Watchmode

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

Annotations already indicate read-only and open-world behavior. The description adds that the tool routes questions to 2,353 tools and returns structured answers with stable citation URIs, providing important behavioral context beyond 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?

The description is concise (4 sentences) and front-loaded with the key instruction. Every sentence adds value: usage preference, domain list, operational detail, and example queries. No unnecessary 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 simple input schema and no output schema, the description explains the output format and tool behavior adequately. It covers what the tool does, when to use it, and what to expect. For a single-parameter tool, this is fully complete.

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

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

The only parameter 'question' has a schema description 'Your question or request in natural language'. The description greatly expands on this by explaining the kinds of questions that are appropriate and providing examples, making the parameter semantics fully clear.

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: answering factual questions about structured data from authoritative sources, with a strong preference over web search. It lists specific domains (SEC filings, FDA data, etc.) and provides example queries, distinguishing it from sibling tools that are more specialized.

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

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

The description explicitly says 'PREFER OVER WEB SEARCH' and instructs to use for factual questions with typical query phrases like 'what is', 'look up', etc. It gives many examples. However, it does not explicitly state when not to use the tool (e.g., for subjective or non-factual questions), which would make it a 5.

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

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

Annotations indicate readOnlyHint=true and destructiveHint=false, which the description aligns with by describing a non-destructive research operation. The description adds valuable behavioral context, such as fanning out to specific data packs per bet type (crypto, Fed, geopolitical, etc.) and returning an evidence packet. No contradictions with annotations.

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

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

The description is well-structured, front-loaded with the core action, and each sentence adds value. However, it contains some marketing language ('core demo product') that slightly reduces conciseness. Overall, it is efficient for the complexity.

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 is provided, but the description compensates by clearly explaining the return value ('evidence packet plus a simple market-vs-model comparison'). It also covers the internal process (resolve, classify, fan out) and input options, making it complete for a tool with two parameters.

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 meaning by explaining the 'market' parameter accepts slugs, URLs, or question text, and clarifies the 'depth' parameter's default and behavior ('quick' vs 'thorough'). This goes beyond the schema's basic descriptions.

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

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

The description clearly states the tool researches Polymarket bets by pulling Pipeworx data, resolving the market, classifying the bet, and returning evidence with a comparison. It specifies input formats (slug, URL, question text). However, it does not explicitly differentiate from sibling tools like ask_pipeworx or validate_claim, relying on the unique functionality implied.

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

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

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 gives context for when to use the tool but lacks explicit exclusions or comparisons to alternative tools on the server.

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?

Adds rich behavior beyond annotations: specific data sources (SEC EDGAR, FAERS, FDA), number of entities, and return format (paired data + 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?

Efficient front-loaded structure with no wasted words. Covers purpose, usage, and behavior in ~120 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?

No output schema, but description qualitatively describes returns. Covers key aspects for both entity types; could mention ranking/table format explicitly but adequate.

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

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

Schema coverage 100% with clear descriptions; description adds examples and explains what each type pulls, supplementing 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 verb 'compare' with specific entity types (companies/drugs) and examples. Distinguishes from sibling tools by being the only comparison-focused tool.

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

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

Explicitly lists user phrases that trigger use and details what each type retrieves. Implicitly excludes non-comparison tasks; lacks explicit when-not-to-use but strong context.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that it 'returns the top-N most relevant tools with names + descriptions' and explains the query-based matching, adding behavioral context beyond annotations.

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

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

Every sentence adds value. The description is front-loaded with the tool's purpose, then usage guidance, then examples, then strategic instruction. No unnecessary words.

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

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

Given the tool's simplicity and lack of output schema, the description fully covers purpose, usage, return type (top-N tools with names+descriptions), and examples. 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% with good descriptions. The description reinforces parameter meaning with examples for query and mentions default/max limit, adding value beyond the schema.

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

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

The description clearly states the verb+resource: 'Find tools by describing the data or task.' It lists many specific data domains, distinguishing it from sibling tools that are narrow data 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?

