Gov Uk Content

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

Annotations already indicate readOnly, idempotent, non-destructive behavior. The description adds context: returns per-model data including score, confidence, signals, and raw_response, plus a combined view. It also explains the default model and API key handling, 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?

Two sentences, no wasted words. The first sentence front-loads the core purpose and output. The second efficiently covers defaults, API key requirement, and return structure. Every sentence earns its place.

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

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

The tool has no output schema, so the description must explain return values, which it does: 'per-model {score, confidence, signals, raw_response} + combined view'. Combined with annotations, this covers safety and behavior. Could be slightly more specific about 'signals' but adequate 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?

All 4 parameters have 100% schema description coverage. The description augments with context: default model 'Workers AI Llama-3.3-70b', example entities, and clarification that _apiKey is only needed for Anthropic. This adds marginal 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 tool probes LLMs for knowledge about a business/product/topic and provides visibility scores. It distinguishes itself from siblings like 'ask_pipeworx' by focusing on AI visibility scoring across multiple models, using specific verbs 'probe' and 'score'.

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 specifies default model (Workers AI) and optional Anthropic probing with a BYO API key. It lists explicit use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. However, it doesn't explicitly contrast with sibling tools or state when not to use it, leaving some room for improvement.

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, idempotent, and non-destructive behavior. The description adds valuable context: routing to many tools, argument filling, and returning structured answers with 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 well-structured, starting with the key preference, then domains, mechanism, usage cues, and examples. Every sentence contributes value without repetition.

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

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

Given no output schema, the description explains the return format (structured answer with citation URIs). Covers purpose, usage, and mechanism sufficiently for a routing tool. Slight lack of error/limitation detail, 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 is 100% with comprehensive parameter descriptions and aliases. The description reinforces that it accepts natural language questions and provides examples, adding context beyond the schema.

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

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

The description explicitly states the tool routes questions to 3,436 tools across 780 sources, returning structured answers with citations. It lists specific domains (SEC filings, FDA drug data, etc.) and differentiates from web search by directing preference for factual queries.

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

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

Provides clear guidance on when to use: for factual questions about real-world entities, events, numbers, and when authoritative structured data is needed. Includes examples of query types and domains. Lacks explicit exclusions but strongly contrasts with 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 goes far beyond annotations, detailing resolver confidence levels, parent event extraction, news fields with fallback mechanisms, safety low-confidence blocks, closed market handling, and spread warnings. It fully discloses behavioral traits like parallel fan-out and blocking on low-confidence matches.

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 long with multiple paragraphs. While densely informative, it could be more concise and structured (e.g., bullet points for classifiers, fan-out examples). The main purpose is front-loaded, but excess detail follows without clear hierarchy.

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, no output schema, and rich annotations, the description covers all necessary context: input formats, behavior of depth parameter, response shape details (market, analysis, evidence), resolver contract, parent events, news fields, and edge cases (closed markets, wide spreads). It is exceptionally 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%, so baseline is 3. The description adds significant value by providing examples for 'market', explaining 'depth' behavior, and detailing 'include_raw' default and size implications. This enhances understanding beyond the schema's concise 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. It gives specific use cases ('should I bet on X', 'what does the data say about Y') and distinguishes from siblings by focusing on a single bet's evidence packet vs. other tools like polymarket_edges or polymarket_arbitrage.

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

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

The description explicitly says 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z"' and provides numerous examples of when to use it. However, it does not explicitly mention when not to use it or directly compare to sibling tools, though the context implies exclusion.

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, openWorldHint, idempotentHint, and destructiveHint. Description adds valuable context: explains data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of fiscal years, result sorting, and citation URIs, far exceeding what annotations provide.

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

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

Description is informative but slightly verbose. It front-loads typical user phrases and uses bullet-like structure, though could be more concise. Still effectively communicates all 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?

Given only 2 parameters and no output schema, the description covers all necessary context: what the tool does, how to use it, what data it retrieves, and how results are presented. 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 adds meaning by providing examples of valid values (tickers/CIKs for company, drug names for drug) and reiterating constraints, earning a 4.

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

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

The description clearly states the tool's purpose: side-by-side comparison of 2-5 companies or drugs. It provides specific example phrases and details about data sources, distinguishing it from sibling tools like entity_profile or 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?

Explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing clear guidance on when to use this tool over alternatives. Also includes examples of typical user queries.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds no further behavioral details (e.g., what 'content' includes, performance, or pagination). It 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?

The description is extremely concise (one short sentence) and front-loaded. Every word earns its place, though it could be slightly more informative without becoming 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?

Despite having an output schema, the description omits context about what 'content' entails (e.g., HTML, metadata, or structured data). For a tool with one parameter and minimal description, the agent lacks sufficient context to fully understand the tool's output.

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 0% schema description coverage, the description should compensate by explaining the base_path parameter's meaning. It only mentions 'by base_path,' adding no semantic value beyond the parameter name. Examples in the schema help slightly, but the description itself is insufficient.

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

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

The description states 'Content for a gov.uk page by base_path,' which clearly indicates the tool retrieves page content using a unique path. It distinguishes from siblings as no other tool has a similar function. However, it lacks an explicit verb like 'get' or 'fetch,' slightly reducing clarity.

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

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

No guidance is provided on when to use this tool versus alternatives, nor any exclusion criteria or context. The description simply states what it does without helping the agent decide when it is appropriate.

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 context beyond annotations: it explains the output format ('names, descriptions, and full input schemas with curated examples') and the readiness for direct calling ('each result is ready to call directly'). This complements the readOnlyHint and idempotentHint annotations by detailing the non-destructive, read-only behavior.

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

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

The description is three sentences long. The first and third are clear and action-oriented. The second sentence lists many domains, which is helpful but somewhat verbose. Overall, it could be more concise by trimming the domain list or using a representative sample.

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

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

Despite lacking an output schema, the description sufficiently explains the return value (top-N tools with schemas). It covers essential aspects: what it does, when to use, and output format. With 6 parameters fully described in the schema, no further details are necessary.

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

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

With 100% schema description coverage, the description adds value by clarifying that the query parameter accepts natural language and by listing aliases ('task, q, description, search'). It also provides examples in the input schema, reinforcing 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?

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It lists specific domains (SEC filings, financials, etc.) and distinguishes itself from sibling tools by focusing on tool discovery rather than data retrieval. The verb 'find' and resource 'tools' are specific and unambiguous.

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

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

The description explicitly advises 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear context for when to use. However, it does not explicitly state when not to use it or name alternative tools for specific tasks.

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 already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive, the description adds rich behavioral context: it fans out across multiple sources (SEC, XBRL, USPTO, news, GLEIF), returns specific fields with URIs, details fallback chains, and mentions known limitations (patents API sunset).

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

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

The description is front-loaded with examples that immediately convey the tool's purpose. Every sentence adds useful information, though the length could be slightly trimmed for conciseness without losing 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?

Given the tool's complexity (multiple data sources, fallback logic, no output schema), the description is remarkably complete: it lists all return components, notes limitations, and provides usage instructions. An agent can accurately set expectations.

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 already thoroughly describes both parameters (type enum, value string). The description adds value with concrete examples ('AAPL', '0000320193') and clarifies the constraint that names are not supported, reinforcing usage beyond the schema.

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

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

The description starts with clear examples ('Tell me about X', 'research Acme') and explicitly states the tool's purpose: 'full cross-source profile of a US public company in ONE parallel call'. It distinguishes itself from chaining single-pack lookups and sibling tools like resolve_entity.

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 advises ALWAYS PREFER this tool over chaining single-pack lookups for holistic views. It also specifies when to use resolve_entity first (if only a name is available), providing clear when-to-use and when-not-to-use guidance.

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

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

Annotations already indicate destructive and idempotent behavior. Description adds 'delete' and 'clear' but does not provide further behavioral details like reversibility or permissions.

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

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

Two concise sentences, front-loaded with the action, 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 tool with one parameter, no output schema, the description completely covers purpose, usage, and relationship to siblings.

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 parameter description 'Memory key to delete'. The description adds no new meaning beyond the schema.

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

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

The description clearly states the verb 'Delete' and the resource 'memory by key', distinguishing it from siblings 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: stale context, task done, or clearing sensitive data. Does not mention when not to use, but provides clear 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?

No annotation contradiction. The description adds behavioral context beyond annotations: it explains the tool fetches a page, extracts title/description/links, and emits standard markdown. Combined with annotations (readOnly, idempotent, non-destructive), this provides 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?

