Gov Uk Content

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 that it routes to many tools and returns structured answers with citations, providing additional context without contradiction.

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

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

Description is front-loaded with the key instruction and is informative, though slightly verbose. Every sentence adds value, but could be trimmed slightly.

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 a simple schema (1 param, no output schema) and strong annotations, the description fully covers purpose, usage, and examples, making it complete for the agent.

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

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

Only one parameter 'question' with a natural language description. Schema coverage is 100%, so baseline 3. Description doesn't add much beyond the schema but aligns with it.

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 explicitly states it's a router for factual questions with authoritative structured data, lists specific domains (SEC filings, FDA data, etc.), and contrasts with web search. It clearly identifies the tool's purpose.

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

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

Explicitly says 'PREFER OVER WEB SEARCH' for factual questions, provides specific domains and example queries, and indicates when to use it versus alternatives (web search).

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

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

The description details behavioral traits beyond annotations: it resolves the market, classifies the bet, fans out to the right packs, and returns an evidence packet plus market-vs-model comparison. This adds context not covered by readOnlyHint and openWorldHint, and no contradictions exist.

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 core action. It is five sentences long, each adding value, but could be slightly more concise. Still, it remains efficient and clear.

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

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

Given the tool's complexity (multiple input formats, fan-out logic) and the absence of an output schema, the description adequately covers the process and output (evidence packet + comparison). It could mention error handling or output structure, but is largely complete for a research 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%, but the description adds meaning: it explains the 'market' parameter accepts slug, URL, or question text, and specifies 'depth' defaults to thorough with quick using 2-3 sources. This enhances understanding beyond the schema.

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

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

The description clearly states the tool researches Polymarket bets by pulling Pipeworx data in one call. It specifies the verb 'research' and resource 'Polymarket bet', and distinguishes from siblings like 'ask_pipeworx' and 'polymarket_edges' by emphasizing the all-in-one convenience.

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

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

The description explicitly lists use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', or 'is there edge in this bet?'. It also contrasts with alternatives by noting agents that use this tool convert better than those discovering packs themselves.

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

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

Discloses data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/trials for drugs) and mentions return format (paired data + citation URIs). No contradiction with annotations (readOnlyHint=true, openWorldHint=true, destructiveHint=false).

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

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

Description is well-structured with the main purpose front-loaded. Slightly verbose but every sentence adds value; could be slightly more terse but remains 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?

Provides sufficient context despite no output schema: explains return data (paired data + citations) and covers both entity types comprehensively. No gaps that would confuse an agent.

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

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

Schema coverage is 100% (both parameters described). The description adds semantic value by explaining what each type entails and what data is returned, going beyond the schema's enum and array 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 it compares 2-5 companies or drugs side by side, specifying the verb 'compare' and the resources. It distinguishes itself from sibling tools like entity_profile or search by focusing on side-by-side comparison.

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

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

Provides explicit triggers for use (e.g., 'compare X and Y', 'X vs Y') and describes the data pulled for each type. It doesn't explicitly state when not to use, but the context is clear enough for effective agent 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, so the tool's safety profile is clear. The description adds minimal behavioral context beyond what's structured, such as stating it returns content but not specifying format or any special behavior. Thus a score of 3 is appropriate.

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 single-sentence description is succinct and front-loaded. While it could include more details without becoming verbose, it avoids wasted words and effectively communicates the core function.

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 the tool's simplicity, the description lacks completeness. With no output schema, it should clarify what 'content' entails (e.g., full HTML, metadata). It also leaves the meaning of 'base_path' ambiguous, forcing the agent to guess the exact return value.

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

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

Schema description coverage is 0%, so the description must compensate. It mentions 'by base_path' but does not explain its format or constraints (e.g., absolute path required). No additional meaning is added beyond the parameter name.

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 'Content for a gov.uk page by base_path' clearly indicates the tool retrieves content for a given base path. It distinguishes from siblings like 'search' or 'taxons' by focusing on a specific page. However, the verb 'retrieve' is implied rather than explicit.

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 does not mention exclusions or prerequisites, leaving the agent to infer usage context from the schema and name 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?

Description adds that the tool returns top-N most relevant tools with names and descriptions, complementing annotations (readOnlyHint, non-destructive). 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?

Single paragraph, front-loaded with purpose, but includes a long list of examples which slightly reduces conciseness. Still efficient overall.

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 and simple input schema, the description adequately covers use-first tactic, parameter examples, return format, and scope. Sufficient 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?

