Datos Gob Es

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

Annotations provide readOnlyHint=true and destructiveHint=false, indicating safe read operation. The description adds value by revealing that the tool routes to 2,520 tools, fills arguments, and returns structured answers with citation URIs. No contradiction.

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

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

The description is detailed but front-loads the key preference over web search. It contains multiple examples and use cases, which is justified given the broad functionality. However, it could be slightly more concise without losing essential 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 complexity of 2,520 tools and simple input, the description fully covers behavior (routing, argument filling), output (structured answer with citations), and provides ample examples. Despite no output schema, the description sufficiently explains return value.

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?

Only one parameter 'question' with schema description 'Your question or request in natural language'. Schema coverage is 100%, but the description adds examples of valid questions, enhancing understanding of expected input format 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 it is for factual queries about authoritative structured data, listing many domains like SEC filings, FDA data, etc. It distinguishes from web search by explicitly preferring this tool for such queries. The verb 'ask' and resource 'pipeworx' are clear.

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

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

Explicitly says 'PREFER OVER WEB SEARCH' and gives specific when-to-use criteria: for questions about current/historical data, authoritative structured data with citations. It also provides examples of user intents ('what is', 'look up', etc.) and concrete query examples, effectively guiding when to invoke.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds significant behavioral context: the fan-out process, classification into bet types, and the specific data packs queried (e.g., crypto+fred+gdelt). It accurately describes the read-only nature and the complexity of 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 somewhat lengthy but each sentence adds value. It front-loads the core action and then elaborates on the fan-out process and use cases. Minor redundancy could be trimmed, but overall it is well-structured and informative.

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

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

Given no output schema, the description adequately describes the return value ('evidence packet plus a market-vs-model comparison') and the process (resolution, classification, fan-out). It covers the complexity of the tool, though it omits error handling or performance considerations, which is acceptable for this level of detail.

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%. The description adds value by clarifying that the market parameter accepts a slug, URL, or question text, which the schema does not specify. It also notes the default depth ('thorough'). This extra semantic guidance improves parameter 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 clearly states the tool researches Polymarket bets by pulling Pipeworx data, specifying inputs (slug, URL, question text) and output (evidence packet plus comparison). It distinguishes itself from sibling tools like ask_pipeworx and polymarket_edges by positioning as the core demo product for betting 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 lists use cases ('should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'). It does not provide explicit when-not-to-use or alternatives, but the use cases are clear and the tool is positioned as the primary research 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 declare readOnly, openWorld, non-destructive. The description adds substantial context: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), specific metrics pulled, and that it returns paired data with citation URIs. No contradiction.

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

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

The description is a single dense paragraph that covers purpose, usage, data sources, and efficiency. It is concise but could benefit from slight restructuring (e.g., bullet points) for easier scanning.

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 entity types, multiple data sources), the description provides a good overview of what data is returned and where it comes from. Lacks explicit output structure details, but the paired data and citation URIs are mentioned.

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

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

Schema coverage is 100%, baseline 3. Description adds meaning: explains what each 'type' value retrieves, and provides examples for 'values' (tickers/CIKs vs drug names), going beyond the schema's minimal 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 compares 2-5 companies or drugs side by side, with specific examples and the data pulled for each type. It distinguishes from siblings like entity_profile by focusing on multi-entity comparison.

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 (e.g., when user says 'compare X and Y', 'X vs Y', wants rankings). It notes efficiency gains by replacing many agent calls, but doesn't 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 annotations already indicate readOnlyHint=true and destructiveHint=false, but the description adds no behavioral context beyond that. It does not mention response format, data scope, or any other traits.

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 the description is technically concise, it is under-specified and not structured. It consists of two words that add little value, and the brevity undermines clarity rather than enhancing it.

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

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

Despite having a simple parameter and annotations, the description lacks essential details about the tool's purpose, return value, or place among siblings. It is insufficient for a tool called 'dataset' when 'datasets' is also available.

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 one required parameter 'id' with 0% description coverage, yet the description does not explain its meaning, format, or how to obtain it. The description fails to compensate for the missing schema documentation.

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

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

The description 'Single dataset.' is a fragment that restates the tool name without specifying any action. It does not clarify whether the tool fetches, creates, or modifies a dataset, and the sibling tool 'datasets' (plural) suggests this is a singular access, but the description lacks a verb.

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 like 'datasets' (list) or other siblings. There are no preconditions, exclusions, or context cues.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds no behavioral context beyond that. It does not discuss pagination, rate limits, or other traits.

