Iso Codes

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

Annotations declare readOnlyHint and destructiveHint, and the description adds context about routing, argument filling, and returning structured answers with citation URIs. This aligns well, though the exact error handling or routing failure behavior is not described.

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 key preference for this tool over web search. It is somewhat lengthy due to examples, but every sentence adds value for such a broad tool.

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

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

The description explains the tool's high-level operation, return format with citations, and usage scope. Given the tool's complexity and lack of output schema, it is sufficiently complete, though it could mention limitations or fallback 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?

With only one parameter (question) and 100% schema coverage, the description adds value by narrowing the expected input to factual topics from specific domains. This compensates for the lack of enum constraints.

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 appropriate specialized tools among thousands, providing structured answers with citations. It explicitly contrasts with web search and lists specific domains, distinguishing it from siblings 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?

The description gives explicit guidance to 'PREFER OVER WEB SEARCH' and provides a comprehensive list of domains and example queries. It also lists trigger phrases like 'what is' and 'look up', making it clear when to use 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?

The description fully discloses the tool's behavior: it resolves the market, classifies the bet type, fans out to relevant data packs, and returns an evidence packet with model comparison. This goes beyond the annotations (readOnlyHint, openWorldHint) without contradiction, and it explains internal steps without jargon.

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 is informative but could be slightly more structured (e.g., bullet points for inputs/outputs). However, it is front-loaded with the purpose and avoids redundant details, making it effective for an AI agent.

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 (no output schema, multiple parameter types, and fan-out behavior), the description covers all essential aspects: input formats, processing steps, output contents, and default behavior. It is complete enough for an agent to understand and use the tool correctly.

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

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

The description adds meaning beyond the input schema by explaining that the 'market' parameter can accept a slug, URL, or question text, and that the 'depth' parameter defaults to 'thorough' and specifies that 'quick' uses 2-3 sources while 'thorough' does full fan-out. This contextual information is not present in the schema descriptions.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the resource (Polymarket bet) and the action (research by pulling data), and distinguishes it from siblings like ask_pipeworx and polymarket_edges by emphasizing it is a one-call specialized solution.

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 lists use cases: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?".' It also positions itself as the core demo product, implying it should be used before other tools for bet research.

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, non-destructive behavior. The description adds value by disclosing specific data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), the return format (paired data + citation URIs), and efficiency benefit (replaces 8-15 sequential calls). 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 fairly concise and front-loaded with purpose. However, it includes a list of example user triggers that could be shortened. Overall, it is efficient without being overly verbose.

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

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

Given the tool's complexity (2 parameters, no output schema), the description covers purpose, usage triggers, data sources, and return format. No gaps are apparent; it is complete for an agent to select and invoke correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds no extra parameter details beyond the schema; it only repeats the examples already in the schema. No additional meaning 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 clearly states the tool compares 2-5 companies or drugs side by side. It specifies the verb 'compare', the resources (companies/drugs), and the scope (side-by-side, up to 5). It distinguishes from siblings by noting it replaces 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?

The description provides explicit usage triggers ('compare X and Y', 'X vs Y', etc.) and details behavior for company vs drug. However, it lacks guidance on when not to use it or alternative sibling tools for related tasks.

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 description does not add behavioral context. It does not mention what happens on no match, whether results are filtered, or any other behavioral traits 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?

The description is extremely concise at 7 words, front-loading the key purpose. While it could be slightly expanded for clarity, it avoids unnecessary verbosity and is appropriate for a simple lookup tool.

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

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

With no output schema, the description fails to explain what the tool returns (e.g., country details, codes, capital). This leaves the agent uncertain about the output structure, making it incomplete for practical 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?

The description explicitly lists the accepted input types (name, alpha-2, alpha-3, numeric) which adds meaning beyond the schema's bare 'string' type. This helps the agent understand what to provide, compensating for the 0% schema description coverage.

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

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

The description clearly states the tool's function: lookup a country by name, alpha-2, alpha-3, or numeric code. It is specific and distinguishes from sibling tools like 'currency' or 'language', though it does not explicitly differentiate.