Both parameters are already described in the schema (100% coverage). The description adds example queries and default limit value, providing extra context beyond schema.

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

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

The description clearly states the tool finds tools based on a natural language query, listing many example domains (SEC filings, financials, etc.). It directly addresses the tool's function and distinguishes it from specific 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 instructs to call this tool first when many tools are available and you need to see the option set, providing clear guidance on when to use it versus alternatives.

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

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

Annotations already declare readOnlyHint and openWorldHint. The description adds value by specifying the exact data returned (SEC filings, fundamentals, patents, news, LEI) and the citation format, providing 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?

The description is a single paragraph that fronts the core purpose and use cases. Every sentence adds value without redundancy, balancing completeness with 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?

Despite no output schema, the description fully explains the return payload: recent filings, fundamentals, patents, news, and LEI, including the pipeworx URI format. This is complete for an agent to understand what the tool returns.

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

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

Despite 100% schema coverage, the description adds crucial semantics: for 'type' it notes future support for person/place; for 'value' it clarifies acceptable formats (ticker or CIK) and explicitly states names are not supported, directing to 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 the tool's purpose: 'Get everything about a company in one call.' It provides specific example queries and lists the data returned, distinguishing it from sibling tools like 'compare_entities' which only compare, not profile.

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

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

Explicitly tells when to use: when a user asks for a company profile or would otherwise need multiple tools. Also provides a clear exclusion: 'Names not supported — use resolve_entity first.' This gives both positive and negative usage guidance.

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

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

Annotations already mark destructiveHint=true; description reinforces destructive nature with 'Delete' and adds context about appropriate deletion scenarios (stale context, sensitive data). 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?

Two concise sentences that are front-loaded: first states action, second gives usage guidance. 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?

For a simple single-parameter tool with no output schema, the description fully covers purpose, when to use, and relationship to sibling tools. 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 baseline is 3. Description does not add additional meaning beyond the schema's description of 'key' as the memory key to delete.

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

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

The description clearly states the tool deletes a memory by key, with a specific verb ('Delete') and resource ('previously stored memory'). It distinguishes itself from siblings by mentioning pairing with 'remember' and 'recall'.

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

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

Provides explicit use cases: when context is stale, task done, or to clear sensitive data. Also suggests pairing with related tools, 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?

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds no behavioral context beyond that, but does not contradict annotations.

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

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

Extremely concise (two words) but at the expense of completeness. Lacks structure and fails to convey essential usage 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?

Missing details on return format, pagination behavior, and filtering. With no output schema, the description should explain what is returned, but it does not.

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

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

Schema description coverage is 0%, and the description does not explain the 'count' and 'start' parameters (likely pagination). The description provides no 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?

Description states 'List organisations' which is a clear verb+resource. However, it does not differentiate from sibling tools like 'search' or 'content' that may also list entities.

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

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

No guidance on when to use this tool versus alternatives (e.g., search, entity_profile). No context about prerequisites or typical 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?

Discloses rate limiting (5 per identifier per day), that it's free and doesn't count against quota, and that the team reads digests daily. Annotations (readOnlyHint=false) imply a write operation, which is consistent and not contradicted.

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 paragraph that front-loads purpose, then provides usage guidelines and important details. Every sentence adds value with no fluff.

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 parameters with nested object and no output schema, description covers purpose, usage, constraints, and team behavior. Minor missing: example of good message, 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 covers parameters with descriptions. Description adds value by explaining the intent of each enum value for 'type' and advising on message specificity, going beyond schema.

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

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

Clearly states the tool collects feedback on broken, missing, or needed items, listing specific types (bug, feature, data_gap, praise) and distinguishing it from siblings.

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 (for bugs, missing features, praise) and provides actionable guidance: describe in terms of tools/packs, don't paste end-user prompt, mentions rate limits and quota exemption.

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=true, openWorldHint=true) are already present. The description adds context: it walks child markets, groups related markets, checks monotonicity, and returns ranked opportunities with reasoning. This is clear and consistent 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 (4 sentences) and well-structured: purpose statement, then two modes clearly demarcated with examples. Every sentence earns its place 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?

The description covers both modes, return type (ranked opportunities with reasoning), and explains the monotonicity concept. There is no output schema, but the description compensates well. It could mention error handling or rate limits, but overall it's sufficiently complete for usage.

