Regulations Gov

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

No annotations provided, so description carries the full burden. It explains the routing and argument filling behavior but does not explicitly state whether the tool is read-only or has any side effects. Adequate for a query tool but lacks explicit safety confirmation.

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 with front-loaded purpose and examples. 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 single-parameter tool without output schema, the description covers purpose, usage scope, and examples. It doesn't specify the return format, but 'returns the result' is sufficient. Minor gap but overall complete.

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

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

Schema has one parameter with 100% coverage. The description adds value by providing example questions, which clarifies the expected input beyond the schema's generic description.

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

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

Description clearly states the tool answers natural language questions by auto-selecting data sources, with specific examples and a list of 300+ sources. Distinguishes from siblings by avoiding manual tool selection.

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

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

Explicitly says when to use (e.g., 'What is X?', 'Look up Y') and that it's for when you don't want to figure out which tool to call. Implicitly distinguishes from sibling tools, but does not explicitly say 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 declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds behavioral context: it resolves the market, classifies the bet, fans out to relevant packs (e.g., crypto+fred+gdelt), and returns evidence packet plus comparison. 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?

The description is a single, well-structured paragraph that front-loads the purpose, explains inputs, process, and returns. It is concise but slightly dense; breaking into bullets could improve readability while maintaining information density.

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 only 2 parameters, no output schema, and no nested objects, the description covers key aspects: input flexibility, classification, fan-out logic, and return contents. It explains the tool's role as a core demo product. However, without an output schema, slightly more detail on the return 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 description coverage is 100%, so baseline is 3. The tool description adds some context about the 'market' parameter (accepts slug, URL, or question text) and hints at the fan-out logic, but does not significantly enhance parameter understanding beyond the schema's own descriptions of 'market' and 'depth'.

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 a Polymarket bet by pulling Pipeworx data, specifying inputs (slug, URL, question text) and outputs (evidence packet, market-vs-model comparison). It distinguishes itself from siblings by being a one-call solution for bet-related context, explicitly claiming agents using it convert better than those discovering packs manually.

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

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

The description provides explicit use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It contrasts with manually discovering packs but lacks explicit exclusions for non-Polymarket bets or when to use alternative siblings like ask_pipeworx.

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 carries full burden. It explains data sources (SEC EDGAR/XBRL, FAERS, FDA, trials) and return format (paired data + citation URIs). Could mention potential limitations like data freshness.

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 dense paragraph front-loads core purpose. Information is packed but clear; minor improvement could be separating return type 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?

No output schema; description explains returns (paired data, citation URIs). Covers when to use, what each type does, and constraints. Adequately complete.

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

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

Schema coverage is 100% with descriptions. Description adds context: values format (tickers vs drug names) and constraints (2–5). Adds 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 'Compare 2–5 companies (or drugs) side by side in one call' and details the specific data pulled for each type, distinguishing it from siblings by noting it replaces 8–15 sequential calls.

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

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

Explicitly states when to use with example phrases like 'compare X and Y' and 'X vs Y'. While it doesn't explicitly state when not to use, the context is clear.

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

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

No annotations are provided, so the description carries the burden. It describes the tool as read-only ('find tools', 'returns the top-N most relevant tools') with no mention of destructive side effects. It does not detail authentication or rate limits, but for a discovery tool, the behavior is transparent enough given its purpose.

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

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

The description is a single paragraph that is front-loaded with purpose and examples. Every sentence adds value: domain list, return behavior, and usage guidance. 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 absence of annotations and output schema, the description is complete enough for a meta-discovery tool. It explains what it does, when to use it, and what it returns. Could mention the algorithm (e.g., semantic search) but not essential.

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

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

Schema coverage is 100% with both parameters described in the schema. The description adds context (e.g., examples for query, default/max for limit) but does not significantly expand beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool's function: discovering tools by describing data or task. It lists many domains (SEC filings, FDA drugs, etc.) and explicitly distinguishes itself from sibling tools by advising 'Call this FIRST when you have many tools available and want to see the option set', indicating it is a meta-tool for exploration.

