Cheapshark

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

Annotations already declare readOnlyHint, openWorldHint, destructiveHint. Description adds valuable context: routes to 2,520 tools, fills arguments, returns structured answers with pipeworx:// citation URIs. 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 thorough but slightly lengthy. It front-loads the key preference message and provides examples. Every sentence adds value, though could be trimmed slightly.

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

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

Given the tool's complexity (routing to 2,520 tools, no output schema), the description covers purpose, usage, return format (structured with citations), and examples. Lacks explicit discussion of error handling or limitations, but sufficient for an agent.

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

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

Schema coverage is 100% with a basic description for 'question'. The tool description greatly expands on acceptable question types and domains, adding significant semantic value beyond the schema.

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

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

The description clearly states the tool routes questions to a vast array of structured data sources, prefers over web search, and distinguishes itself from sibling tools like 'bet_research' or 'compare_entities'.

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 'PREFER OVER WEB SEARCH', lists precise scenarios (SEC filings, FDA data, etc.), and gives example queries. Clearly guides when to use this tool vs 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?

The description discloses behavior beyond annotations: it resolves the market, classifies the bet, fans out to specific data packs based on bet type, and returns an evidence packet with market-vs-model comparison. Annotations already indicate read-only and open-world hints, but the description adds concrete details without 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?

The description is front-loaded with the main purpose, then provides details on inputs, behavior, and use cases. While it is somewhat verbose, every sentence adds value. Could be shortened slightly, 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's complexity (market resolution, classification, fan-out, evidence packet), the description covers key aspects well. However, the output ('evidence packet') is not fully detailed, and no output schema exists to compensate. Still, the description provides sufficient context for an AI agent to understand the tool's role and capabilities.

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 mentions the 'depth' parameter enum and the 'market' parameter input formats, but does not add significant meaning beyond what the schema already provides. It reiterates the same information.

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 the relevant Pipeworx data for it in one call.' It specifies the action (research) and the resource (Polymarket bet), and distinguishes itself from siblings as the 'core demo product' that aggregates data from multiple packs in one call.

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 usage guidance is provided: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?".' It contrasts with alternatives by stating that agents using this tool 'convert better than ones that have to discover the packs themselves,' implying when to choose this over other 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 already declare readOnly and non-destructive; description adds data sources (SEC EDGAR/XBRL, FAERS) and return format (paired data + citation URIs). 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?

Concise overall; front-loaded with main purpose, then usage triggers, per-type details, and efficiency note. No superfluous text.

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 adequately specifies return type (paired data + URIs) and per-field details for each entity type. Adequate for a two-parameter 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%; description adds context by explaining how 'values' should be specified per type (tickers/CIKs for company, drug names for drug) and elaborates on the 'type' enum.

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 'Compare 2–5 companies (or drugs) side by side', specifies data sources per type, and distinguishes itself from siblings like entity_profile by replacing 8–15 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?

Explicit usage patterns provided ('compare X and Y', 'X vs Y'), with concrete examples. Does not explicitly state when not to use, but context implies for single entities use other 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?

The description adds no behavioral transparency beyond what annotations already provide (readOnlyHint=true, destructiveHint=false). It does not disclose return format, pagination, or error behavior, which a read operation might benefit from.

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 (three words) and front-loaded. While it could be slightly more informative, it contains no unnecessary 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 lack of output schema and parameter descriptions, the tool description is incomplete. It does not explain what 'detail' includes or how the id is structured, which is needed for correct invocation.

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 only parameter 'id' is typed as string but lacks any description in both schema and tool description. With 0% schema description coverage, the description should clarify the expected format (e.g., numeric vs alphanumeric), but it does not.

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 'Single deal detail' clearly indicates the tool retrieves details for one specific deal, distinguishing it from the sibling 'deals' tool which likely lists multiple. However, it lacks an explicit verb (e.g., 'get', 'fetch'), making it slightly less direct.

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 usage guidance is provided. The description does not specify when to use this tool over alternatives like 'deals' or 'compare_entities', nor does it mention prerequisites or context.

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

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

Annotations (readOnlyHint: true, destructiveHint: false) already indicate safe read-only behavior. The description adds no further behavioral context such as pagination, return format, or rate limits. Minimal extra value.

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 (4 words). While brevity is valued, it lacks sufficient detail and structure to be maximally helpful. It does not waste words but is too minimal.

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 15 parameters and no output schema, the description is deeply incomplete. It omits what constitutes a 'deal', return format, filtering behavior, and how parameters interact. Annotations provide safety context but not functional completeness.

