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

Without annotations, the description carries full burden. It explains that Pipeworx selects the right tool and fills arguments, but does not disclose any limitations, required permissions, or whether the tool is read-only or destructive.

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 at three sentences with front-loaded purpose. Every sentence adds value: defines the tool, explains automation, and gives concrete examples.

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

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

For a single-parameter tool with no output schema, the description is largely complete. It explains how the tool works and what to expect, though it could mention whether answers are textual or structured.

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 single parameter 'question' described as 'Your question or request in natural language'. The description adds value by explaining the parameter's role and providing examples, exceeding 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 tool accepts plain English questions and returns answers from the best available data source. It provides specific examples, making the purpose unmistakable.

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 explains when to use the tool (for natural language questions) and implies not needing to browse other tools. However, it does not explicitly state when not to use it or provide 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 read-only, open-world, non-destructive behavior. The description adds rich behavioral details: resolved market, classifies bet type, fans out to appropriate packs, and returns a comparison. This goes beyond annotations without contradicting them.

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

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

The description is well-structured and front-loaded with the main purpose. Every sentence adds value, though the promotional closing sentence is slightly unnecessary. It is efficient for the information conveyed.

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 two parameters are fully described in the schema and the description covers the output format (evidence packet + comparison), the tool definition is complete. No output schema exists, but the description adequately explains return values.

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 explains that 'market' can be slug, URL, or text, but the schema already provides that. It does not add new parameter-specific semantics beyond what the schema describes.

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: research a Polymarket bet by pulling Pipeworx data. It lists inputs (slug, URL, question text) and outputs (evidence packet + comparison), distinguishing it from sibling tools by positioning it as the core demo product that simplifies fan-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 explicitly states use cases: 'should I bet on X?', 'what does the data say?', 'is there edge?'. It does not list alternative tools for other queries, but the usage context is clear and sufficient for an agent to decide when to invoke this tool.

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 burden. It discloses it returns paired data and pipeworx:// URIs, and specifies data points per type. It does not mention limitations or error handling, but covers key behavioral aspects.

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 serving a purpose: purpose, type-specific details, and output/efficiency benefit. 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?

Given no output schema, the description adequately covers what is returned (paired data, URIs) and the efficiency gain. Could mention error handling or format, but sufficient for the 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?

Schema coverage is 100%, baseline 3. The description adds value by linking the 'type' parameter to specific data output (e.g., 'type=company' returns revenue, net income, etc.), which is beyond the schema's enums.

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: 'Compare 2–5 entities side by side in one call.' It specifies the two entity types and distinguishes itself from sibling tools (which are unrelated Gads and memory 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 context: it's for comparing entities and replaces many sequential calls. However, it does not explicitly state when not to use or list 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 carries the full burden. It states that the tool searches and returns tools with names and descriptions, which is clear. However, it does not disclose any behavioral traits like whether the search is case-sensitive, how the ranking works, or any side effects. The description is adequate but not detailed.

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 two sentences, each serving a clear purpose: stating the action and providing usage guidance. No wasted words, and the key information is front-loaded. 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 that the tool has only 2 parameters (both documented), no output schema, and the context signals indicate low complexity, the description provides enough information for an agent to use it effectively. The only minor gap is the lack of detail on how results are ranked, but for a discovery tool, this is acceptable.

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 any additional meaning beyond what the schema already provides for the 'query' and 'limit' parameters. It does provide a good example for the query parameter in the schema, but the description itself adds no extra 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 it searches the Pipeworx tool catalog by describing what you need, and returns the most relevant tools with names and descriptions. It explicitly calls out the use case: call this FIRST when you have 500+ tools available. This distinguishes it from other tools, making the purpose unmistakable.

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

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

The description provides explicit usage guidance: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This tells the agent when to use this tool (first step in tool discovery) and implies that other tools should be used for actual operations once the right tool is found.