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 context for use: 'when you need to browse, search, look up, or discover what tools exist' and suggests it as a first step. It does not provide explicit 'when not to use' alternatives, but the context is clear and sufficient 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns SEC filings, fundamentals, patents, news, and LEI, all with citation URIs. It implies read-only behavior ('Get everything'), which is sufficient. Lacks explicit mention of being non-destructive, but context is clear.

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

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

The description is concise (4 sentences) and front-loaded with the main purpose. Every sentence adds value, including use cases, input guidance, and output summary. 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 no output schema, the description adequately explains what is returned (list of items: filings, fundamentals, patents, news, LEI). It also covers parameter constraints and provides examples. Could mention that output is likely structured, but not necessary.

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% (2 parameters, both described). The description adds meaning beyond the schema by explaining that 'type' only supports 'company' and 'value' can be ticker or CIK, and by clarifying that names are not accepted and recommending 'resolve_entity'.

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

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

The description clearly states 'Get everything about a company in one call' and provides specific use cases like 'tell me about X' or 'research Microsoft'. It distinguishes from the sibling tool 'resolve_entity' by explaining that names are not supported.

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

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

The description explicitly states when to use the tool (when a user asks for a company profile) and what inputs to provide (ticker or CIK). It also specifies when not to use it (names not supported) and directs to use 'resolve_entity' first if only a name is available.

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

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

No annotations provided, but the description clearly indicates a destructive action ('delete'), which is appropriate. Could mention reversibility, but 'delete' and 'forget' imply permanent removal.

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 wasted words. Front-loaded purpose then usage guidelines.

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

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

Given the simplicity (1 parameter, no output schema), the description fully covers purpose, usage, and pairing with siblings. 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?

Schema coverage is 100%, so description adds no additional meaning beyond the schema's 'Memory key to delete'. The description says 'by key' but does not elaborate on key format or constraints.

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

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

The description clearly states the verb 'delete' and the resource 'memory by key'. It distinguishes from siblings by mentioning '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 states when to use: 'when context is stale, the task is done, or you want to clear sensitive data'. Also pairs with sibling 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?

With no annotations, the description carries full burden. It indicates a read operation but doesn't disclose potential errors, authentication needs, or side effects. Adequate for a simple get but lacks depth.

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

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

Single sentence, no wasted words. Clearly states purpose and key outputs.

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

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

No output schema exists, so description must cover return values. It lists four fields but omits the comment_id itself and any error handling. Reasonably complete for a simple lookup.

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

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

Schema covers 100% of parameters with descriptions. The tool description does not add meaning beyond the schema; it lists return fields but not parameter details. Baseline 3 applies.

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 action (get single comment by ID) and the resource (comment) with specific return fields (text, submitter, posting date, attachments), distinguishing it from siblings like search_comments and get_docket.

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

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

No guidance on when to use this tool versus alternatives like search_comments or get_docket. No conditions or exclusions stated.

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?

Describes return of full metadata + summary counts, but no details on side effects, rate limits, or authentication. With no annotations, minimal disclosure.

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

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

Single sentence that is front-loaded and concise. 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 simple one-parameter tool with no output schema, it reasonably describes the return data. Slightly vague on what 'full metadata' includes, but 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 has 100% coverage with 'Docket ID' description. The description adds an example format (e.g., 'EPA-HQ-OAR-2021-0317'), providing extra semantic value.

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

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

Clearly states it retrieves single docket detail by docketId with an example. Distinguishes from sibling tools like search_dockets and get_comment.

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?

Implies use when you have a specific docketId, but no explicit guidance on when not to use or alternatives like search_dockets.

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 absent, so description must disclose behavior. It only lists result fields, no mention of pagination, ordering, rate limits, or read-only nature. Minimal behavioral context.

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 concise sentences, front-loaded with purpose. No redundant words. Efficient for a simple listing tool.