The description is concise (3-4 sentences), front-loaded with the core action, and every sentence adds value. No redundancy or unnecessary 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?

Given no output schema, the description explains the return value as a text blob. It covers the tool's behavior (fetch, extract, format) and use cases, making it sufficient for a simple 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%, so the schema adequately describes parameters. The description does not elaborate on parameters beyond the schema, but it implicitly ties 'url' to the target and 'max_links' is not mentioned. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: generating a production-ready llms.txt file for any URL. It specifies the output format and provides concrete use cases, making it distinct from sibling tools like 'content' or '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?

The description enumerates specific use cases (indexing a client's site, personal project drafting, competitor auditing), giving clear guidance on when to use. It does not explicitly state when not to use or suggest alternatives, but the context is strong enough.

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, openWorldHint, idempotentHint, and destructiveHint. The description adds no additional behavioral context beyond the annotations, but there is 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?

Extremely concise at two words, but it is under-specified for a tool with two parameters. While front-loaded, it could usefully include more detail without sacrificing brevity.

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

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

Despite having an output schema, the description lacks context about pagination, filtering, or the scope of organisations listed. Compared to sibling tools, more detail is needed for effective 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 does not explain the 'count' or 'start' parameters. The description must compensate for low coverage but fails to add any 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?

Description states 'List organisations' which is a specific verb+resource, clearly indicating the tool's purpose. However, it does not distinguish itself from sibling tools like search or content that 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, content). The description provides no context about appropriate scenarios 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 are minimal (readOnlyHint: false, etc.), but the description adds key behavioral traits: rate-limited to 5 per identifier per day, free, doesn't count against quota, and team reads digests daily. 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?

Well-structured and front-loaded with the purpose. Every sentence adds value, though slightly verbose with multiple examples. Could tighten slightly but still effective.

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

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

Given the low complexity (3 parameters, no output schema), the description fully covers purpose, usage, parameter details, constraints, and team practices. No gaps remain for an agent to use this 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 description coverage is 100%, so the baseline is 3. The description adds extra meaning by explaining each type's purpose and the context object's optional fields. Message parameter is described with specificity instructions.

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 starts with a clear verb+resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It explicitly lists the categories (bug, feature, data_gap, praise) and distinguishes this tool from siblings (it's the feedback channel).

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 guidance on when to use each type (e.g., 'Use when a tool returns wrong/stale data (bug)'). Advises what to include ('Describe the issue in terms of Pipeworx tools/packs') and what to avoid ('don't paste the end-user's prompt'). Mentions rate limits and free 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 already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds substantial context: data source (CF analytics-engine), caching behavior (5min-1h), and privacy guarantee (no PII). This goes beyond annotations without contradiction.

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

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

The description is remarkably concise: two sentences plus a bulleted list of use cases. Every sentence adds unique information, and the structure front-loads the core purpose. No redundancy or filler.

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

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

Given the tool's simplicity (one parameter, no output schema), the description fully covers purpose, usage context, parameter semantics, and behavioral traits (caching, privacy). It doesn't need an output schema description because the return values are explicitly mentioned.

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

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

Schema coverage is 100% for the single parameter 'window', with enum values and description. The tool description adds value by explaining the interpretative difference: shorter windows for hot trends, longer for steady-state. It also clarifies the default ('24h'). This supplements but does not replace the schema.

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

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

The description clearly states the tool returns 'top tools, top packs, and total call volume' over a recent window. It uses specific verbs ('returns') and resources ('tools', 'packs', 'call volume'), and distinguishes itself from siblings like search and ask_pipeworx by focusing on trending/usage data.

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

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

The description explicitly enumerates three use cases (discovering hot data sources, confirming canonical choice, checking alignment) and explains window trade-offs. While it doesn't mention when not to use or alternatives, the provided context is clear and useful for decision-making.

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 safe, read-only, idempotent behavior. Description adds extensive context: Jaccard similarity threshold, partition filter, response structure, and how monotonicity checks work. 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?

Front-loaded with the requirement statement. Well-organized with clear sections for modes, semantic anchor, partition filter, and response. Slightly verbose but each sentence adds value.

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

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

Given the tool's complexity (dual modes, constraints, filtering), the description covers behavior, parameters, response structure, and edge cases like placeholder filters. No output schema, so the detailed response explanation is essential and provided.