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-word description is short but not informative. It lacks structure and fails to front-load key details. Under-specification outweighs brevity.

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 search tool with 4 parameters and no output schema, the description is insufficient. It does not explain how to use pagination, query format, or theme values, missing critical context.

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 4 parameters with 0% coverage in schema descriptions. The description 'Search datasets.' provides no information about parameters like 'query', 'theme', 'page', or 'page_size', leaving agents without guidance.

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 'Search datasets.' provides a verb and resource but is extremely generic and fails to distinguish from sibling tools like 'dataset' or 'publishers'. It essentially restates the tool name.

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 'dataset', 'publishers', or 'themes'. There is no mention of context 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 show readOnlyHint=true and destructiveHint=false. Description adds that it returns top-N tools with names and descriptions. No additional behavioral details beyond that.

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 clear action, every sentence adds value, no wasted words.

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

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

For a discovery tool with no output schema, description explains what is returned (top-N tools with names+descriptions) and lists example domains, making it complete for its purpose.

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

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

Schema coverage is 100% with descriptions. Description adds context about query being natural language and limit controlling top-N return, adding value 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 tool finds tools by describing data or task, listing many domains. It distinguishes from sibling tools by advising to call it first to see options, not for specific tasks.

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 need to browse, search, look up, or discover what tools exist' and 'Call this FIRST'. Lacks explicit when-not, but implication is clear.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true. Description adds that results include various data types with citation URIs, and explains input format. No contradiction.

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

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

Six sentences, front-loaded purpose and usage, efficient with no redundant 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 complexity (aggregates multiple sources) and no output schema, description lists returned data types and input constraints adequately. Could mention error scenarios but acceptable.

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

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

Schema coverage is 100%. Description adds examples for ticker and CIK, clarifies that names are unsupported, and explains the type enum. Provides value 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?

Description clearly states 'Get everything about a company in one call' and lists specific data returned, distinguishing it from specialized sibling tools by noting it replaces 10+ calls.

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

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

Explicitly provides example user queries ('tell me about X', etc.) and tells when not to use (names not supported, use resolve_entity first).

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 destructiveHint=true, so description carries less burden. The description adds context about appropriate usage but no further behavioral details (e.g., error handling for missing keys). Adequate but not rich.

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

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

Two sentences, front-loaded with action, then usage guidelines and sibling pairing. No wasted words or redundancy.

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

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

For a simple tool with one parameter and no output schema, the description is complete: it covers purpose, usage, and pairs with related tools. Annotations cover destructive behavior.

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

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

Schema coverage is 100%, with the key parameter fully described in the schema. The description does not add extra meaning beyond 'memory key to delete', so baseline 3 is appropriate.

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

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

The description clearly states 'Delete a previously stored memory by key', specifying the verb (delete), resource (memory), and parameter (key). It distinguishes from siblings by naming '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 'Use when context is stale, the task is done, or you want to clear sensitive data', providing when-to-use conditions. It also pairs with siblings, but does not explicitly state when not to use.

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

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

Annotations indicate a non-destructive write operation. The description adds that feedback is read daily and influences the roadmap, providing behavioral context beyond the annotations. 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?

Four sentences, each serving a distinct purpose: what the tool does, when to use, how to write effectively, and operational notes. No waste, well 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?

Despite no output schema, the description explains the outcome (digests, roadmap impact) and is fully adequate for a feedback tool. The sibling list shows clear differentiation.

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

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

Schema coverage is 100% with descriptions for all parameters. The description adds valuable guidance (avoid pasting prompts, be specific) but does not fundamentally change parameter understanding, elevating it slightly above the baseline.

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 ('Tell', 'Use when') and clearly states the tool is for providing feedback about bugs, missing features, data gaps, or praise. It distinguishes from siblings by focusing on reporting issues rather than asking questions or other operations.

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

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

The description explicitly lists when to use the tool (wrong data, missing tool, praise) and what not to do (paste end-user prompt). It also mentions rate limits and that it's free, providing comprehensive 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 declare safe read-only behavior (readOnlyHint, openWorldHint, no destructiveness). Description adds context about checking monotonicity, grouping markets, and returning ranked opportunities with reasoning, going beyond annotations without contradiction.

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

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

Every sentence serves a purpose: defines purpose, explains modes, provides examples, and justifies cross-event usage. Well-structured with clear separation of modes and no redundant text.