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 must compensate. Clearly states aggregation behavior, returns citation URIs, and notes speed limitation for federal contracts. Does not mention read-only nature or permissions, but behavioral traits are well disclosed.

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, front-loaded with purpose, lists data sources, notes return format, and provides alternatives. Every sentence adds value with no repetition.

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

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

Despite no output schema, description specifies return type (citation URIs) and covers data domains. Usage guidelines and alternative are included. Could mention error handling or limitations, but overall sufficient for effective use.

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 descriptions already cover both parameters with examples and restrictions (only company, ticker/CIK). Description adds context about data sources but no additional parameter-level meaning beyond schema. Baseline 3 due to high coverage.

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

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

Clear verb 'Full profile' with specific resources: SEC filings, XBRL, patents, news, LEI. Distinguishes from sibling tools like compare_entities and resolve_entity by mentioning it replaces 10–15 sequential 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?

Explicitly states when to use: replaces sequential agent calls. Provides alternative: use usa_recipient_profile for federal contracts. Input schema further instructs to 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, the description must carry the behavioral burden. It correctly identifies the tool as destructive (delete), but lacks details on whether deletion is permanent, if confirmation is needed, or if related data is affected.

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 conveys all essential information with no fluff. It is front-loaded 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 simple tool (one param, no output schema, no nested objects), the description is adequate but could mention whether deletion is reversible or if there are any side effects.

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 one required 'key' parameter. The description adds clarity by specifying 'Memory key to delete', which reinforces the schema's purpose.

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'), the resource ('a stored memory'), and the parameter ('by key'). It is specific and distinct from sibling tools like 'recall' and 'remember'.

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 you need to delete a memory, but does not provide guidance on when to use this tool versus alternatives like 'recall' or 'remember', nor does it mention prerequisites or consequences.

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 correctly indicates it's a read operation returning metrics. However, does not disclose if there are rate limits, data latency, or whether it returns only active campaigns or all campaigns. Behavior is generally clear 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?

Extremely concise: two sentences front-load the purpose and list metrics. No wasted words. Essential information presented efficiently.

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

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

Given no output schema and no annotations, the description is fairly complete for a simple metrics tool. However, it lacks details on pagination (limit parameter mentioned only in schema), data aggregation (daily or cumulative?), and whether campaigns without data are returned. Slight gaps.

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 describes all parameters with full coverage. Description adds context by naming the returned metrics (impressions, clicks, etc.), which helps infer parameter usage but doesn't add significant meaning beyond the 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?

Description clearly states it retrieves performance metrics for campaigns, listing specific metrics (impressions, clicks, cost, etc.). Distinguishes from siblings: 'gads_list_campaigns' likely lists campaigns without metrics, 'gads_get_campaign' is singular, but this one focuses on metrics.

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 implies usage for retrieving campaign metrics over a date range. No explicit guidance on when to use this vs. other tools like 'gads_search' (which may provide more flexible queries). No mention of prerequisites or limitations.

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 describes the operation as a read ('Get') and lists returned fields, but doesn't disclose any side effects, permissions, or limitations. The description is adequate but not 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?

One sentence with no wasted words. Front-loaded with purpose and lists key return fields efficiently.

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

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

For a simple read tool with two parameters and no output schema, the description is fairly complete. However, it could mention that it returns a single campaign object or any error conditions. Sibling tools provide context, but description alone is adequate.

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

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

Schema coverage is 100%, so the description adds minimal value beyond field names. It implies customer_id and campaign_id are used to identify the campaign, which is obvious from the schema. No additional constraints or usage notes provided.

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 ('Get') and resource ('Google Ads campaign'), explicitly listing returned fields (name, status, budget, bidding strategy, settings) and distinguishing from siblings like gads_list_campaigns which lists campaigns.

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 clearly states the tool retrieves detailed info for a specific campaign, implying use when you have an ID. It doesn't explicitly mention when not to use it or alternatives, but context from siblings suggests gads_list_campaigns for listing, so guidance 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?

