Ecb

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

No annotations are provided, so the description carries full burden. It discloses the routing behavior across 300+ sources and that it returns results, but does not mention potential delays, failure modes, or limitations. For an opaque question-answering tool, more behavioral detail would be helpful.

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

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

The description is front-loaded with the core purpose and usage. It includes examples and a list of sources, which adds useful context. It is slightly wordy but not excessively so; each sentence earns its place.

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

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

Given that there is only one parameter and no output schema, the description adequately covers what the tool does, when to use it, and what kinds of questions are appropriate. It does not detail the return format, but for a natural-language answer, that is understandable and sufficient.

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

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

The input schema has 100% coverage with a single parameter 'question' described as 'Your question or request in natural language'. The description adds significant value by providing concrete examples of acceptable questions, which clarifies the parameter's usage 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: answer natural-language questions by automatically selecting the right data source. It provides a specific verb ('answer') and resource ('Pipeworx routing system'), and distinguishes from siblings like get_data or exchange_rate by positioning itself as a general query router.

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 the tool: when a user asks questions like 'What is X?' or 'Find Z' and you want to avoid picking the specific data source manually. It does not explicitly state when not to use it, but the context of routing across many sources implies it is for general queries.

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

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

Beyond annotations (readOnlyHint, openWorldHint, destructiveHint), the description discloses internal workflow: resolves market, classifies bet type, fans out to relevant packs (with examples like BTC betting crypto+fred+gdelt), and returns evidence packet plus comparison. No contradictions with annotations.

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

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

Single paragraph is dense but not overly long; every sentence contributes value. Could be slightly more structured (e.g., bullet points for use cases) but remains efficient for the content delivered.

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?

In the absence of an output schema, the description effectively communicates return format (evidence packet + comparison). Explains classification and fan-out logic. Minor gap: no mention of error handling for unresolvable markets, but sufficient for typical 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%, but description adds significant meaning: explains three valid formats for 'market' (slug, URL, text) and defines 'depth' values ('quick = 2-3 sources, thorough = full fan-out'). Enhances understanding beyond schema fields.

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 specifies a specific verb-resource combination ('Research a Polymarket bet') and details the input types (slug, URL, text), distinguishing itself from siblings by being the integrated fan-out tool that 'convert better than ones that have to discover the packs themselves.'

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 use cases: 'should I bet on X?', 'what does the data say...', 'is there edge...'. Contrasts with alternative approach of manually discovering packs, providing clear when-to-use and implied 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?

No annotations provided, so description carries full burden. It discloses data sources (SEC EDGAR, FAERS, etc.), return format (paired data + citation URIs), and that it replaces multiple calls. Does not mention side effects, but given read-like behavior, it is adequate.

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

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

Single paragraph that is front-loaded with purpose. Every sentence adds unique value, and there is no redundant or extraneous information.

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

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

For a tool with only 2 parameters and no output schema, the description sufficiently explains inputs, behavior, data sources, and return format. It also contextualizes the tool's efficiency gain.

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

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

Schema coverage is 100%, but description adds value by explaining the meaning of 'type' values (company vs drug) and providing examples of acceptable values (tickers, drug names). This goes beyond the schema 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 verb 'compare' and resource '2–5 companies or drugs'. It differentiates from siblings by noting it replaces 8–15 sequential calls, implying it's a bulk comparison tool versus single-entity tools like entity_profile.

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 user phrases that trigger use ('compare X and Y', 'X vs Y', etc.) and provides context for when to use. No explicit exclusion conditions, but the use cases are well-defined.

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

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

No annotations are provided, so the description must cover behavioral traits. It states the tool returns 'top-N most relevant tools with names + descriptions,' but does not explain how relevance is determined, whether the operation is read-only, or any authentication/rate limit considerations. Basic behavior is covered, but gaps remain.

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 (3-4 sentences) and front-loaded with the primary action. It efficiently lists example domains and concludes with a usage guideline. Every sentence contributes meaningfully; no wasted words.

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

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

Given the tool's simplicity (2 parameters, no output schema), the description is largely complete. It explains what the tool does, when to use it, and how to frame queries. However, it could mention that only tools on this server are returned and lacks detail on output format or pagination. Still, it is sufficient for an agent to understand the tool's role.

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

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

Schema description coverage is 100%, and the description adds value beyond the schema. It provides rich examples for the 'query' parameter (e.g., 'analyze housing market trends') and contextualizes the 'limit' parameter with default and max values. The overall description also offers domain examples that inform parameter 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 tool's purpose: 'Find tools by describing the data or task.' It lists numerous example domains (SEC filings, financials, etc.), distinguishing it from sibling tools which are specific data retrieval tools. The tool is uniquely positioned as a meta-tool for discovery.