Explicitly says 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available.' This provides clear when-to-use guidance and strategic ordering.

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) set a baseline. The description adds value by listing exactly what is returned (SEC filings, fundamentals, patents, news, LEI) and mentions pipeworx:// citation URIs. This goes beyond the annotations by clarifying the output's structure and provenance.

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-organized and front-loaded with the core purpose. Every sentence adds essential information without redundancy. It balances brevity with sufficient detail.

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?

Considering the tool's complexity (multiple data sources) and the absence of an output schema, the description adequately covers return values (SEC filings, fundamentals, patents, news, LEI) and input constraints. It does not address error cases, but for an agent's decision-making it is 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?

Schema description coverage is 100%, so baseline is 3. The description adds meaning by clarifying that type is limited to 'company', with future support planned, and that value must be a ticker or zero-padded CIK (not a name). This aids correct parameter choice beyond 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 states 'Get everything about a company in one call' and provides concrete example queries. It effectively distinguishes itself from alternative tools by listing the breadth of data sources it consolidates (SEC, USPTO, news, GLEIF), making its purpose and scope 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?

Explicit usage guidance is given: 'Use when a user asks ... or you'd otherwise need to call 10+ pack tools'. It also advises using resolve_entity when only a name is available. While it lacks direct 'when not to use' examples, the guidance is strong enough for most scenarios.

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?

Annotation destructiveHint=true aligns with 'delete'. Description adds useful context about clearing sensitive data but lacks details on side effects or reversibility.

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 and usage, no superfluous 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 one-parameter destructive tool without output schema, the description covers purpose, usage context, and companion tools adequately.

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

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

Schema provides full description of the 'key' parameter; description adds no extra semantic value beyond reinforcing 'by key'.

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 explicitly states the verb 'delete' and resource 'memory by key', clearly differentiating from sibling tools like 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 (context stale, task done, clear sensitive data) and mentions pairing with remember and recall.

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

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

While annotations indicate readOnlyHint=true, destructiveHint=false, and openWorldHint=true, the description adds no behavioral context beyond these. It does not describe what the tool actually does or any side effects.

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

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

The description is extremely concise at one word, but lacks structure and completeness. While brevity can be positive, here it is under-specified and fails to convey necessary 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?

With no output schema, the description should explain what the tool returns. It does not mention any response format, list contents, or behavior, leaving the agent completely uninformed.

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

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

There are 0 parameters, and the schema coverage is 100% by default. However, the description ('Genres.') adds no meaning about what is returned or how to interpret the lack of inputs. A baseline of 4 is adjusted down due to absence of useful semantics.

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 is 'Genres.' which is a tautology—it merely restates the tool name without specifying any verb or scope (e.g., list, get). It does not distinguish from siblings like 'languages' or 'list_titles'.

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. The description provides no context about its purpose relative to other tools on the server.

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, but the description adds no behavioral details beyond the name. It does not disclose what the tool returns or any side effects.

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

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

The description is extremely short but under-specifies the tool. Conciseness should not sacrifice informativeness; here it provides no actionable detail.

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 zero parameters and minimal annotations, the description fails to explain what the tool returns or how it behaves. Without an output schema, more description is needed to specify the result format.

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

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

The tool has zero parameters, so the input schema is trivially covered. Per guidelines, 0 parameters earns a baseline of 4, and the description does not need to add parameter semantics.

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 is a single word 'Languages' which is a tautology of the tool name. It does not specify what the tool does (e.g., list, retrieve, or manage languages) and fails to differentiate from siblings like 'genres' or 'networks'.

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. The description lacks context about its purpose or any 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 indicate readOnlyHint=true and openWorldHint=true. The description adds no behavioral context (e.g., pagination, limitations), despite having 11 parameters.

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 brief (2 words) but underspecified; fails to earn its place by providing necessary information. Conciseness should not come at the cost of clarity.

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

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

With 11 parameters, no output schema, and zero parameter descriptions, the description is wholly inadequate to understand the tool's capabilities and usage.