Annotations are empty, so description carries full burden. It discloses that the tool returns specific fields and supports filtering by status and limit, which is useful. However, it doesn't mention pagination, rate limits, or whether it only returns active ad groups by default.

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 (two sentences) and front-loads the purpose. No unnecessary words. However, it could mention the limit default and output fields more efficiently.

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

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

Given no output schema, the description adequately states what fields are returned. However, it lacks context on error handling, performance implications of listing many ad groups, or how to interpret the response structure. Could be more complete for a list operation.

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

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

Schema coverage is 100% and all parameters have descriptions, so baseline is 3. The description adds no extra meaning beyond what the schema already provides (e.g., doesn't clarify that 'status' filter defaults to all).

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 lists ad groups for a campaign and specifies returned fields (names, IDs, statuses, CPC bid). However, it doesn't differentiate from sibling tools like gads_search or gads_list_campaigns, which could also list ad groups but with different scopes.

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 explicit guidance on when to use this vs. alternatives like gads_search, which might offer more flexible filtering. The description implies use for a specific campaign but doesn't mention prerequisites (e.g., campaign must exist) 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?

No annotations are provided, so the description must carry the burden. It states that it returns campaigns and lists fields, but does not disclose side effects (none expected, but not stated), performance limits, or whether it returns all campaigns or paginated results. Adequate but not detailed.

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 two sentences, front-loaded with the core purpose, and no wasted words. 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 the simple tool (no output schema, no nested objects), the description is adequate but could mention that results are paginated if limit is unspecified, or that all statuses are returned by default. It covers the basics but lacks some edge-case 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 description coverage is 100%, so the description adds minimal extra meaning beyond what the schema already provides. It mentions the returned fields but does not elaborate on parameter usage beyond listing them.

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

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

The description clearly states the verb 'list' and the resource 'campaigns in a Google Ads account', and specifies the returned fields: names, IDs, statuses, budgets, and types. This differentiates it from siblings like gads_get_campaign (single campaign) and gads_campaign_metrics (metrics).

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 listing campaigns, but does not explicitly state when to use this tool over siblings like gads_search or gads_campaign_metrics. No exclusions or alternatives are mentioned, relying on the user to infer from tool names.

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 burden. It notes execution of arbitrary queries, implying flexibility but also risk. Lacks details on mutability, permissions, or rate limits. Adequate but not thorough.

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 short sentences with a link, no fluff. Front-loaded with purpose and use case.

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 is sufficient for a query execution tool. Could mention that output format is raw GAQL response, but otherwise complete.

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

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

Schema coverage is 100%, and description adds minimal extra meaning beyond schema. The GAQL syntax link provides value, but parameters are well-documented in 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 it executes a custom GAQL query, distinguishes from other tools by noting 'advanced queries not covered by other tools'.

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

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

Explicitly says to use when other tools don't cover the query, and provides a link to GAQL syntax. Could mention when not to use, but context implies it's for complex cases.

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

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

Discloses rate limiting (5 messages per identifier per day) and that it is free. Specifies the format of the message. Lacks details on feedback handling or confirmation, but for a simple feedback tool this is adequate.

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

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

Extremely concise: two sentences describing purpose and usage, plus a brief note on rate limiting. No unnecessary words; 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 the simple input and no output schema, the description covers purpose, allowed content, constraints, and rate limit. It could mention what happens after submission (e.g., no response expected), but overall complete for its role.

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 reiterates the enum values for 'type' and suggests being specific in the message, adding slight guidance 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 sends feedback to the Pipeworx team and lists specific use cases: bug reports, feature requests, missing data, or praise. Distinguishes itself from sibling tools which perform data retrieval or actions.

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

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

Provides explicit categories of feedback and instructs users to describe what they tried in terms of Pipeworx tools/data, and not to include the end-user's prompt verbatim. Mentions rate limit of 5 per day. Does not explicitly state when not to use, but this is implied by the listed use cases.