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

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

Given the complexity of two modes and cross-market logic, the description covers key aspects: what it does, modes, inputs, and output (ranked opportunities with reasoning). No output schema, but the description implies the return format sufficiently. Minor gap: no mention of pagination or rate limits, but acceptable for a read-only 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. The description adds value by explaining the two modes in relation to the parameters (event vs. topic), including examples of usage and why cross-event mode is needed, enriching 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 it finds arbitrage opportunities by checking monotonicity violations, and distinguishes two modes (event and topic) with concrete examples. It differentiates from siblings like polymarket_edges by specifying the mechanism.

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 two modes with when to use each (single-event vs. cross-event), including an example of when cross-event is necessary. Lacks explicit 'when not to use' or alternative tool references, but guidance is sufficient for typical use.

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

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

The description adds rich behavioral context beyond annotations: it covers crypto-price bets, uses a lognormal model from FRED and coinpaprika, scans top markets, groups by asset, fetches price history once, computes model probability, and ranks by edge. This fully discloses the internal process without contradicting the readOnlyHint and openWorldHint 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 with no wasted words. It front-loads the core purpose, then explains the model and process efficiently. It could be slightly more structured (e.g., bullet points) but remains concise and clear.

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

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

Given the tool's complexity (scanning, grouping, fetching, computing, ranking) and the absence of an output schema, the description is thorough. It covers the entire workflow, explains the model source, and describes the output (ranked edges with trade direction). No gaps remain.

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 100% schema description coverage, the baseline is 3. The description adds value by contextualizing the parameters (e.g., 'Top N edges to return after ranking') and stating defaults, which reinforces and slightly extends the schema definitions.

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

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

The description clearly specifies the tool's action: scanning high-volume Polymarket markets, identifying where Pipeworx data disagrees with market price, and returning top edges. It is a specific verb-resource combination that distinguishes itself from siblings like polymarket_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 the use case: 'Built for the "what should I bet on today" question — agents/users discover opportunities without paging through hundreds of markets by hand.' This implies when to use it, though it does not explicitly mention alternatives or when not to use.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description's 'List' verb is consistent but adds no new behavioral context. The description does not disclose any limitations like pagination or rate limits, which are not covered by annotations. Since annotations carry the behavioral load, the description meets the baseline.

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

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

The description is extremely concise with three words, front-loading the core purpose. While no words are wasted, the brevity sacrifices useful context that could be added without significant bloat.

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 optional parameter and no output schema, the description should provide more context about what is listed (e.g., publisher names, IDs) and how the limit parameter affects results. The current description leaves the agent with insufficient information to understand the tool's 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?

The description does not mention the single parameter 'limit' (number). With schema description coverage at 0%, the description should compensate but fails to explain how limit affects the listing. This omission reduces the tool's usability for parameter selection.

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 publishers' clearly states the verb (list) and resource (publishers). While it does not explicitly differentiate from sibling tools like 'datasets' or 'entity_profile', the resource name is distinct enough. However, the description lacks any elaboration on what constitutes a publisher, which could enhance clarity.

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 vs alternatives. It does not mention any prerequisites, context, or when not to use it. Sibling tools like 'datasets' or 'entity_profile' could overlap, but the description gives no hints for disambiguation.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds value by specifying scope ('anonymous IP, BYO key hash, or account ID') and the listing functionality when key is omitted. 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?

Four sentences each add distinct value: retrieval action, listing behavior, use cases, scoping, and pairing with siblings. Could be slightly more concise but 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?

Despite no output schema, the description implies return values (the saved value or list of keys). With 1 optional parameter and clear scope, the description is complete for agent invocation.

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

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

Schema coverage is 100% with a clear description for the key parameter. Description adds crucial context: 'omit to list all keys' which clarifies optionality beyond the schema's 'required: []'.

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

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

Description uses specific verb+resource ('Retrieve a value previously saved via remember') and clearly distinguishes from sibling tools (remember, forget) by naming them. The 'or list all saved keys' clause covers the optional key behavior.

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 use case ('look up context the agent stored earlier') and pairs with remember/forget. Missing explicit when-not-to-use, but the context of retrieving stored data 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?

The description details behavior beyond annotations: fans out to multiple sources, accepts ISO or relative date formats, returns structured changes with count and URIs. Annotations (readOnlyHint etc.) are consistent.

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 (a single paragraph) and well-structured: purpose first, then example queries, then sources, then parameter format, then return info. 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?

Despite no output schema, the description specifies return structure (structured changes, total_changes count, URIs). It covers inputs, sources, and outputs sufficiently for an agent to use the tool confidently.

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. The description adds no new parameter-level information beyond what the schema already provides, so 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?