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, no context about input preferences, and no exclusions or prerequisites. The description is purely declarative.

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 description only adds that queries are by name/code/numeric. It does not contradict annotations and provides minimal additional behavioral context beyond the safety profile.

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, concise sentence that is front-loaded and contains no extraneous information. Every word serves a purpose.

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

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

Given the absence of an output schema, the description should explain what the tool returns (e.g., currency details, list of matches). It does not, leaving the agent uncertain about the response format. Also lacks details on error handling or result ordering.

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 description adds context that the 'query' parameter can be a name, code, or numeric identifier, even though schema coverage is 0%. This clarifies the expected input format but lacks specifics like case sensitivity or partial matching.

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 indicates the tool retrieves currency information based on name, code, or numeric identifier. It distinguishes itself from sibling tools like 'country' and 'language' by specifying the resource type, but lacks an explicit verb (e.g., 'get' or 'lookup').

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

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

The description provides no guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or limitations. The agent is left to infer usage from 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 declare readOnlyHint as true; description adds that it returns 'top-N most relevant tools with names and descriptions'. No contradictions. Could mention more about side effects (none).

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. Slightly long but every sentence adds value. Could benefit from bullet points for 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?

Given no output schema, description clarifies return format (names+descriptions). Sufficient for a discovery tool with rich annotations. Could mention ordering or relevance criteria.

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

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

Schema covers 100% of parameters with descriptions. Description adds natural language examples for the query parameter (e.g., 'analyze housing market trends') and explains the limit default, enhancing schema info.

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

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

The description states the tool's purpose as 'Find tools by describing the data or task' and lists specific domains (SEC filings, FDA drugs, etc.), distinguishing it from sibling tools like 'bet_research' or 'validate_claim'.

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 instructs 'Call this FIRST when you have many tools available' and provides examples of when to use it, effectively differentiating from 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 safety profile is clear. Description adds value by detailing return content (SEC filings, revenue, patents, news, LEI) and citation format (pipeworx:// URIs).

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?

Only 4 sentences with no waste: front-loaded purpose, usage examples, output summary, parameter clarification. Every sentence earns its place.

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

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

No output schema, but description explains return values sufficiently. Lacks details on pagination or result limits, but overall complete for a data 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 coverage is 100%, so baseline 3. Description adds meaning by explaining 'type' limitation (only company) and 'value' alternatives (ticker/CIK, not names; suggests 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?

Description clearly states 'Get everything about a company in one call', includes specific verb and resource, and distinguishes from sibling 'resolve_entity' by noting names are not supported.

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 lists when to use ('when user asks tell me about X...') and provides alternative ('use resolve_entity first if you only have a name').

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?

Description confirms destructive behavior (delete), aligning with destructiveHint=true. Adds context on appropriate use cases, going 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?

Two sentences, no extraneous information. Every sentence adds value, clear and front-loaded.

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

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

For a simple one-parameter delete tool, description fully covers purpose, usage, and behavior. No missing information.

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

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

Schema covers the single parameter 'key' with a clear description. Description does not add additional semantic value beyond schema, achieving baseline for high schema coverage.

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

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

Clearly states verb (delete) and resource (memory by key). Distinguishes from siblings 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?

Explicitly says when to use: stale context, task done, clear sensitive data. Mentions pairing with remember and recall, providing alternative 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?

The description adds no behavioral details beyond what annotations already provide (readOnlyHint, openWorldHint). No mention of result format, ambiguity handling, or error behavior, which would be useful given no output schema.

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 concise sentence with no wasted words. It front-loads the core purpose and is appropriately sized for a simple lookup tool.

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

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

Given the tool's simplicity, the description covers the basic purpose and input. However, it lacks any indication of output (e.g., returns language object, list, or matches). Annotations partially compensate, but a short note on return behavior would improve 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?

With 0% schema description coverage, the description compensates by specifying valid input types: name, 2-letter code, or 3-letter code. This tells the agent what kinds of strings are accepted, adding significant value beyond the bare 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 'Language by name / 2-letter / 3-letter code' clearly indicates the tool resolves language names or codes. It uses specific verb implied (lookup) and specifies resource (language). It differentiates from sibling tools like country or currency by mentioning code types, but could be more explicit about the action.