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

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

Annotations already indicate read-only, open-world, non-destructive. Description adds substantial behavioral detail: walks child markets, searches across events, groups related markets, checks monotonicity, returns ranked opportunities with reasoning. No contradiction.

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 (about 100 words) and front-loaded with core purpose. Every sentence serves a clear purpose: defining the tool, explaining two modes, and indicating return type. No redundant content.

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 states what is returned (ranked opportunities with direction and reasoning). All necessary context for a tool with two modes and optional parameters is present. Annotations cover safety and external data access.

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 basic descriptions. Description adds value by clarifying that 'event' can be slug or full URL, and explains the context for both parameters with examples. Exceeds baseline 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?

Clearly states it finds arbitrage opportunities by checking monotonicity violations. Distinguishes two modes (event and topic) with examples, and implicitly differentiates from sibling tools like 'polymarket_edges'.

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

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

Provides explicit guidance on when to use each mode (single-event vs cross-event) and explains that cross-event catches cases missed by single-event. Lacks explicit 'when not to use' or alternative tools, but context is 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 (readOnlyHint:true, destructiveHint:false) already indicate safe read operation. Description adds rich behavioral context: scans top markets, groups by asset, fetches each asset's price history ONCE, computes model probability per market, ranks by |edge|, and returns top N with suggested trade direction. This explains internal process and data sources (FRED, coinpaprika) beyond the annotations. No contradiction.

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 serving a distinct purpose: what it does, how it works, and who it's for. Front-loaded with main action. No redundant or superfluous text. 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?

Given the complexity (custom model, multiple steps, 3 optional parameters, no output schema), the description covers the algorithmic process, data sources, and intended use case. It is complete enough for an agent to decide if the tool is appropriate. Minor gap: does not specify behavior when no markets meet criteria (e.g., returns empty list), but this is acceptable.

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% - each parameter (limit, window, min_edge_pp) already has a clear description. The description adds the context that 'Returns top N ranked by edge magnitude' which reinforces the limit parameter, but does not add substantial new semantics beyond what the schema provides. 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?

Description clearly states it scans highest-volume Polymarket markets, finds where Pipeworz data disagrees with market price, computes model probability, ranks by edge, and returns top N with suggested direction. It specifies 'V1 covers crypto-price bets (lognormal model from FRED + live coinpaprika price)' which distinguishes it from siblings like polymarket_arbitrage and bet_research. The verb-resource combination is specific and unambiguous.

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

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

Description explicitly says 'Built for the "what should I bet on today" question — agents/users discover opportunities without paging through hundreds of markets by hand.' This gives clear context for when to use. While it does not explicitly name alternatives, the description implies its purpose versus other tools (e.g., for specific research use bet_research, for arbitrage use polymarket_arbitrage). The guidance is strong but could be more explicit about when not to use.

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 key behavior (omit to list) and purpose (retrieve saved context). However, it doesn't disclose side effects, return format, or error cases, which would be helpful.

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, both essential. No fluff. First sentence states action, second sentence explains 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?

Tool is simple (0 required params, no output schema). Description explains both modes (retrieve by key, list all) and rationale. No output schema needed for a retrieval tool.

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

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

Schema description coverage is 100% for the single parameter 'key'. Description adds context by explaining that omitting key lists all memories, which complements the schema's description. 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?

Description clearly states the tool retrieves a memory by key or lists all memories when key is omitted. It distinguishes from sibling tools like 'remember' (store) and 'forget' (delete), but doesn't explicitly name them.

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: to retrieve context saved earlier. Implicitly contrasts with 'remember' (store) and 'forget' (delete). No explicit when-not-to-use or alternatives listed, but context 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?

With no annotations, the description carries full burden. It reveals parallel fan-out behavior, input formats (ISO/relative), and return structure (structured changes, total_changes count, URIs). However, it does not discuss rate limits, error handling, or what happens when no changes are found.