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

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

Schema description coverage is 0% and the description fails to explain any parameter meaning. 'Title catalog' gives no insight into how 'page', 'limit', 'types', etc. work.

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 'Title catalog' vaguely suggests the tool lists titles but lacks specificity. It does not clarify that it is a listing/filtering tool, making it minimally better than the name itself.

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 siblings like 'title_search' or 'title_detail'. The description provides no context for selection.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, indicating a safe read operation. However, the description adds no additional behavioral context such as what data is returned or any limitations. Given the annotations, a minimum viable score of 2 is warranted.

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 extremely short, it fails to be concise because it omits essential information. 'Networks.' is under-specified, not concise.

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 parameters and no output schema, the description should at least state the tool's purpose (e.g., 'List all networks'). It does not, leaving the agent without sufficient context to invoke it correctly.

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

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

The input schema has no parameters, so schema_description_coverage is 100%. Baseline for such cases is 3. The description adds no parameter information, but none is needed.

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 is a single word 'Networks.' which is a tautology of the tool name. It does not specify any verb or resource context, making it impossible to discern what action the tool performs.

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

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

No guidance is provided on when to use this tool versus sibling tools. There is no mention of context, prerequisites, or alternatives.

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

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

The description discloses rate limits (5 per identifier per day), that it is free and doesn't count against tool-call quota, and that feedback affects roadmap. Annotations provide no behavioral hints, so the description fully carries the transparency burden.

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 at four sentences, front-loaded with the purpose, and every sentence adds essential information without redundancy.

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

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

Given the tool's simplicity (3 params, one nested object) and no output schema, the description covers all necessary aspects: purpose, usage, behavioral notes, and parameter guidance.

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 value by advising to describe issues in terms of Pipeworx tools and not paste end-user prompts, which aids correct usage.

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

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

The description clearly states the tool's purpose: to tell the Pipeworx team about bugs, missing features, data gaps, or praise. It distinguishes from siblings by specifying use cases like when a tool returns wrong data or when a desired tool is absent.

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 guidance on when to use the tool (bug, feature, data_gap, praise) and includes context on rate limits and quota. It lacks explicit when-not-to-use instructions but the positive guidance 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 confirm this tool is read-only and non-destructive. The description adds behavioral details: it searches and groups markets, checks monotonicity, and returns ranked opportunities with reasoning. No contradictions with annotations.

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

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

The description is concise yet comprehensive. Each sentence contributes value: first states purpose, then explains modes with examples. No redundancy or fluff. Well-structured and front-loaded.

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

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

Given the tool's complexity (two modes, dynamic results) and the absence of an output schema, the description is thorough. It explains what the tool does, how to use it, and what it returns (ranked opportunities with reasoning). No obvious 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?

Despite 100% schema coverage, the description adds significant meaning by explaining each parameter triggers a different mode (event vs. topic). It provides examples and clarifies the logic behind parameter choice, going well beyond the schema descriptions.

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

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

The description clearly states the tool finds arbitrage opportunities by checking monotonicity violations. It distinguishes between two modes (event and topic) and explains their difference. This specificity differentiates it from sibling tools like bet_research or polymarket_edges.

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 guidance on when to use each mode, including an example illustrating why cross-event mode is necessary for cutoff-dated events. It implies when not to use single-event mode and gives concrete 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?

The description discloses key behavioral details: scanning top markets, grouping by asset, fetching price history once, computing model probability, and ranking by edge. It also notes the tool is V1 covering crypto-price bets, setting expectations. This aligns with annotations (readOnlyHint=true, destructiveHint=false) and adds context beyond them.

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

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

The description is a single paragraph that front-loads the core purpose in the first sentence, then efficiently details the methodology and output. It is slightly longer than necessary but every clause adds value, so it remains concise without being verbose.

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

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

For a tool of moderate complexity with three parameters and no output schema, the description explains the input (limit, window, min_edge), the processing steps (grouping, model computation, ranking), and the output (top N edges with trade direction). It provides sufficient completeness for an agent to understand what to expect, though a brief mention of the exact output structure (e.g., fields in each result) would improve it.