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 100% description coverage for both parameters. The main description enriches this by explaining usage modes, recommended use, and that full Polymarket URLs are accepted for 'event'.

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 finds arbitrage opportunities on Polymarket using monotonicity violations and partition-sum checks. Distinguishes between event and topic modes, but does not explicitly differentiate from sibling tools like 'polymarket_edges' or 'polymarket_kalshi_spread'.

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 requires one of 'event' or 'topic' and recommends using 'event' for specific markets and 'topic' for cross-event scanning. Explains that call with no args fails, but does not state when not to use or list 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?

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint=false), the description details caching at KV level for 1 hour, market move warnings, and Fed bet exclusion. It explains how edges are computed and that opportunities carry diagnostic info. No contradiction with annotations.

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

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

The description is very long and packed into one paragraph, which reduces conciseness. However, it is well-structured with clear sections: purpose, model families, response, knobs, diagnostics. Front-loaded with purpose.

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

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

Given the tool's complexity (9 params, no output schema), the description thoroughly explains return structure, diagnostics for empty results, and caching behavior. It covers all necessary context for an agent to use 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%, but the description adds value by explaining parameter usage: e.g., min_liquidity and max_spread_pp as 'tradeable-edge filters', and min_partition_leg_kelly for thin partitions. This context goes 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?

Description clearly states it scans Polymarket markets and returns opportunities where Pipeworx data disagrees with market price. It is built for daily betting discovery and distinguishes from siblings like polymarket_arbitrage by its specific function.

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

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

The description provides clear context: 'what should I bet on today' and avoids paging hundreds of markets. While it does not explicitly mention alternatives or when not to use it, the use case is well-defined.

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

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

The description goes well beyond the annotations (readOnlyHint, idempotentHint) by detailing safety fields like compatibility_warning, temporal_alignment, and skipped_cross_type. It explains conditions under which spreads are meaningless, adding valuable context without contradicting 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 long but well-structured with bold headers for TWO MODES, RESPONSE, and SAFETY FIELDS. It is front-loaded with the core purpose. A slight reduction in verbosity could improve conciseness, but every sentence contributes unique information.

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

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

Given the tool's complexity (cross-venue spread with safety fields) and lack of output schema, the description thoroughly covers response fields, compatibility warnings, temporal alignment, and leg matching details. It provides a complete understanding of what the tool returns and when it's meaningful.

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 the description adds marginal value by explaining the two modes and providing examples. It clarifies that kalshi_event_ticker and polymarket_event_slug override topic-mapped sides, enriching the schema information.

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 calculates the cross-venue spread between Kalshi and Polymarket for the same resolving question. It specifies two modes (topic shortcuts and explicit pairings), distinguishing it from similar sibling tools like polymarket_arbitrage.

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

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

It explains when to use the tool (comparing prices across venues) and describes two modes with example inputs. It does not explicitly list when not to use or name alternatives among siblings, but it does hint that pre-mapped topics often yield compatibility warnings, implying not all topics are tradeable.

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, idempotentHint, destructiveHint. Description adds scoping (identifier-based) and pairing guidance, going beyond annotations without contradiction.

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

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

Three sentences, each adding value: primary action, use cases, scoping/pairing. No wasted words, front-loaded with main 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?

Given simplicity (1 optional param, no output schema), description covers behavior, scoping, and relationships with siblings. Complete for agent invocation.

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

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

Schema coverage is 100%, description adds context that omitting key lists all keys, enhancing 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?

Description states 'Retrieve a value previously saved via remember, or list all saved keys' with specific verb and resource. Clearly distinguishes from sibling tools '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?

Explicitly says to use for looking up stored context and pairs with remember/forget. Provides clear context but does not explicitly state when not to use or list 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?

While annotations already declare readOnlyHint, openWorldHint, etc., the description adds valuable behavioral details: fan-out to multiple APIs, fallback from GDELT to GNews on rate limits/5xx, USPTO soft-fail, and return format with citation URIs. No contradiction with annotations.

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

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

The description is a single well-structured paragraph that front-loads common query patterns, then details sources, parameter syntax, and return structure. Every sentence adds value without redundancy.

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

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

Given the complexity (multi-source, fallback, parameter variations) and no output schema, the description adequately covers what the tool does, how to use it, what to expect in return, and when not to use it. It is complete for effective agent invocation.