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?

Five sentences, front-loaded with purpose, then structured details. Every sentence provides essential information without redundancy.

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

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

Given no output schema and no annotations, the description is nearly complete. It covers inputs, behavior, and likely outputs. A minor gap is the lack of explanation for cases where no changes exist or how errors are handled, but overall it is sufficient for effective tool 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?

Schema coverage is 100% with basic descriptions, but the description adds significant value: explains 'since' accepts ISO or relative with examples, recommends '30d' or '1m' for typical use, clarifies 'value' can be ticker or CIK, and confirms 'type' is limited to 'company.' This goes well 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 that the tool retrieves 'what's new about an entity since a given point in time.' It explicitly mentions the specific data sources fanned out for type 'company' (SEC EDGAR, GDELT, USPTO), which distinguishes it from siblings like 'entity_profile' that likely provide static profiles.

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: 'Use for "brief me on what happened with X" or change-monitoring workflows.' It does not mention when not to use or alternatives among siblings, but the context is clear enough for basic selection.

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 persistence behavior (authenticated vs anonymous sessions) and implicit state modification. With no annotations provided, description carries full burden; it adds behavioral context about session duration but lacks details on overwrite behavior or limits.

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

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

Three sentences, front-loaded with action, usage guidance, and behavioral note. Every sentence adds value; no wasted words.

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

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

Given no output schema and no annotations, description covers purpose, usage, and persistence. Could mention overwrite behavior or key uniqueness, but overall sufficient for a simple key-value store.

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. Description adds minimal extra meaning beyond schema; schema already provides clear examples for 'key' and type for 'value'. No additional parameter context in 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?

Clearly states verb 'store' and resource 'key-value pair in your session memory', and distinguishes from sibling 'recall' by focusing on storage vs retrieval. No ambiguity.

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

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

Explicitly says 'use this to save intermediate findings, user preferences, or context across tool calls', providing clear when-to-use guidance. Does not explicitly mention when not to use or alternatives, but context is 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?

No annotations present, so description carries full burden. It details the input types, output fields (ticker, CIK, name, URIs), and mentions it is a single call. It does not explicitly state read-only or side effects, but the described behavior implies a lookup 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?

Two sentences with no extraneous words. Key information is front-loaded: purpose, version, input examples, output items, and benefit (replaces multiple calls). Perfectly concise.

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

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

Given the tool's simplicity (2 params, no nesting), the description is complete. It explains the input format, expected output (even without output schema), and the tool's value proposition. No gaps remain for the intended 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 coverage is 100%, baseline 3. The description adds value by explaining the 'type' enum (v1 only 'company'), describing 'value' options (ticker, CIK, name), and providing examples, which goes beyond the schema's property 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 resolves an entity to canonical IDs, specifies verb 'resolve' and resource 'entity', and gives concrete examples (ticker, CIK, name). It distinguishes from siblings by focusing on entity resolution across Pipeworx sources.

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 explains when to use (canonical ID lookup) and notes it replaces 2-3 calls. However, it does not explicitly state when not to use or provide alternatives beyond the implied replacement.

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 describes return values (verdict, extracted form, actual value with citation, percent delta) and that it replaces sequential calls. Lacks details on failure modes or rate limits, but given read-only nature, 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?

Two sentences, front-loaded with purpose, no unnecessary words. 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 low complexity (one parameter, no output schema), the description covers input, output, and scope. Could include more on return structure details, but overall sufficient.

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 already describes the single parameter 'claim' well. Description adds context with examples and domain restriction, enhancing understanding 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 it fact-checks natural-language claims, specifies domain (company-financial, public US companies), and lists verdict types and return components. Distinguishes from sibling tools which are mostly advertising or memory-related.

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 defines scope (v1 supports company-financial claims via SEC EDGAR + XBRL) and explains it replaces multiple agent calls. Does not explicitly state when not to use or list alternatives, but scope is clear enough for an agent to decide.

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