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 guidance is provided: 'Use when you need to browse, search, look up, or discover what tools exist for...' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This clearly indicates when to use the tool and implies alternatives (specific tools for direct answers).

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

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

No annotations are provided, so the description carries full burden. It clearly states the output includes 'recent SEC filings, latest revenue/net income/cash position fundamentals, USPTO patents... recent news mentions, and the LEI' and mentions citation URIs. This fully discloses what the tool returns and its read-only nature.

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

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

The description is concise, with only two sentences plus a list of returned data. It is front-loaded with the core purpose, includes usage instructions, and avoids unnecessary details. Every sentence serves a purpose.

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

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

Given the tool's complexity (aggregating multiple data sources) and the absence of an output schema, the description provides a comprehensive overview of what is returned. It covers inputs, outputs, and usage context, making it complete for the agent to understand 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% with existing descriptions, so baseline is 3. The description adds significant value by providing concrete examples ('AAPL', '0000320193') and clarifying that the type parameter only supports 'company'. This enhances 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: 'Get everything about a company in one call.' It provides specific use cases ('tell me about X', 'give me a profile of Acme') and lists the data categories returned (SEC filings, fundamentals, patents, news, LEI). This distinguishes it from sibling tools like resolve_entity by explicitly directing users to use resolve_entity for name resolution.

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: when a user asks for a company profile or when many other tools would be needed. It also provides clear exclusions: names are not supported, and instructs to use resolve_entity first if only a name is available. This gives the agent clear decision 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?

The description discloses the output (time series) and the required parameter format (ISO 4217), but does not mention any side effects, data freshness, rate limits, or other behavioral traits beyond what is implicit in a read operation. With no annotations, this is adequate but not thorough.

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

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

The description is succinct (two sentences), front-loads the core purpose, and contains no redundant information. Every sentence adds value.

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

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

Given the tool has 4 parameters, no output schema, and no annotations, the description covers the main purpose and key parameter (currency), but omits details on return format, error handling, or data range behavior, leaving some gaps for the agent.

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

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

Schema coverage is 100%, so the schema already documents all parameters. The description adds examples of currency codes and implies that frequency defaults to daily, but does not significantly extend understanding of the parameters 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 returns daily EUR exchange rate time series for a given currency, specifying the resource (EUR exchange rate) and output type. It distinguishes from sibling tools like 'hicp_inflation' which deals with inflation indices.

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 are provided; the description does not indicate when to use this tool over alternatives (e.g., 'get_data' or 'hicp_inflation') nor any prerequisites or limitations.

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

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

No annotations provided, so description must disclose behavioral traits. It states 'delete' but doesn't mention irreversibility or any side effects. The action is clear but lacks depth beyond the basic operation.

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

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

Two sentences, no filler. Front-loaded with the action and resource, then usage guidelines. Every sentence earns its place.

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

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

Given the tool's simplicity (one param, no output schema), the description covers purpose, usage, and sibling context. Missing return behavior (e.g., success confirmation) but not critical for such a 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% with one parameter 'key' already described. The description adds only 'by key', which provides minimal extra meaning beyond the schema's 'Memory key to delete'.

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

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

The description clearly states the action ('delete') and the resource ('previously stored memory by key'), and distinguishes it from sibling tools like remember and recall by specifying the context of 'pair with 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 provides when-to-use guidance: 'Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' This is direct and actionable.

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

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

No annotations provided; description only mentions it is a fetch operation without clarifying read-only nature, authentication, error handling, or rate limits. The example is helpful but insufficient for full 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?

Two sentences and an example—concise, front-loaded, no wasted words. Every sentence adds 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?

While the purpose and key concept are covered, the description lacks details on output format, error conditions, and usage limits. Given no annotations and no output schema, it is only moderately complete.

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

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

The description adds value beyond schema by explaining dot-separated keys with wildcards and providing a concrete example. Schema coverage is 100%, so baseline is 3; the extra clarification justifies a 4.

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

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

The description clearly states it fetches generic SDMX data from any ECB flow, with an example. It does not explicitly distinguish from sibling tools like exchange_rate or hicp_inflation, but the generic nature is implied.

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 such as exchange_rate or hicp_inflation. The description lacks when-not-to-use instructions 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 are empty, so the description carries full burden. It discloses the tool returns an annual rate of change and monthly frequency, but does not mention data source, handling of missing data, or any side effects. It is adequate for a simple read-only data retrieval.

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

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

The description is a single sentence that is concise and front-loaded with the main metric. It contains no unnecessary words and is easy to parse.

