest-volume Polymarket markets'), and outcome ('return ones where Pipeworx data disagrees most with market price'). It clearly distinguishes itself by focusing on edge discovery for betting, setting it apart from siblings like 'polymarket_arbitrage' which likely targets different use cases.

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, providing clear usage context. However, it does not mention when not to use the tool or explicitly compare to alternatives like 'polymarket_arbitrage', though the differentiation is implicit.

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

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

With no annotations, description must disclose behavior. It states retrieval and listing, but doesn't mention any side effects or limitations (e.g., memory persistence, deletion 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?

Two sentences, clear and front-loaded. First sentence states purpose and usage, second sentence provides usage context.

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 simple tool with one optional parameter and no output schema, description covers key usage. Could add info about return format or memory lifecycle, but adequate for current complexity.

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

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

Schema already covers parameter well (100% coverage). Description adds context that omitting key lists all, which schema implies but description makes explicit.

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

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

Description clearly states verb 'retrieve' and resource 'memory', specifies two modes (by key or list all) and distinguishes from 'remember' and 'forget' 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?

Explicitly says when to use (retrieve context saved earlier) and how to list all (omit key). Could mention when not to use (e.g., for new information, use remember).

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 exist, so description carries full burden. It discloses parallel fan-out to three sources, accepted date formats, and return structure (changes, count, URIs). Does not mention potential delays or error handling, but is good for a read operation.

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

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

The description is concise (4 sentences), front-loaded with purpose, and logically structured from general behavior to parameter details and use cases. No superfluous 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 no output schema, description adequately explains return structure. Covers fan-out behavior and parameter semantics. Missing pagination or limits on results, but overall sufficient for a change-monitoring tool.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by explaining 'since' formats (ISO vs relative) with examples, and clarifies that only 'company' type is supported, going beyond 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 tool retrieves changes for an entity since a given time, specifying behavior for company type (SEC, GDELT, USPTO). It distinguishes from siblings like 'entity_profile' by focusing on recent 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 using for 'brief me on what happened with X' or change-monitoring workflows. Lacks explicit when-not-to-use or direct comparison to alternatives, but guidance is clear and actionable.

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 authenticated users get persistent memory and anonymous sessions last 24 hours, which is important behavioral context. It does not mention any destructive effects or rate limits, but these are not likely needed for a memory store.

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 adding essential information: purpose, typical use cases, and persistence behavior. No wasted words; information is front-loaded.

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

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

Given no output schema and simple input schema, the description is complete enough. It explains what the tool does, when to use it, and a key behavioral detail (persistence). It could mention return value or error cases, but not strictly necessary for this simple 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 baseline is 3. The description adds minimal parameter information (key and value) but does not elaborate beyond what the schema provides. Examples are given in schema descriptions, not in the tool 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 stores a key-value pair in session memory, with a specific verb ('store') and resource ('session memory'). It distinguishes from siblings like 'recall' (retrieve) and 'forget' (delete) by its purpose of saving data.

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

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

The description explicitly says to use this tool for saving intermediate findings, user preferences, or context across tool calls. It provides context but does not explicitly state when not to use it or mention alternatives, though siblings are implied.

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 tool's behavior: single call, v1 supports only company, accepts three identifier formats, and returns canonical IDs and URIs. It does not explicitly state read-only or no side effects, but for a lookup tool, this is adequate.

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

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

The description is extremely concise—two sentences with no waste. It front-loads the purpose and efficiently conveys key details (v1 restriction, accepted inputs, outputs). 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?

Despite no output schema, the description details the return values (ticker, CIK, company name, URIs). For a simple lookup tool with two parameters, the description is complete and leaves no major gaps in 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 both parameters. The description adds value by providing concrete examples (e.g., 'AAPL', '0000320193', 'Apple') that clarify the value parameter's accepted formats, going beyond the schema's generic 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 explicitly states the tool's purpose: 'Resolve an entity to canonical IDs across Pipeworx data sources in a single call.' It specifies the supported entity type (company) and input formats (ticker, CIK, name), and highlights it replaces multiple lookup calls, clearly distinguishing 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?

The description provides clear usage context by specifying the input formats and noting it replaces 2–3 lookup calls, implying efficiency. However, it does not explicitly state when not to use this tool or mention alternatives, though sibling tools are unrelated.

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 the description must convey behavior. It discloses return values (verdict, structured form, actual value with citation, percent delta) and notes the external data source. However, it omits details like latency, rate limits, or error conditions for a tool making external API calls.

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

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

Three sentences, each with distinct value: purpose, domain/source, and return+efficiency. No filler or redundancy. Information is front-loaded and scannable.

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

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

With one parameter and no output schema, the description covers the essential context: what it does, what claims it handles, what returns to expect, and why it's efficient. It lacks an explanation of verdict meanings or examples of output, but these are not critical for a well-documented 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 coverage is 100% for the single parameter 'claim', so baseline is 3. The description adds example values and clarifies it's a natural-language claim, but the schema already provides a clear description. Minimal additional 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 clearly specifies the tool's purpose: fact-checking natural-language claims, with a specific domain (US public company financials). It provides examples ('Apple's FY2024 revenue...'), distinguishes from sibling tools like census_acs or entity_profile, and uses a specific verb+resource combination.

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

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

The description explicitly states the supported claim types (company-financial, revenue/net income/cash for public US companies) and mentions the data source (SEC EDGAR + XBRL). It implies usage context by noting it replaces 4-6 sequential agent calls, but does not directly enumerate when to avoid the tool or compare to alternatives.

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