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

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

Schema coverage is 100%, but the description adds significant value: for 'event' it says 'walks that event's child markets and checks ordering within it'; for 'topic' it explains the cross-event search and grouping. This goes beyond the schema's basic descriptions.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket by checking for monotonicity violations across related markets.' It specifies two modes (event and topic) and distinguishes the tool from siblings like validate_claim or bet_research.

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

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

The description explicitly explains when to use each mode: event for a single event's child markets, topic for cross-event searches. It provides an example ('Strait of Hormuz traffic returns to normal') and clarifies that cross-event mode catches cases missed by single-event mode.

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

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

Beyond readOnlyHint and destructiveHint annotations, description details the underlying model (lognormal from FRED + coinpaprika), grouping by asset, probability computation, and ranking by edge magnitude. It also mentions return of top N with suggested trade direction, providing full 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?

Description is a single paragraph of ~80 words, efficient and without fluff. Every sentence adds value, though slight restructuring into bullet points could improve scanability.

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

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

Given no output schema, description sufficiently explains return value (top N ranked by edge with trade direction). Covers model, grouping, and intended use, making it complete for a tool with 3 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?

Schema coverage is 100% with clear descriptions for limit, window, and min_edge_pp. The description does not add significant new meaning beyond the schema, so baseline score of 3 is appropriate.

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

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

Description clearly states it scans high-volume Polymarket markets and returns those with biggest disagreement between Pipeworx data and market price, using specific models. This distinguishes it from sibling like 'polymarket_arbitrage' which likely focuses on arbitrage 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?

Description explicitly positions it for the 'what should I bet on today' question, indicating when to use it. However, it does not mention alternatives or when not to use it, leaving room for improvement on exclusion criteria.

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 scoping detail and behavior of omitting key, which are 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?

Two sentences, front-loaded with main action, 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?

Complete for a simple read-only tool with one optional parameter and no output schema. Covers purpose, usage, scoping, and sibling relationships.

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

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

Single parameter 'key' has schema description explaining omission behavior. Description repeats this and adds usage context, adding value beyond schema.

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

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

Description explicitly states 'Retrieve a value previously saved via remember, or list all saved keys', with specific verb and resource. 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?

Describes when to use: 'to look up context the agent stored earlier' with examples. Mentions scoping and pairing with remember/forget. Does not explicitly state when not to use, but 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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: it fans out to three sources in parallel, accepts specific date formats, returns structured changes with a count and citation URIs. 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 a single paragraph that effectively front-loads the purpose, provides example queries, then details mechanics. While it packs useful information, it could be slightly more concise by grouping examples more tightly. Still, it remains 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?

Given there is no output schema, the description adequately covers return values (structured changes, count, URIs). All parameters are explained with examples. The complexity of the tool (fanning out to multiple sources) is well described, making it complete for an AI 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 100%, but the description adds significant value: it clarifies that 'since' accepts ISO dates or relative shorthand with examples ('7d', '30d', '1m', '1y'), explains that 'value' can be a ticker or CIK, and specifies that 'type' is currently limited to 'company'. This goes well beyond the schema.

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

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

The description clearly states the tool's purpose: retrieving recent changes for a company. It provides specific example queries and explicitly distinguishes its scope from other tools by mentioning it fans out to multiple sources (SEC EDGAR, GDELT, USPTO). This makes the purpose highly specific and distinguishable from siblings.

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 clear example use cases ('what's happening with X?', 'any updates on Y?') and explains the tool is for monitoring changes. It does not explicitly mention when NOT to use it or name alternative tools, but the context is strong enough for an agent to infer appropriate usage.

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 read-write and non-destructive behavior. Description adds key context: key-value pair scoped by identifier, persistence differences (authenticated vs anonymous, 24-hour retention). 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?

Three sentences with front-loaded main purpose. No redundant words, efficiently covers purpose, usage, and behavior.

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

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

Covers all essential aspects: purpose, use cases, persistence behavior, related tools. No output schema needed; sufficient for a simple store operation.

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

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

Schema coverage is 100% with examples. Description reinforces by explaining key as identifier and value as any text, adding context about scoping and usage. 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?

Description clearly states the tool saves data for reuse across sessions, with specific examples (resolved ticker, target address, user preference). Explicitly contrasted with sibling tools 'recall' and 'forget', making purpose distinct.

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

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

Provides explicit when-to-use guidance ('when you discover something worth carrying forward') and mentions complementary tools (recall, forget). Lacks explicit when-not-to-use but 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?