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

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

No output schema, so description should detail return structure (array vs object). Also lacks explanation of parameter interactions (e.g., query + docket_id?). For a 5-param tool, this is incomplete.

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 for all 5 parameters. The description adds no parameter-level information beyond the schema, meeting the baseline but not exceeding.

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 specifies resource (documents) and context (docket or query), and lists returned fields. It is clear but does not explicitly state the action (list). Distinguishes from sibling search_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?

Implies two use cases (by docket or by query) but provides no guidance on when to use this vs sibling tools like search_comments. No exclusions or prerequisites mentioned.

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

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

No annotations are provided, so the description carries the full burden. It discloses behavioral traits: rate-limited to 5 per identifier per day, free, doesn't count against tool-call quota, and that the team reads digests daily. 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 well-structured and front-loaded with the purpose. All sentences are meaningful, though slightly verbose. Still, it's clear and organized.

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

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

Given the tool's complexity (3 parameters, one nested object, no output schema), the description is highly complete. It covers purpose, usage, behavioral constraints, parameter details, and processing context. 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?

Schema coverage is 100%, but the description adds significant meaning beyond the schema. For 'type', it clarifies each enum value with specific examples. For 'context', it explains it's optional and provides pack/tool/vertical fields. For 'message', it gives formatting tips ('be specific', '1-2 sentences typical', '2000 chars max').

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 broken, missing, or needed features. It specifies verb ('tell'), resource ('Pipeworx team'), and types of feedback (bug, feature, etc.), which distinguishes it from sibling tools like 'ask_pipeworx' or 'discover_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 when-to-use guidance is given: use for bugs, feature requests, data gaps, or praise. It also states what not to do (don't paste end-user prompt) and provides context on how feedback is used (daily digests, roadmap influence). Rate limit and free nature are mentioned, covering when to use and expectations.

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 behavioral trait of checking price ordering and reports arbitrage. It aligns with annotations (readOnlyHint, not destructive) and adds context about how the tool processes events and what output to expect.

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

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

The description is well-structured with a clear explanation of the concept, mechanism, and output. It is slightly long but every sentence adds value. Could be more concise by removing the example inline with 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 tool's simplicity (1 parameter, no output schema) and sufficient annotations, the description completely covers input, behavior, and output format. It provides enough context for an agent to correctly select and invoke the 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%, providing baseline 3. The description adds meaning by explaining that the event parameter can be a slug or full URL, and describes the required structure of child markets, which is not in 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?

The description clearly states it finds arbitrage opportunities via monotonicity violations in Polymarket events. It explains the logic with an example and distinguishes itself from sibling tools like 'bet_research' and 'polymarket_edges' by focusing on intra-event 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 explains when to use the tool: when an event has multiple 'by date' or 'by threshold' markets. It implicitly tells when not to use (no violations or single market) but does not explicitly name alternatives or conditions to avoid.

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

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

Annotations already indicate read-only, open-world, non-destructive behavior. The description adds valuable context: the underlying model (lognormal from FRED + coinpaprika), the process (groups by asset, fetches price history once), and the output (edge magnitude, trade direction). This goes beyond annotations by detailing the computation and return structure.

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

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

The description is a single paragraph of five sentences, front-loading the main action and purpose. It efficiently conveys the tool's operation, scope, and intended use case. While slightly dense, it contains no filler and earns its length. A minor improvement could be separating the model details for scannability.

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 (multiple data sources, ranking logic, output structure), the description covers essential aspects: data inputs, computation steps, and result format. It explicitly states the limitation to crypto-price bets (V1). Without an output schema, the description adequately informs what is returned (top N, edge, trade direction), making it sufficiently 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?

Input schema covers 100% of parameters with clear descriptions. The description adds minimal extra meaning: e.g., 'Top N edges to return after ranking' confirms limit's role, and 'Polymarket volume window' clarifies window's semantics. This does not significantly enhance understanding beyond the schema, so baseline of 3 is appropriate.