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 other entity lookups. No when/why/when-not context. The agent has no information about alternatives or prerequisites.

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, and destructiveHint=false, signaling safe, dynamic listing. The description adds no additional behavioral context such as pagination, ordering, or response format. 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?

Extremely concise at 5 words, with no unnecessary text. However, it could sacrifice a bit of conciseness to include more useful information.

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

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

Given no output schema, the description should explain what the tool returns (e.g., a list of entities). It does not. The presence of an optional 'filter' parameter is not explained. Incomplete for the agent to fully understand 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 description coverage is 50% (kind has enum-like values, filter has no description). The description vaguely hints that 'kind' selects the type but adds no detail about 'filter' or syntax. With moderate coverage, the description should compensate but 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 'List all of one kind' clearly communicates listing aggregated items, and given sibling tools like 'country' and 'language', it implies this is a batch retrieval tool. However, it does not explicitly state the available kinds, relying on the schema.

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 sibling tools (e.g., country, language). It does not mention that for a single entity one should use the specific tools, nor does it state when the 'filter' parameter should be used.

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, the description reveals that the tool is free, doesn't count against quota, is rate-limited to 5 per identifier per day, and that the team reads digests daily. This adds significant transparency.

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

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

Five sentences, each serving a distinct purpose: purpose, usage, formatting, team action, and rate limits. No wasted words; front-loaded with key information.

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

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

Covers usage, format, and behavioral constraints well. Could explicitly state that no data is returned after submission (just acknowledgment), but this is minor given the tool's nature.

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

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

Schema coverage is 100%, but the description adds valuable guidance on how to format the message (describe in terms of tools/packs, avoid pasting user prompts). This exceeds mere schema repetition.

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

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

The description clearly states the tool's purpose: to report bugs, request features, or give praise. It distinguishes itself from siblings (e.g., ask_pipeworx, discover_tools) by focusing on feedback to the team, not on solving user queries.

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 scenarios: bug, feature/data_gap, praise. Includes instructions on what not to do (don't paste user prompt) and mentions rate limits and quota. No alternatives are listed, but the tool is unique in its role.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds behavioral details: it walks markets, searches, groups, checks monotonicity, and returns ranked opportunities with reasoning. This provides valuable context 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?

The description is well-structured with bullet points for the two modes, making it easy to scan. While it is somewhat lengthy, every sentence adds value and the information is efficiently presented.

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 fully explains what the tool returns: ranked opportunities with suggested trade direction and reasoning. It covers both modes, the logic behind them, and provides examples, leaving no significant 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?

The input schema has 100% description coverage with clear parameter descriptions. The description significantly expands on the schema by explaining the two modes and giving examples of how to use each parameter (e.g., 'event' is a slug or URL, 'topic' is a seed question).

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: finding arbitrage opportunities on Polymarket by checking monotonicity violations. It specifies two modes and provides concrete examples, making it distinct from sibling tools like 'polymarket_edges' and 'bet_research'.

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 explains when to use each mode (event vs topic) and highlights that cross-event mode catches cases missed by single-event mode. It provides clear context for mode selection, though it does not explicitly mention when not to use the 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds significant behavioral context: it scans top markets, groups by asset, fetches price history once, computes model probability, and ranks by edge. It also notes V1 covers crypto-price bets with a lognormal model from FRED and live coinpaprika price, going well 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?

The description is a single paragraph of about 100 words, front-loading the main action and then explaining the process. It is efficient but could be slightly tighter; the note about V1 is useful but might be secondary.

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 3 parameters, no output schema, and the tool's complexity, the description explains the model logic, data sources, and output structure. It lacks mention of error handling or data availability assumptions, but the openWorldHint mitigates this. It is largely complete for an agent to decide 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%, so the schema already describes all 3 parameters. The description mentions default values (limit=10, window='1wk') and ranking by edge, but these are already in the schema or implied. It adds no extra meaning beyond what the schema provides.