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 parameter descriptions. The description reinforces the semantics by mentioning 'top N edges' (limit), 'highest-volume markets' (window), and 'disagrees most' (min_edge_pp). While it doesn't add new information beyond the schema, it provides useful context that helps an agent understand parameter roles in the overall workflow.

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

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

The description clearly states the tool scans high-volume Polymarket markets and returns those with greatest disagreement between Pipeworx data and market price, using a specific model. It sufficiently distinguishes its purpose from siblings like polymarket_arbitrage by detailing its unique methodology (lognormal model, edge ranking) and intended use case (discovery of betting opportunities).

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

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

The description explicitly frames the tool for the 'what should I bet on today' question, indicating when an agent should use it. However, it does not provide explicit when-not-to-use guidance or compare directly to alternatives like polymarket_arbitrage or bet_research, which would strengthen the score.

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

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

Annotations already indicate readOnly and non-destructive. Description adds context about scoping to identifier (IP, key hash, or account ID), which is beyond annotations.

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

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

Three sentences, front-loaded with main action, no filler. Every sentence adds 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?

Adequate for a simple retrieval tool with no output schema. The description explains return types (single value or list) implicitly and mentions scoping. Lacks error handling but not critical.

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

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

Schema already covers the parameter fully (100% coverage), describing the key and its optionality. Description adds examples of values (ticker, address, notes) and the list behavior, adding some 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?

The description clearly states the verb 'retrieve' and resource 'value saved via remember' or 'list all saved keys'. It distinguishes from siblings remember and forget by naming them directly.

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: 'look up context the agent stored earlier' and pairs with remember and forget, providing clear alternatives for storing and deleting.

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. The description adds value by detailing the parallel fan-out to SEC EDGAR, GDELT, and USPTO, and specifying return fields (structured changes, count, citation URIs). No contradictions with annotations.

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

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

The description is concise: two sentences covering purpose, use cases, sources, parameter details, and output. It front-loads the core question and quickly gets to actionable guidance, with no superfluous words.

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

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

Given the tool's complexity (3 params, no output schema), the description covers what the tool does, which sources it queries, and what it returns. It could mention pagination or result limits, but the openWorldHint mitigates that need. Adequate for the given features.

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% (all params described). The description adds extra meaning for 'since' by showing accepted formats ('2026-04-01', '7d', '30d', '3m', '1y') and recommends '30d' or '1m' for monitoring. This 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: 'What's new with a company in the last N days/months?' and provides specific use-case queries ('what's happening with X?', etc.). It distinguishes from siblings like entity_profile by focusing on recent changes across multiple sources.

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 examples of when to use the tool ('brief me on what happened with Microsoft this quarter') and mentions monitoring. It does not explicitly state when not to use it or offer alternative tools, but the context is clear enough for correct invocation.

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, openWorldHint, and destructiveHint. The description adds no behavioral context beyond the name, failing to disclose what the tool returns or any constraints.

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 brief but lacks substance. Every sentence should add value; this single word is insufficient to guide an AI agent.

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

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

With no parameters, no output schema, and annotations covering safety, the description still fails to clarify the tool's purpose, such as that it likely lists available regions. It feels 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?

There are no parameters, so schema coverage is effectively 100%. The description adds nothing beyond the schema, but a baseline of 4 is appropriate given zero parameters.

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 is a tautology: 'Regions.' It restates the tool name without specifying any verb or action, and does not distinguish it from sibling tools like 'genres' or 'languages'.

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 whether to retrieve a list of supported regions or filter by region.

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

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

Annotations indicate readOnlyHint, openWorldHint, and destructiveHint, but the description adds no behavioral details such as pagination, date filtering behavior, or response structure. It doesn't enrich the safety profile already 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?

While very short, the description is under-specified and lacks essential details, making it insufficient rather than concise. Every sentence should add value, but this single phrase adds minimal value.