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

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

The description clearly states the tool's action: scanning Polymarket markets and returning those where Pipeworx data disagrees most. It specifies the verb 'scan and return', the resource 'Polymarket markets', and the unique value proposition of ranking by edge magnitude. This distinguishes it from sibling 'polymarket_arbitrage' which likely focuses on cross-platform 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 ties the tool to the 'what should I bet on today' question, providing clear context for when to use it. It notes the current scope (V1 crypto-price bets), implying limitations but not explicitly stating when not to use or alternatives. The usage scenario is clear, but exclusion guidance is absent.

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 covers the tool's behavior: retrieval, listing all keys, scoping, and pairing with remember/forget. It does not contradict any annotations (none provided).

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, well-structured paragraph with no wasted words. It front-loads the core action, then adds use cases, scope, and relationships, making it highly efficient.

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

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

For a simple retrieval tool with one optional parameter and no output schema, the description covers purpose, usage, scoping, and sibling relationships completely. 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?

The description adds context beyond the schema by explaining that omitting the key lists all keys and giving concrete examples (ticker, address, notes). Schema coverage is 100%, so baseline is 3; the extra value 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 identifies the tool as retrieving a value saved via 'remember' or listing all keys when no key is provided. It explicitly names the resource (memory keys) and distinguishes from siblings '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 explains when to use the tool (to look up context stored earlier without re-deriving) and mentions scoping by identifier. It pairs with 'remember' and 'forget' but does not explicitly state when not to use it.

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

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

No annotations provided, but the description discloses parallel fan-out to SEC EDGAR, GDELT, and USPTO, plus return format (structured changes, total_changes count, citation URIs). This adequately informs the agent of the tool's behavior.

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

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

Well-structured with front-loaded purpose and examples. A bit wordy but each piece adds value. Could be slightly tighter, but overall effective.

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

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

Given the simplicity of the tool (3 parameters, no output schema), the description covers all necessary aspects: purpose, parameters, behavior, and return format. No gaps or missing information.

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

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

Schema coverage is 100%, and the description adds significant value by explaining the 'since' parameter format with examples (ISO date and relative shorthand) and suggesting typical usage ('30d' or '1m'). 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?

Clearly states the tool's purpose: retrieving recent changes for a company within a time window. Lists multiple example user queries and distinguishes from siblings by focusing on temporal updates across SEC, GDELT, and USPTO.

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 use-case examples ('what's happening with X?') and indicates it fans out to multiple sources. Lacks explicit when-not-to-use or alternative tool references, but the context is sufficiently 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?

Given no annotations, the description fully discloses scope by identifier, persistence duration (authenticated vs anonymous), and pairing behavior. 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?

Three sentences, front-loaded with purpose, no fluff. Efficient and 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?

Comprehensive for a simple store tool, covering persistence and pairing. Lacks explicit mention of overwrite behavior, but otherwise complete.

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

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

Schema descriptions already cover key and value with examples. Description adds no new parameter-level information beyond schema, but overall context is helpful. Baseline 3 due to 100% schema coverage.

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

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

The description clearly states the tool saves data for reuse later, with specific examples like ticker or preference. It distinguishes from siblings recall and forget by mentioning them explicitly.

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

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

Explicit guidance on when to use ('when you discover something worth carrying forward') and how to pair with recall/forget. Provides concrete examples of use cases.

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

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

No annotations provided, so the description carries full burden. It discloses return types (IDs, citation URIs) and implies read-only behavior. Could add details on edge cases or rate limits, but sufficient for a lookup tool.

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

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

Well-structured, front-loaded purpose, concise examples, and clear usage instruction. Every sentence adds value without unnecessary repetition.

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

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

Given no output schema, the description adequately covers return values (IDs, URIs). It addresses the main use case and provides sufficient detail 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 meaningful context by explaining parameter values with examples (e.g., ticker, CIK, name). This goes beyond the schema's brief 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 resolves entities to canonical identifiers, listing specific ID systems (CIK, ticker, RxCUI, LEI) and providing examples. It distinguishes from siblings by noting it replaces multiple lookup 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 advises using this tool before others requiring official identifiers. Examples and context are provided. However, it does not explicitly list alternatives or when not to use it, though the context implies its primary role.