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

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

The description clearly states the tool scans high-volume Polymarket markets, finds where Pipeworx data disagrees with market price, and returns top N ranked by edge magnitude with suggested trade direction. It distinguishes itself from siblings like polymarket_arbitrage by focusing on data-model disagreement rather than arbitrage.

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 it's built for the 'what should I bet on today' question and saves agents from paging through markets. While it implies usage context, it does not explicitly mention when not to use or name alternatives among siblings like bet_research or polymarket_arbitrage.

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

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

Annotations already indicate readOnly and non-destructive behavior. Description adds that the operation is scoped to a user identifier and is purely a retrieval, consistent with annotations. No contradictions. Additional context about identifier scoping enhances transparency.

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

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

Four sentences, hierarchical structuring: first sentence defines core action, second gives usage examples, third adds scoping, fourth links to sibling tools. Every sentence adds value with no 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 tool's simplicity, description covers retrieval, listing, scoping, and relationships. Lacks explicit mention of return format or error behavior (e.g., key not found), but these are minor gaps. Annotations compensate for safety concerns.

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

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

Input schema has 100% coverage with a single optional parameter 'key' described as 'Memory key to retrieve (omit to list all keys)'. Description repeats this exactly, adding no new parameter-level information beyond the schema's own description.

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

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

Description clearly states the tool retrieves a value or lists keys, using specific verbs ('Retrieve', 'list') and resource ('saved values'). It explicitly distinguishes from siblings 'remember' and 'forget' by naming them and describing their roles.

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

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

Provides clear usage guidance: 'Use to look up context... without re-deriving it from scratch' and gives concrete examples (ticker, address). Mentions scoping and pairing with remember/forget. Could explicitly state when not to use, but the context is sufficient.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the tool is safe. The description adds valuable behavioral context: it fans out to three sources in parallel, returns structured changes with citation URIs, and explains 'since' parameter formats. 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 efficiently structured with the core purpose first, followed by example queries, data sources, and return details. While somewhat long (5 sentences), each sentence adds value and there is no redundancy. Minor improvement could be trimming examples slightly, but overall it's well-organized.

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 an output schema, the description adequately explains return values (structured changes, total_changes count, URIs). It covers the parallel fan-out behavior and parameter semantics. For a complex tool with multiple data sources, this provides sufficient context for an agent to understand and invoke it correctly.

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

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

Schema description coverage is 100%, so parameters are already documented. The description adds meaning by explaining 'since' formats (ISO vs relative shorthand with examples) and noting that 'value' accepts ticker or CIK. This helps agents format inputs correctly beyond the schema alone.

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

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

The description clearly states the tool's purpose: retrieving recent changes for a company. It provides specific example queries ('what's happening with X?', 'any updates on Y?') and explains it fans out to multiple data sources (SEC, GDELT, USPTO). This effectively distinguishes it from sibling tools like entity_profile 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?

The description explicitly mentions when to use the tool (user asks for updates, changes, news) and provides context for typical monitoring ('30d' or '1m'). While it doesn't explicitly state when not to use or list alternatives, the context is clear enough given the sibling tool set.

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 minimal (readOnlyHint false, destructiveHint false). The description adds critical behavioral details: scoped by identifier, persistence differences for authenticated vs. anonymous sessions, and a 24-hour retention limit.

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, front-loaded with purpose, then usage, then details. Every sentence is informative 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?

For a simple store tool with 2 parameters and no output schema, the description fully explains behavior (persistence, scoping, related tools), making it complete for the agent to use correctly.

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

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

Schema coverage is 100%, but the description adds value by explaining the scoping mechanism ('scoped by your identifier') and providing meaningful examples for the key parameter, which aids in correct usage.

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 the resource 'data', specifying the context 'across conversation or sessions'. It distinguishes from siblings by mentioning 'recall' and 'forget'.

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 when you discover something worth carrying forward' and advises pairing with 'recall' and 'forget', providing clear guidance on when to use this tool versus alternatives.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds that it returns IDs plus pipeworx:// citation URIs and mentions it replaces multiple lookups. This provides additional 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?