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

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

Schema coverage is 100% with descriptions, but the description adds significant extra meaning: 'since' accepts ISO date or relative shorthand with examples, 'value' supports both ticker and CIK, 'type' is limited to 'company'. This clarifies usage beyond the schema 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?

The description clearly states the tool provides a 'change feed for a company in the last N days/weeks/months' with specific sources (SEC EDGAR, GDELT/GNews, USPTO) and distinguishes itself from sibling 'entity_profile' which provides a static 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 states when to use this tool versus an alternative: 'Use entity_profile instead when you want the static profile...' and provides query examples like 'what's new with X' to guide 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?

Discloses persistence behavior beyond annotations: scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous sessions. 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, front-loaded with purpose, followed by usage and behavioral context. 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?

Adequately covers purpose, usage, and behavior for a simple store operation. Lacks mention of return value, 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 coverage is 100%, so baseline is 3. Description adds examples but no new semantic info beyond schema descriptions.

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

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

The description clearly states the tool's purpose as saving data for reuse, with specific examples (resolved ticker, target address, etc.). It distinguishes itself from siblings '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 tells when to use the tool ('when you discover something worth carrying forward') and mentions complementary tools ('Pair with recall to retrieve later, forget to delete').

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, idempotentHint, etc. The description adds 'Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups,' providing useful internal behavior 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 well-structured with examples and bullet points, front-loading purpose. While slightly verbose, every sentence adds value. Could be tightened but still 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?

Despite lacking an output schema, the description fully details return values for both entity types including citation URIs. It also explains internal cascading, making the tool's behavior completely understandable.

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

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

Schema coverage is 100%, but the description adds value by explaining return fields (ticker, CIK, RxCUI, URIs) and noting auto-disambiguation for company. It elaborates on input formats beyond the schema's brief descriptions.

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

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

The description starts with example queries and clearly states 'resolve a user-spoken NAME to the canonical/official identifier'. It specifies supported types (company, drug) and their details, making the purpose explicit and distinguishing from sibling tools.

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

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

The description says 'Use FIRST whenever you have a name but need an ID,' providing clear when-to-use guidance. It implies when not to use (if ID already known) but does not explicitly name alternative tools 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, openWorldHint, idempotentHint=true, and destructiveHint=false. The description adds that the tool internally probes each entity with ai_visibility_check, ranks results, and returns a ranked list with score, confidence, and signal density. This adds behavioral context 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?

The description is three sentences: first sentence states purpose, second explains mechanism and usefulness, third specifies output structure. It is front-loaded with the primary action and contains no redundant information.

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

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

The tool has 4 parameters, no output schema, but good annotations. The description covers the output structure (ranked list with score, confidence, signal density) and the internal call to ai_visibility_check. It does not address error cases or what happens if an entity is not found, but given the complexity, it is fairly 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 the baseline is 3. The description adds meaning by stating that the first entity is treated as the 'subject' for narrative and the rest as competitors, which is not in the schema. It also clarifies the models parameter and API key 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 verb 'compare' and the resource 'AI visibility across multiple entities side-by-side'. It specifies the actions: probes, ranks, surfaces most/least recognized. It distinguishes from sibling 'ai_visibility_check' (single entity) and 'compare_entities' (generic) by focusing on AI visibility specifically.

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 includes a use case ('competitive AI-marketing audits') and an example query. It implies when to use this tool over siblings (e.g., for side-by-side comparison), but does not explicitly state when not to use it or list alternatives beyond the implied contrast with ai_visibility_check.

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, idempotentHint, openWorldHint, and no destructiveness. The description adds valuable behavioral details: fans out across services, returns specific fields, partial failure handling with sources_failed, and a timing note for bundlephobia's first measurement (5-30s). 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?

Front-loaded with main purpose in first sentence. The second sentence is dense with lists and parentheses, but overall structure is logical. Could be slightly more concise, but still efficient given 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, but description compensates by listing return fields and behavior. Parameter coverage is full. Covers purpose, usage guidelines, limitations, and partial failure behavior comprehensively. Suitable for the tool's complexity.

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 adds that version defaults to latest and scoped packages are accepted, but this information is already in the schema descriptions. No significant additional semantic value for parameters beyond what schema provides.