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

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

No annotations exist, so description carries full burden. It discloses it lists public comments and filters, but does not mention pagination behavior, default page size, rate limits, or any side effects. Minimal transparency beyond basic function.

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 front-loads the purpose and lists key filters. Every word is essential, no redundancy. Ideal conciseness.

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 no output schema and 7 parameters, the description covers purpose and filters but lacks information on output format, default pagination (page_size default?), max results, ordering, or error cases. Adequate but not fully comprehensive.

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 parameters are already documented. The description adds value by providing a concrete example for document_id (e.g., 'EPA-HQ-OAR-2021-0317-0001'), which aids understanding. However, it does not elaborate on meaning beyond the schema.

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

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

The description clearly states the purpose: searching public comments filed on a docket or document, and lists specific filter options (docketId, documentId, free-text, date). It distinguishes from sibling tools like get_comment (single comment) and search_dockets (different resource).

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

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

The description implies usage for searching comments with various filters, but does not explicitly state when to use this tool over siblings like get_comment (for a single comment) or search_dockets (for dockets). No exclusion criteria provided.

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

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

With no annotations, the description carries the full burden. It indicates a read-only search operation and lists return fields, but omits behavioral details such as default ordering, pagination behavior (page_size default is 25 in schema but not mentioned), or what happens if no results are found. The description is adequate but not richly transparent.

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—two sentences covering purpose, filters, and return fields. It is front-loaded with the primary action and avoids unnecessary words or repetition of schema details. Every sentence contributes 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 tool has 7 parameters (none required) and no output schema, the description is fairly complete. It covers what the tool does, what it filters on, and what fields it returns. However, it omits details about pagination defaults and result ordering, which are partially addressed in the schema. The description is sufficient for the complexity level.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by grouping parameters (e.g., 'date range' implying two date fields), but it does not provide significant new meaning beyond the schema. The schema already describes parameters with examples (e.g., 'Agency acronym (e.g., "EPA", "FDA", "DOT")'), so the description adds only marginal contextual grouping.

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

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

The description clearly states the tool's function: 'Search regulatory dockets.' It lists specific filter criteria (query, agency, docket type, date range) and specifies what is returned (title, agency, type, status, document counts). This differentiates it from siblings like 'get_docket' (single docket retrieval) and 'search_comments' (comment search), making the purpose unambiguous.

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

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

The description provides no guidance on when to use this tool versus alternatives such as 'get_docket' or 'list_documents'. It does not mention scenarios where search is appropriate, nor does it specify exclusions or prerequisites. The agent is left to infer usage context from the filter capabilities alone.

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

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

With no annotations, the description carries the full burden. It discloses that the tool returns a verdict (confirmed, approximately_correct, etc.), includes a cited actual value, and mentions it replaces multiple sequential calls. It also notes domain limitations (v1 supports company-financial claims). It could be more explicit about constraints like only US public companies, but it is reasonably transparent.

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 (2 sentences plus additional details) and front-loaded with the primary purpose. Every sentence earns its place, covering what the tool does, when to use it, supported domain, and output format. 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 single-parameter tool with no output schema, the description provides sufficient context: purpose, usage scenarios, input examples, output structure, and domain scope. It is complete enough for an agent to invoke correctly, though it could discuss edge cases or error handling.

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, so the baseline is 3. The description adds value by providing examples of claims and explaining the output structure (verdicts, actual value, citation), which goes beyond the schema's parameter description.

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

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

The description clearly states the tool's purpose: fact-checking natural-language factual claims against authoritative sources, specifically company-financial claims for US public companies. This is a specific verb+resource combination that distinguishes it from siblings like 'ask_pipeworx' or '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?

The description provides clear context for when to use the tool, such as checking the truth of a user's statement and gives example queries. However, it does not explicitly mention when not to use it or suggest alternative tools like 'ask_pipeworx' for non-financial claims.

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