The description clearly states the tool's purpose: 'What's new with a company in the last N days/months?' with explicit example queries and sources (SEC EDGAR, GDELT, USPTO). It distinguishes itself from siblings like 'entity_profile' by focusing on changes over time.

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: 'when a user asks...' with multiple example phrasings. It also indicates the tool is for monitoring changes, implicitly distinguishing from snapshot tools.

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

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

Annotations indicate a write operation (readOnlyHint=false) and non-destructive (destructiveHint=false). The description adds valuable context: key-value storage scoped by identifier, persistence differences (24-hour for anonymous, persistent for authenticated). No contradictions.

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

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

The description is concise and well-structured: first sentence states purpose, second provides usage context, third explains scope, fourth suggests companion tools. No redundant information; 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?

While the description covers purpose, usage, and behavioral details (persistence), it does not mention what the tool returns (e.g., confirmation message) or handle errors (e.g., key already exists). Since there is no output schema, this gap leaves the agent uncertain about the response.

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 covers all parameters with descriptions (100% coverage). Schema already provides examples for key and explains value as 'any text'. Description adds the concept of key-value pairs but does not significantly enhance understanding 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 tool's purpose: saving data for reuse across conversations or sessions. It provides concrete examples (resolved ticker, target address, user preference) and explicitly 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?

The description gives explicit guidance on when to use ('when you discover something worth carrying forward') and explains scope differences for authenticated vs. anonymous sessions. It also directs users to pair with recall and forget, providing clear 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 (readOnlyHint, openWorldHint, destructiveHint) are consistent with description. The description adds value by disclosing return format (multiple ID types and pipeworx:// citation URIs) and hinting at multi-lookup behavior, but could further detail error handling or rate limits.

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

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

The description is concise (3 sentences) with the core action front-loaded. Every sentence provides distinct value: what it does, when to use, what it returns. No redundancy or filler.

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 sufficiently describes return values (IDs and URIs) and connects the tool's purpose to downstream usage. Given schema richness and clear usage context, it is fully complete for an agent to 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%, but the description adds significant value by explaining accepted input formats for each entity type (ticker, CIK, name for companies; brand/generic for drugs) and giving examples, which go beyond the schema's terse 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 uses specific verbs ('Look up the canonical/official identifier') and specifies the resource types ('company or drug'), clearly distinguishing this tool from siblings by naming exact identifier systems (CIK, ticker, RxCUI, LEI) and giving concrete examples like 'Apple' → AAPL / CIK.

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

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

The description explicitly states when to use the tool ('Use when a user mentions a name and you need the CIK...') and provides a directive to use it before other tools that need official identifiers, along with a note that it replaces 2-3 lookup calls, making alternatives clear.

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

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

Annotations already declare readOnlyHint and openWorldHint. The description adds no behavioral details like pagination or ordering, providing no additional 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?

Extremely concise (two words) but at the cost of missing essential information. Not front-loaded with key details.

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

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

Given the lack of output schema and only one optional parameter, the description is inadequate. It does not explain what themes are or what the tool returns.

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

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

Schema coverage is 0% and description does not mention the 'limit' parameter, offering no explanation of its purpose or 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 states the verb 'List' and resource 'themes', but lacks context on what themes are. It is not a tautology, but fails to differentiate from sibling tools like 'datasets' or 'publishers'.

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 mention of prerequisites, filtering, 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?

Discloses source (SEC EDGAR + XBRL), return verdict types, structured form, citations, and percent delta. Also mentions it replaces 4-6 sequential calls. Annotations confirm read-only, non-destructive, and open-world. 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 concise yet comprehensive: starts with action, then usage, then limitations, then return values. Every sentence adds value; no redundancy. Well-structured for agent consumption.

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

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

For a single-parameter tool with no output schema, the description fully explains behavior, return values, and supported domain. Annotations cover read-only and non-destructive aspects. No 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?

Single parameter 'claim' is well-described in both schema and description. Description adds context about supported claim types and examples, reinforcing schema. Schema coverage is 100%, so the description adds extra clarity on domain.

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

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

Clearly states the action (fact-check, verify, validate) and the input type (natural-language factual claim). Provides specific examples of when to use and the supported domain (company-financial claims), distinguishing it from siblings. No ambiguity.

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

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

Explicitly describes when to use the tool (e.g., 'Is it true that…?') and sets boundaries (v1 supports only company-financial claims). Does not explicitly name alternatives but the context is clear. Replaces multiple sequential calls, which guides usage.

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