Three sentences cover purpose, usage, examples, and output. While efficient, the description could be slightly more concise by removing redundant phrases like 'the ID systems that other tools require as input' which is implied by context.

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

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

Given no output schema, the description adequately describes the output (IDs and citation URIs) and the tool's role in a pipeline. However, it could mention whether multiple ids are returned or the exact structure of the 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% with both parameters described. The description adds examples for values (e.g., 'Apple' → 'AAPL', '0000320193') and context on how to use 'type' and 'value', 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?

The description clearly states the tool's purpose: look up canonical identifiers for companies or drugs, listing specific ID systems (CIK, ticker, RxCUI, LEI). It also provides concrete examples (e.g., Apple → AAPL/CIK, Ozempic → RxCUI) and distinguishes itself from other tools.

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

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

The description explicitly tells when to use this tool: 'Use when a user mentions a name and you need the CIK...' and 'Use this BEFORE calling other tools that need official identifiers.' It also states it replaces 2-3 lookup calls, but does not explicitly mention 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?

The description adds no behavioral context beyond what annotations already provide (readOnlyHint, openWorldHint). It neither clarifies side effects nor contradicts 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?

While extremely short, the description is under-specified to the point of being unhelpful. It lacks essential detail, making conciseness a detriment.

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 one parameter, no output schema, and minimal annotations, the description fails to equip the agent with sufficient context to use the tool correctly.

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

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

The single 'query' parameter has 0% schema coverage and no explanation in the description. The agent receives no clues about expected input format (e.g., script code, name, pattern).

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 'Script (ISO 15924)' merely restates the tool name with a standard reference. It does not specify any verb or action (e.g., lookup, resolve, list), leaving the tool's purpose vague.

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

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

No guidance is provided on when to use this tool versus sibling tools like 'language' or 'country'. There is no mention of use cases or alternatives.

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

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

Annotations already indicate readOnlyHint and destructiveHint, but the description adds no behavioral context beyond that. It does not explain what happens with invalid country codes, data completeness, or return structure.

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, concise and front-loaded. However, it is overly brief and misses opportunities to add value without increasing length significantly.

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 tool with one parameter and no output schema, the description minimally covers the purpose. It does not explain return format, error handling, or provide any usage example, leaving gaps 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 description coverage is 0%, and the description does not elaborate on the 'country_alpha2' parameter (e.g., format, accepted values). The parameter name is self-explanatory but lacks formal validation or examples.

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

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

The description clearly states the tool returns ISO 3166-2 subdivisions for a given country. It is specific to a well-known standard and resource, distinguishing it from sibling tools like 'country' or 'currency'.

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

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, limitations, or context where it should be preferred.

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 declare readOnlyHint=true and destructiveHint=false, which align with a read-only verification tool. The description adds valuable behavioral details: return format (verdict, structured form, citation, delta) and efficiency gains (replaces 4-6 sequential calls). 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 about 120 words, well-organized: purpose, usage, scope, return details. Every sentence is meaningful and concise. It front-loads the core function and efficiently packs additional information.

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

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

Given the single parameter, the description covers purpose, usage, scope, and return format. It lacks details on error handling, authorization, or rate limits, but these are not critical for a simple verification tool. The absence of an output schema is partially offset by the description of the verdict.

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 examples. The description adds context about supported claim types (company-financial) and the return format, which enriches understanding beyond the schema. However, the schema itself is already descriptive, so the added value is moderate.

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 identifies the tool's purpose: fact-checking natural-language claims against authoritative sources. It uses multiple verbs (fact-check, verify, validate, confirm/refute) and specifies the resource (company-financial claims via SEC EDGAR). It distinguishes itself from siblings like 'compare_entities' and 'entity_profile' by focusing on claim verification.

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 context with examples ('Is it true that…?'). It states the current scope (v1 supports company-financial claims) which implicitly defines when not to use. However, it does not mention alternative tools or provide explicit exclusions for out-of-scope claims.

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