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

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

Schema description coverage is only 13% (2 out of 15 parameters have descriptions). The tool description does not explain any parameters, leaving most cryptic (e.g., 'AAA', 'desc'). Did not compensate for low schema coverage.

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

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

The description 'Search deals across stores' clearly specifies the action (search) and resource (deals across stores). It is unambiguous but lacks specificity about the type of deals (e.g., game deals). It distinguishes from sibling 'deal' (singular) suggesting a list vs. single item.

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 usage guidelines provided. The description does not indicate when to use this tool vs. alternatives like 'deal' or other search tools. No prerequisites or exclusions 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 indicate read-only, non-destructive. Description adds that it returns top-N tools with names and descriptions, which is consistent and clarifies output behavior beyond the annotations.

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

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

Description is front-loaded with the core purpose and includes examples and usage guidance. Though a bit long, every sentence serves a purpose and no extraneous content is present.

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 discovery tool, the combination of schema, annotations, and description fully covers purpose, usage, parameters, and output. No output schema is needed given the explicit statement of return format.

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 both parameters. The description does not add new constraints or clarify formats beyond what the schema already provides, so 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 the tool discovers tools by describing data/task. It specifies verb (find/discover) and resource (tools), and provides extensive examples distinguishing it from sibling tools which perform specific 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?

Explicitly says 'Call this FIRST' and describes when to use (browse, search, discover what tools exist). No explicit when-not case, but the context strongly implies it's for exploration before using specific 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 already declare readOnlyHint=true and destructiveHint=false. Description adds valuable behavioral context: returns pipeworx:// citation URIs, specifies the types of data aggregated, and implies the tool is comprehensive and open-world (consistent with openWorldHint=true).

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

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

The description is a single paragraph that efficiently front-loads the purpose and includes all key details. Slightly more structured bullet points could improve readability, but overall it is concise and informative.

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 lists exactly what the tool returns (SEC filings, revenue/net income/cash position, USPTO patents, news, LEI) and mentions citation URIs. This is comprehensive for an agent to understand the tool's output.

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 enriches both parameters: explains type is currently limited to 'company' (with future expansion noted), and for value clarifies ticker or CIK formats, and explicitly excludes names, directing to resolve_entity.

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 function: 'Get everything about a company in one call'. It lists specific data types (SEC filings, fundamentals, patents, news, LEI) and distinguishes from alternatives by mentioning it replaces calling 10+ pack 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?

Explicit usage examples are given: 'tell me about X', 'give me a profile of Acme', etc. It also instructs to use resolve_entity first if only a name is available, providing clear when-to-use and when-not-to-use guidance.

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

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

Annotations already mark destructiveHint true; description confirms deletion and adds context about clearing sensitive data, but does not significantly expand beyond annotation disclosure.

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, no extraneous information, front-loaded with core purpose.

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

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

For a simple one-parameter tool, the description covers purpose, usage context, and related tools adequately.

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 only mentions 'key' without adding new meaning beyond the schema's 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 action ('Delete a previously stored memory by key') and distinguishes from sibling tools 'remember' and 'recall'.

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 use cases (stale context, task done, clear sensitive data) but does not explicitly state when not to use or list alternatives beyond mentioning pair 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 already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds no further behavioral context but does not contradict annotations. With annotations covering safety, a baseline 3 is appropriate.

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 short (3 words), which is concise but under-specified. It does not earn its place by providing useful guidance beyond the name.

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 low complexity, the description should explain what 'details' entails. It fails to set expectations about the return value, making the tool poorly documented for an agent.

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

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

The sole parameter 'id' has no description in the schema (0% coverage). The description does not clarify its meaning or format, leaving the agent to infer what identifier is required.

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 'Game details.' is vague; it does not specify a clear verb or resource. It only restates the tool name with minimal elaboration, failing to distinguish from the sibling 'games' tool.

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 'games'. There is no mention of context, prerequisites, or exclusions, leaving the agent without differentiation cues.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, indicating a safe read operation. The description adds no behavioral context beyond 'lookup', missing details like return format or pagination.

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 only two words, which is under-specification rather than effective conciseness. It lacks necessary detail for a tool with multiple parameters.

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 four parameters, no output schema, and no parameter descriptions, the description is severely incomplete. It fails to explain how to use the tool or what it returns.

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 0%, and the description provides no information about the four parameters (title, exact, limit, steamAppID). The agent must infer meaning from names 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?

Description 'Game lookup.' conveys a general purpose (lookup) but fails to specify the type of lookup or distinguish from sibling tool 'game'. It is not a tautology but lacks specificity.