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

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

With 8 parameters, no output schema, and no parameter descriptions, the description is severely incomplete. It fails to explain what the tool returns, how to use parameters, or the scope of results.

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

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

Schema description coverage is 0%; the description does not explain the meaning or usage of any of the 8 parameters (limit, types, regions, etc.). It provides no semantic help beyond parameter names.

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 'Recent/upcoming releases.' is vague and does not specify what type of releases (e.g., movies, games) or how it differs from sibling tools like 'recent_changes' or 'title_search'. It lacks a specific verb and resource context.

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

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

No guidance is provided on when to use this tool versus alternatives. There is no context about prerequisites, use cases, or conditions.

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 provide readOnlyHint and destructiveHint; description adds key context: scoped by identifier, persistence details (24 hours for anonymous), and pairing with recall/forget. 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 redundant information, efficiently covers usage, pairing, and persistence.

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

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

Despite no output schema, description fully covers when to use, what is stored (key-value scoped by identifier), persistence boundaries, and pairing with recall/forget.

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% with descriptions and examples; description adds context about scoping and pairing but does not significantly extend 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 'Save data the agent will need to reuse later' with specific verb and resource, and distinguishes from sibling tools 'recall' and 'forget'.

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

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

Explicitly states when to use ('when you discover something worth carrying forward'), pairs with recall/forget, and explains persistence differences between authenticated and anonymous sessions.

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, so safety is clear. Description adds that it returns IDs plus pipeworx:// citation URIs, which is useful but does not elaborate on additional behavioral traits beyond the annotations.

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

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

Single paragraph with clear front-loading of purpose, followed by examples and usage instruction. While slightly lengthy (4 sentences), every sentence adds value.

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

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

Given no output schema, the description explains what IDs are returned and mentions citation URIs, but does not describe the exact return structure. However, for a lookup tool with 2 simple params, it is 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?

Schema coverage is 100% with descriptions for type and value. The description adds context by specifying acceptable inputs (ticker, CIK, name for company; brand/generic for drug) and gives examples, enhancing the schema's meaning.

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

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

Clearly states the tool resolves company/drug names to canonical identifiers (CIK, ticker, RxCUI, LEI) with examples. Distinguishes itself from sibling tools by noting it replaces 2-3 lookup calls and should be used before other tools needing identifiers.

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

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

Explicitly instructs to use before calling other tools that need official identifiers. Provides examples and mentions it consolidates multiple lookups, but does not give explicit when-not-to-use scenarios.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds no behavioral context beyond the label, which is adequate but not improved.

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

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

The description is very short, which is concise, but it may be too vague. It could be improved by adding a verb or clarifying that it returns a list.

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 parameters and no output schema, the description is minimal. It does not explain what the tool returns (e.g., a list of source names) or how it behaves with respect to the open world hint.

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

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

There are zero parameters, and the schema coverage is 100%. Per instructions, the baseline is 4. The description does not need to add parameter details.

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 'Streaming sources/services' gives a general idea of the tool's domain but lacks a verb to specify the action (e.g., list, get). It is clear enough to distinguish from unrelated tools but not from closely related siblings like 'title_sources'.

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 'title_sources' or 'genres'. The description does not mention use cases or exclusions.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. However, the description adds no behavioral context beyond that, such as output format or pagination, which is insufficient given the minimal description.

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

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

The description is extremely short but at the expense of clarity. It lacks detail and structure, making it under-specified rather than appropriately concise.

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 apparent function (fetching title details) and the absence of an output schema, the description fails to indicate what data is returned. It is incomplete for effective agent use.

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

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

Schema description coverage is 0%, and the description provides no information about parameters. Agents are left to guess the purpose of title_id and append_to_response. The description does not compensate for the lack of 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 'Title detail.' is a tautology that merely restates the tool name without specifying a verb or resource. It provides no differentiation from sibling tools like title_search or title_seasons.

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 offers no explicit guidance on when to use this tool versus alternatives. The name implies fetching details for a single title, but there is no direct context or exclusionary language.