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 3 optional parameters, no output schema, and no annotations, the description provides essential context (metric, frequency, default country). It could mention date range limitations or output format, but is reasonably complete for a simple data tool.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by stating the default country (U2), which is not in the schema. This provides additional meaning beyond the parameter 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 returns the HICP annual rate of change for a country or euro area with monthly frequency. It specifies the metric, frequency, and geographical scope, and distinguishes it from sibling tools like exchange_rate or get_data.

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

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

The description provides context (country defaults to U2) but does not explicitly guide when to use this tool vs alternatives. The usage is implied by the specific economic indicator, but no exclusions or comparisons are given.

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

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

Annotations are empty, so the description must stand alone. It discloses that the operation is a read-only list with an optional filter, but does not mention pagination, permissions, or return format, which are gaps.

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

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

The description is two concise sentences with no superfluous information. It is front-loaded with the action and resource.

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 parameter, no output schema), the description covers the purpose and filter sufficiently. However, it does not specify what the returned data contains (e.g., flow names, refs) or if there is any limit.

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 describes the filter as 'Case-insensitive substring filter', and the description adds that it applies to 'flow ref or name', providing additional 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 lists ECB SDMX data flows, using a specific verb ('list') and resource ('ECB SDMX data flows'). It is distinct from sibling tools like 'get_data' or 'exchange_rate'.

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 vs. alternatives. There is no mention of prerequisites, typical use cases, or when the optional filter should be applied.

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

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

With no annotations, the description fully handles transparency. It discloses that feedback is read daily, affects roadmap, is rate-limited (5/identifier/day), is free, and doesn't count against quota. This gives the agent clear behavioral expectations.

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

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

The description is slightly longer but each sentence serves a purpose. It front-loads the purpose and then adds guidelines and constraints. Could be tightened, but no extraneous information.

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

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

Given the tool's complexity (nested objects, rates, and usage guidelines), the description covers essential aspects: purpose, when to use, what to include, and constraints. No output schema is needed for a feedback tool, so completeness is adequate.

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

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

Schema coverage is 100%, but the description adds value by explaining enum values (bug, feature, etc.) and providing guidance on message content (be specific, 1-2 sentences). It also clarifies the context object fields, going beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: telling the Pipeworx team about bugs, missing features, data gaps, or praise. It uses a specific verb ('tell') and resource ('Pipeworx team'), and is distinct from siblings like ask_pipeworx or get_data.

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

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

The description provides explicit usage scenarios (bug, feature, data_gap, praise) and advises against pasting end-user prompts. It does not explicitly state when not to use the tool, but the given scenarios are comprehensive enough for 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 indicate readOnlyHint=true and destructiveHint=false. Description adds valuable behavioral context about the two modes, how cross-event mode catches cases missed by single-event mode, and the output format (ranked opportunities with trade 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?

Description is well-structured with clear sections for modes. Slightly verbose but each sentence adds necessary 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 the tool's complexity (two modes, cross-event logic) and absence of output schema, the description adequately covers functionality and output (ranked opportunities with reasoning). Slightly more detail on expected output format could 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?

Schema coverage is 100% and the description explains the meaning of each parameter beyond the schema's brief descriptions, including usage scenarios and 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?

Description clearly states it finds arbitrage opportunities via monotonicity violations. Specifies two modes (event and topic) with concrete examples, distinguishing its functionality from sibling tools.

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

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

Explicit guidance on when to use each mode with examples. Does not explicitly mention when not to use, but the context is clear enough for an agent to decide.

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

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

Annotations declare readOnlyHint=true and openWorldHint=true. Description adds detail: scans top markets, groups by asset, fetches price history once, uses lognormal model from FRED + coinpaprika, returns ranked edges with direction. No contradiction with 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 informative but slightly verbose with implementation details like 'fetches each asset's price history ONCE'. However, it is well-structured, front-loads purpose, and avoids 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?

Despite lacking output schema, description fully conveys what the tool returns (top N ranked by edge magnitude with suggested trade direction). Combined with annotations (read-only, open-world) and clear parameter descriptions, it provides complete context for a non-mutating discovery 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% with clear descriptions for all three params (limit, window, min_edge_pp). Description adds context (e.g., 'V1 covers crypto-price bets') but does not provide additional semantic detail beyond schema. Baseline score of 3 is appropriate.

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

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

Description clearly states the tool scans high-volume Polymarket markets, finds where Pipeworx data disagrees with market price, computes model probability, ranks by edge magnitude, and returns top N with suggested trade direction. It distinguishes from siblings by focusing on edge detection using Pipeworx model, not general market research or 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?

Explicitly states it answers 'what should I bet on today' by discovering opportunities without manual paging. Implies usage for opportunity discovery but no explicit when-not or alternatives, though sibling tools like bet_research or polymarket_arbitrage are natural alternatives.