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 'game' or other sibling tools. No exclusions or preferred contexts are provided.

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?

While annotations only indicate readOnlyHint=false and destructiveHint=false, the description adds valuable context: the tool is free, doesn't count against quota, and feedback is reviewed daily influencing the roadmap. This goes beyond annotations to inform the agent of non-obvious behaviors.

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

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

The description is concise and well-structured: it starts with the core purpose, then provides usage guidance, and ends with behavioral notes. Every sentence adds value; no filler or 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 the three parameters (two required), a nested object for context, and full schema descriptions, the description covers all necessary aspects for correct usage, including what to include, what to avoid, and rate limits. No additional information is needed.

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

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

The input schema has 100% coverage with descriptions for each parameter. The description adds additional guidance, such as describing the issue in terms of Pipeworx tools/packs and specifying the message format (1-2 sentences, 2000 chars). This enriches the schema but is not essential due to existing schema clarity.

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 is for giving feedback to the Pipeworx team, enumerating specific categories (bug, feature, data_gap, praise) and their definitions. It distinguishes itself from sibling tools like ask_pipeworx by focusing on reporting issues and suggestions.

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

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

The description explicitly states when to use the tool based on the type of feedback (e.g., bug for wrong/stale data, feature for missing tools). It also provides explicit prohibitions (e.g., 'don't paste the end-user's prompt') and notes rate limits (5 per identifier per day), giving clear usage boundaries.

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 indicate read-only, no side effects. Description adds details about monotonicity checking and output format (ranked opportunities with reasoning). No contradictions. Good disclosure beyond annotations.

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

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

Well-structured with purpose first, then mode explanations. Every sentence adds value, though slightly verbose. Could be more concise but efficient overall.

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?

Complex tool with two modes and no output schema. Description explains both modes, output format, and reasoning for cross-event mode. Sufficient for an agent to understand capabilities.

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

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

Schema covers both parameters with descriptions. Description adds operational context: event mode walks child markets, topic mode searches across events and groups them. This adds significant 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?

Description clearly states the tool finds arbitrage opportunities via monotonicity violations, and explains two modes (event and topic). It differentiates from siblings by specifying cross-event mode catches cases single-event mode misses, providing clear purpose.

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

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

Describes when to use event vs topic mode with examples, and explains why topic mode is necessary for cross-event cases. Lacks explicit 'do not use when' statements, but 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?

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds valuable behavioral context: the model (lognormal from FRED + coinpaprika), the process (groups by asset, fetches price history once), and output (ranked by edge magnitude with direction). 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?

The description is dense and front-loaded with the main purpose. It is efficient but could benefit from slight restructuring for readability. Still, every sentence adds value, and it is appropriately sized.

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 explains the return format (top N ranked by edge magnitude with suggested trade direction). Combined with annotations and parameter clarity, it provides a complete understanding of the tool's behavior and output.

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

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

Schema description coverage is 100% with clear parameter descriptions. The description reinforces semantics by referencing 'top N' for limit, 'volume window' for window, and 'minimum edge' for min_edge_pp, adding practical context 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: scanning high-volume Polymarket markets and returning those where Pipeworx data disagrees with market price. It specifies the verb (scan/return), resource (markets), and context (crypto-price bets, lognormal model). It distinguishes itself from siblings like polymarket_arbitrage by targeting opportunity discovery for betting.

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 frames the tool for the 'what should I bet on today' question and outlines the workflow. While it provides clear context for use, it does not explicitly state when not to use or compare with siblings, which would earn a 5.

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

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

Annotations indicate readOnlyHint=true and destructiveHint=false. The description confirms read-only behavior and adds context about scoping and pairing with other tools. 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?

The description is extremely concise with two sentences, front-loading the core purpose. Every sentence adds essential information without waste.

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 (one optional param, read-only, no output schema), the description is complete. It covers purpose, usage, scoping, and relationship to sibling tools.

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

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

The schema has one parameter with description covering 100%. The description adds value by explaining that omitting the key lists all saved keys, which goes beyond the schema's 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 retrieves a value saved via 'remember' or lists all keys if no key argument is provided. It distinguishes itself from sibling tools like 'remember' and 'forget' by naming them explicitly.

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: to look up context stored earlier. It also mentions how to use it (omit key to list all) and scoping to the user's identifier. 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 declare readOnlyHint=true and destructiveHint=false, so the tool is safe. The description adds valuable context: it fans out to three sources (SEC EDGAR, GDELT, USPTO) in parallel, explains accepted date formats, and describes the return structure (structured changes + count + URIs). 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?