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

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

The description clearly states it is a composite check for npm packages, covering multiple data sources (deps.dev and bundlephobia) and using specific verbs like 'scan' and 'fans out'. It distinguishes from siblings by specifying the npm ecosystem and the one-call composite nature.

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 whenever an agent asks...' and gives concrete examples. It also notes limitations (NPM only in v1, fallback for other ecosystems) and handles partial failures gracefully, providing clear context on when to use 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=true, destructiveHint=false, etc., so the description adds no extra behavioral context. It does not disclose any traits beyond what annotations provide.

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 but lacks substance. While not verbose, it is under-specified and fails to convey necessary information efficiently.

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 and no descriptions, and sibling tools present, the description is too sparse to provide complete context for an agent to use the tool correctly. The existence of an output schema reduces some burden, but overall completeness is low.

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 any parameters. The examples in the schema provide some clues, but the description itself adds no meaning beyond the 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 'GOV.UK full search' clearly indicates the tool searches the GOV.UK website. It is specific but does not differentiate from the sibling tool 'search_autocomplete'.

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. The description lacks any context about ideal use cases or limitations.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds no additional behavioral traits (e.g., rate limits, return format), so it does not enhance transparency beyond what annotations provide.

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 (two words) but lacks informative value. It is under-specified rather than concise, and every word could be removed without loss. It does not earn its place.

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

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

Despite having an output schema, the description does not clarify what the tool returns (e.g., list of strings, relevance scores). It is incomplete for a simple tool with one parameter.

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 schema description coverage at 0%, the description must explain the 'query' parameter but fails to do so. 'Autocomplete suggestions' does not describe the parameter's meaning, format, or constraints, leaving a significant gap.

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 provides suggestions for completing a query. This gives a basic idea but is vague; it does not specify what kind of suggestions or how they relate to other tools like 'search'. It is not a tautology 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 such as the sibling 'search' tool. The description does not mention context or exclusions, leaving the agent to guess.

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 convey read-only, open-world, idempotent, and non-destructive behavior. The description adds no behavioral details such as what happens when the base_path is invalid, or the structure of the returned node. It provides minimal added 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 extremely short (three words), but this brevity comes at the cost of completeness. It is not an efficient use of space; it fails to convey essential information while being 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?

Despite having an output schema, the description does not indicate what the tool returns or how it behaves. For a single-parameter tool, a complete description would clarify the return value and edge cases; this one is insufficient.

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 0% description coverage, and the tool description does not explain the 'base_path' parameter. The agent receives no clue about its format, purpose, or constraints, relying solely on the parameter name which is insufficient for 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 'Taxonomy tree node' is a noun phrase that labels the resource but fails to state the action the tool performs. It does not specify whether the tool retrieves, lists, or navigates taxonomy nodes, making the purpose vague for an agent.

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 its siblings (e.g., 'content', 'search'). The description lacks any context about typical use cases or alternatives, leaving the agent without decision support.

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, openWorldHint, idempotentHint, and destructiveHint. The description adds useful behavioral context: the tool uses SEC EDGAR + XBRL, returns a specific verdict set (confirmed, etc.), and provides citations. 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 moderately concise but packed with information: trigger phrases, scope, return output, and efficiency claim. It is front-loaded with the most important usage cues. Every sentence earns its place, though a minor tightening could be possible.

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, complete annotations, and absence of an output schema, the description covers the necessary context: what the tool does, its limitations (v1 supports company-financial), what it returns (verdict, structured form, citation), and how it works (via SEC EDGAR + XBRL). It is sufficiently complete for an agent to invoke correctly.

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

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

With 100% schema coverage, the schema already describes the 'claim' parameter well. The description adds value by providing example formats ('Apple's FY2024 revenue was $400 billion') and clarifying that it supports natural-language claims, thus enhancing the semantic understanding beyond the schema.

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

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

The description clearly states the tool's purpose: natural-language claim verification, fact-checking. It provides explicit trigger phrases ('Is it true that…', 'fact check', etc.) and specifies the scope (company-financial claims for public US companies). While it doesn't explicitly distinguish from sibling tools, the unique combination of trigger phrases and domain specificity makes its purpose clear.

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

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

The description gives clear guidance on when to use: 'whenever the agent needs to check whether something a user said is factually correct.' It also highlights efficiency benefits (replaces 4-6 sequential calls). However, it does not explicitly state when not to use or mention alternative tools.

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