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

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

No annotations are provided, so the description covers behavioral traits: it is scoped to an identifier (anonymous IP, hashed key, or account), and omitting the key lists all stored keys. It does not contradict any annotations.

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

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

The description is concise and well-structured, with two sentences that are front-loaded and no wasted words.

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

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

While the description is generally complete, it does not specify the return value format (e.g., what type or structure is returned for a single key or the list). Given no output schema, this is a minor gap.

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 a single parameter 'key'. The description adds meaning by explaining that omitting 'key' lists all saved keys and gives examples of content types (ticker, address, notes), which goes beyond the schema.

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

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

The description clearly states the tool retrieves a previously saved value via 'remember' or lists all keys if the key argument is omitted. It uses specific verbs and resources, and distinguishes itself from sibling tools like 'remember' 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?

The description explicitly says when to use the tool: to look up stored context like tickers, addresses, or notes. It also mentions pairing with 'remember' and 'forget', though it does not specify 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?

With no annotations, the description carries full burden. It discloses that the tool fans out to multiple external sources in parallel, returns structured changes plus count and citation URIs, and explains parameter formats. It lacks details on error handling or rate limits, but covers key behavioral aspects.

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

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

The description is concise (4 sentences) and front-loaded with purpose and usage. Every sentence adds value: examples, data sources, parameter guidance, and return format. No redundancy or wasted words.

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

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

For a tool with 3 parameters and no output schema, the description is fairly complete. It covers all parameters, explains the return structure (structured changes, total_changes, URIs), and gives context on data sources. Minor omission: no mention of error cases or time window limits, but overall sufficient.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by clarifying the 'since' parameter accepts relative shorthand with examples ('7d', '30d') and explains that 'value' can be ticker or CIK. This goes beyond the schema's basic 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: retrieving recent changes for a company. It uses specific verbs like 'brief me', 'news on', and explicitly mentions the data sources (SEC, GDELT, USPTO), which 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 provides explicit example user queries and explains when to use the tool. However, it does not mention when NOT to use it or suggest alternative tools, which would enhance clarity further.

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?

Despite no annotations, description fully discloses behavior: key-value storage, identifier scoping, persistence differences for authenticated vs anonymous users, and retention duration.

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, no unnecessary words. Well structured.

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

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

Complete for a simple key-value tool: covers purpose, usage, retention, and pairing with siblings. No output schema needed.

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

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

Schema has 100% description coverage, but description adds examples for key and clarifies value as any text, going beyond 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 verb ('Save'), resource ('data'), and purpose ('reuse later'). It distinguishes from sibling tools 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 describes when to use ('discover something worth carrying forward') and how to pair with recall/forget. Also includes scoping and retention 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?

With no annotations, the description fully discloses what the tool returns (IDs, citation URIs) and that it is a lookup operation. No behavioral traits are hidden.

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, and every sentence adds value. No extraneous information.

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

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

For a tool with two parameters, no output schema, and no annotations, the description is fully adequate. It covers purpose, usage, parameter semantics, and provides examples.

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 explains the 'type' parameter and the 'value' parameter with concrete examples (ticker, CIK, name for company; brand/generic for drug), adding meaningful 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 that the tool looks up canonical identifiers (CIK, ticker, RxCUI, LEI) for companies or drugs. It gives specific examples and distinguishes this 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?

Explicitly advises using this tool before other tools that need official identifiers, and notes that it replaces 2-3 lookup calls. Provides clear when-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?

With no annotations provided, the description fully bears the transparency burden. It discloses the supported domain (company-financial claims), data sources (SEC EDGAR + XBRL), output structure (verdict, structured form, actual value with citation, percent delta), and notes the tool's scope ('v1 supports...').

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 (approximately 110 words) and well-structured: it starts with the core purpose, then usage guidance, then scope, then output. Every sentence adds value without redundancy.

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

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

Given the tool's simplicity (one parameter, no output schema), the description completely covers what the agent needs: purpose, input format, domain constraints, and result structure. It is self-contained and actionable.

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' has a clear schema description, and the tool description adds value by providing examples and clarifying the natural-language format. Since schema coverage is 100%, baseline is 3, but the description enhances 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?

The description uses specific verbs ('fact-check, verify, validate, confirm/refute') and clearly states the resource ('natural-language factual claim against authoritative sources'). It distinguishes itself from sibling tools by explicitly describing its unique function of claim validation.

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 when-to-use guidance ('Use when an agent needs to check whether something a user said is true') and includes example query phrasings. It also notes what the tool replaces, helping the agent understand efficiency gains.

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