The description is concise, fitting in a single paragraph. It front-loads the purpose with a question and example queries. Every sentence adds value: use cases, source fan-out, date format, return structure. Slightly more structured formatting could improve scanability, 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 no output schema and moderate complexity (3 required params, parallel fan-out), the description provides sufficient context: it names the sources, explains parameter formats, and notes the return includes structured changes, count, and citation URIs. This allows an agent to invoke it correctly without additional details.

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 enhances understanding by clarifying that 'since' accepts both ISO dates and relative shorthand like '7d', '30d', '3m', '1y'; that 'type' is currently limited to 'company'; and that 'value' can be a ticker or CIK. These details go beyond the basic 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 'What's new with a company in the last N days/months?' using a specific verb ('fan out') and resource ('company'). It lists several natural-language query examples, distinguishing it from sibling tools like 'deal' or 'entity_profile' which serve different purposes.

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

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

The description explicitly says 'Use when a user asks...' and provides multiple example queries, establishing clear use cases. While it doesn't explicitly state when not to use the tool, the examples and context make it apparent this is for monitoring recent changes, not for historical deep dives or specific entity details.

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 scoping by identifier, persistence for authenticated users, and 24-hour retention for anonymous sessions. Annotations indicate write operation, no destructive hint, but description adds valuable behavioral context beyond annotations.

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

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

Five sentences, front-loaded with purpose. Efficient but slightly verbose; could be condensed without losing meaning.

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?

Covers all necessary aspects: purpose, usage, behavioral traits, and pairing with sibling tools. No output schema needed for this simple store 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 already has 100% coverage with descriptions for both parameters. Description adds examples for key and clarifies value as 'any text', providing slight additional meaning.

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 'save' and resource 'data the agent will need to reuse later'. It distinguishes from sibling tools by naming recall and forget as complementary operations.

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 describes when to use ('when you discover something worth carrying forward') and pairs with recall and forget, providing clear alternatives and context.

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

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

Beyond annotations (readOnlyHint, destructiveHint), description details returned IDs and pipeworx:// citation URIs. No contradictions; adds value on what to expect.

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

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

Four sentences, front-loaded with main purpose. Each sentence contributes value. Slightly wordy but efficient overall.

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 sufficiently describes output types and gives examples. Additional details like citation URIs improve completeness. Could specify output structure more precisely.

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

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

Schema covers both parameters with descriptions. Description adds examples and clarifies input formats (ticker, CIK, name for company; brand/generic for drug), enhancing understanding.

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

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

Description clearly states 'Look up the canonical/official identifier for a company or drug' and provides specific examples of outputs (CIK, ticker, RxCUI, LEI). It differentiates from siblings by noting it replaces '2–3 lookup calls' and precedes other tools needing identifiers.

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 advises using when needing official identifiers and before other tools. Examples illustrate usage. Lacks explicit when-not-to-use but context suffices.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, covering safety and output scope. The description adds no extra behavioral context beyond what annotations provide, which is acceptable but not enhanced.

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 with no extraneous text. It is concise, though it could provide slightly more context about what 'stores' refers to.

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 simplicity of the tool (no parameters, clear purpose, good annotations), the description is mostly complete. The openWorldHint explains the lack of strict output specification.

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?

There are no parameters, so schema coverage is 100%. The description does not need to add parameter info, and it does not.

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 supported stores, using a specific verb and resource. The name itself helps differentiate from siblings like 'deals' or 'games', though no explicit differentiation is provided.

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

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

No guidance is given on when to use this tool versus alternatives. The description does not specify context, 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 already declare readOnlyHint and non-destructive behavior. The description adds transparency by detailing the return verdict types, citation format, and source (SEC EDGAR + XBRL). It does not contradict annotations.

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

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

The description is concise (4 sentences) and front-loaded with the key action. It avoids redundancy and each sentence serves a purpose: definition, usage scenario, domain scope, and return details.

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 low parameter count and no output schema, the description adequately covers the tool's behavior, inputs, and output types. It mentions the supported domain (v1) and the return fields. Minor gap: could clarify output structure more, but sufficient for decision.

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 'claim' is fully described in the schema. The description adds value beyond the schema by explaining the types of claims accepted (natural-language, financial) and providing examples, which helps the agent understand valid inputs.

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 (fact-check, verify, validate) and clearly states the tool's purpose: to confirm/refute natural-language factual claims. It distinguishes itself from siblings by focusing on company-financial claims via a consolidated process.

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 tells when to use it (checking user claims about public US company financials) and mentions it replaces multiple sequential calls. However, it does not explicitly state when not to use it or name alternative sibling tools for non-financial claims.

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