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 tool has annotations 'readOnlyHint: true', 'destructiveHint: false', and 'openWorldHint: true', which already indicate safe read-only behavior. The description adds no further behavioral context, but does not contradict the annotations.

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

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

The description is only two words, which is too brief to convey necessary details. While conciseness is valued, this underspecification harms usability, making it insufficient for an agent to understand how to invoke the tool.

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

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

Given the presence of 3 parameters, no output schema, and zero schema descriptions, the description provides almost no useful context for an agent. It fails to explain parameter usage, return format, or behavior, making it inadequate for effective invocation.

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

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

The input schema has 3 parameters (types, search_field, search_value) with 0% description coverage. The description does not explain the role or format of any parameter, leaving the agent to guess their meanings and required values.

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

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

The description 'Search titles' indicates a verb and resource but lacks specificity about the type of search (e.g., fuzzy, exact) and how it differs from sibling tools like 'list_titles' or 'title_detail'. It is clear but not distinctive.

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 such as 'list_titles' or 'title_detail'. The description does not mention any prerequisites or when not to use it, leaving the agent without context for selection.

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

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

Annotations indicate readOnlyHint=true and destructiveHint=false, so the tool is safe. However, the description adds no behavioral context beyond what annotations already provide. It does not explain what 'Seasons' means operationally (e.g., listing all seasons for a title).

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 brief ('Seasons.'), which is under-specified rather than concise. It does not earn its place as it provides no useful information to the agent.

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

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

Given the simple schema and lack of output schema, the description is woefully incomplete. It does not clarify what the function returns or how it behaves, making it insufficient for an agent to use correctly.

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

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

Schema description coverage is 0% for the single parameter 'title_id'. The description fails to add any meaning beyond the schema, leaving the parameter's role completely unclear.

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 'Seasons.' is a tautology, restating the tool name without specifying an action or resource. It does not clarify whether the tool lists, retrieves, or manages seasons, nor does it distinguish it from siblings like 'title_detail' or 'releases'.

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

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

No guidance is provided on when to use this tool vs. alternatives such as 'title_detail' or 'releases'. The description offers no context for appropriate invocation.

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 and non-destructive. Description adds no behavioral context beyond that. Does not explain what streaming sources means or what the output contains.

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 short (two words), but this under-specification sacrifices clarity. Not concise in a helpful way.

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 simple tool with two parameters and no output schema, the description fails to provide essential context about what the tool returns and when to use it. Annotations provide safety info but not functionality.

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 0% description coverage; the description does not explain the parameters 'regions' or 'title_id'. Their purpose must be inferred from names alone.

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

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

Description 'Streaming sources' is vague; it hints at the domain but lacks a verb or explicit action. It doesn't clearly distinguish from siblings like 'sources' or 'title_detail'.

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 vs alternatives. No mention of use cases or exclusions.

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 significant value beyond annotations: it details the return structure (verdict, actual value with citation, percent delta) and mentions that it replaces multiple sequential calls. Annotations already mark it as read-only and non-destructive, and the description aligns with that. 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 with no wasted words. The first sentence nails the purpose, the second gives usage context, and the third provides details and efficiency. Well-structured and front-loaded.

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

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

Given the single parameter, full schema coverage, and annotations, the description covers purpose, usage, limitations, and output. It even explains the efficiency gain. No gaps remain for an agent to understand and invoke the tool correctly.

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

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

Schema coverage is 100% with a good description of the 'claim' parameter. The description reinforces this by providing concrete examples of valid claim formats, adding clarity beyond the schema definition.

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

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

The description clearly states that the tool fact-checks natural-language claims against authoritative sources, with specific verbs (verify, confirm, refute). It provides example queries and distinguishes itself from siblings by being the only claim-validation tool in the list.

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

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

The description explicitly says when to use this tool (e.g., checking if a user's claim is true) and provides example phrasings. It also states the current scope limitation (company-financial claims for US public companies), helping agents understand when not to use it. However, it does not explicitly mention alternatives 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.