Annotations already cover safety (readOnly, not destructive), but description adds valuable behavioral details: returns multiple ID types, pipeworx:// citation URIs, and mentions it replaces 2-3 lookup calls, enhancing transparency.

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

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

Three sentences front-load purpose and action, provide examples, and usage guidance without waste. 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?

Despite no output schema, description covers return values (IDs, citation URIs), usage context, examples, and relation to siblings. Sufficient 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 provides descriptions for both parameters. Description adds concrete examples (Apple→AAPL/CIK, Ozempic→RxCUI) and clarifies acceptable formats for value param, going beyond schema.

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

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

Description uses specific verb 'Look up the canonical/official identifier' and explicitly names the resource types (company/drug) and ID systems (CIK, ticker, RxCUI, LEI), clearly distinguishing from siblings that require these 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 states when to use ('when a user mentions a name and you need...') and when not to (implies before calling other tools), with examples of which identifiers to get, providing clear 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. The description adds no behavioral context beyond 'full search', such as result limits, pagination, or response format. Minimal value beyond annotations.

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

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

The description is only three words, which is under-specification rather than conciseness. It lacks essential details and seems incomplete.

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 4 parameters, no output schema, and siblings like 'search_autocomplete', the description is grossly inadequate. It fails to explain return values, filtering, or pagination, making it hard 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 coverage is 0%, and the description provides no information about parameters (query, count, start, filter_format). The agent cannot infer their purpose or valid values from the 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 'GOV.UK full search.' indicates the tool performs search on GOV.UK, but does not specify what is searched (e.g., pages, documents) or how it differs from 'search_autocomplete'. The purpose is clear at a high level but lacks specificity.

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 'search_autocomplete' or 'content'. There is no mention of use cases, filters, or 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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds no behavioral details beyond what annotations provide, such as how suggestions are generated or any rate limits.

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

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

The description is only two words, which is concise but severely under-specified. It lacks essential context, making it ineffective for guiding 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?

Despite the simplicity of the tool (one parameter, annotations), the description fails to explain the nature of suggestions, result format, or relationship to other tools. Incomplete for reliable use.

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

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

The input schema has one parameter 'query' with 0% schema description coverage. The tool description does not explain what the parameter expects (e.g., prefix, partial text) or any 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 'Autocomplete suggestions' indicates the tool returns suggestions, but it does not specify the domain (e.g., search queries, entities) or how it relates to sibling tools like 'search'. While somewhat clear, it lacks specificity.

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 'search'. No context about complementary usage or exclusions is 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?

Annotations already declare readOnlyHint=true and openWorldHint=true, covering safety profile. Description adds no behavioral details beyond 'tree node', so it meets baseline but offers no extra value.

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

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

Extremely concise (3 words) but at the cost of missing critical information. Not a model of appropriate 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?

Despite having annotations and a single parameter, the description provides no context about output, behavior, or parameter usage. Completely inadequate for effective tool selection.

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 1 parameter (base_path) with 0% description coverage. Description fails to explain the parameter's role, purpose, or expected 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?

Description 'Taxonomy tree node' is a noun phrase restating the tool name, offering vague purpose. It does not specify what action the tool performs, unlike sibling tools with clear verbs (e.g., 'ask', 'search').

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. Missing context about prerequisites or typical 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?

Discloses output structure (verdict, extracted form, actual value with citation, percent delta) and lists possible verdicts. Annotations already indicate non-destructive, read-only behavior, and the description adds domain scope. Could improve by mentioning behavior for out-of-domain claims or error cases, but the openWorldHint suggests inconclusive returns.

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?

Concise and well-structured: one paragraph that front-loads the purpose, then usage, domain, output, and benefit. Every sentence adds value without redundancy.

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

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

Despite lacking an output schema, the description explains the return format and includes key elements like verdict, citation, and delta. It covers capabilities and limitations (v1 supports company-financial claims). Could provide more detail on the extracted structured form, but sufficient for an agent.

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

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

Schema covers the single parameter with description and examples. The tool description reinforces this with additional examples and domain context, adding value beyond the schema. A 5 would require deeper parameter details like claim format variations.

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 with specific verbs (fact-check, verify, validate, confirm/refute) and identifies the resource (natural-language factual claim against authoritative sources). It distinguishes itself from sibling tools like search or bet_research by focusing on claim verification.

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

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

Explicit usage guidance: 'Use when an agent needs to check whether something a user said is true' with example queries. Specifies domain (company-financial claims for US public companies) and explains it replaces multiple sequential calls, providing clear context for when to use this tool.

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