Gdelt

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral details: it explains the default model is free, that Anthropic probing requires a BYO key paid directly to Anthropic, and the output format. No contradictions with annotations.

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

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

The description is concise (4 sentences) and front-loaded with the main action. Every sentence adds value — no redundant information. The structure flows logically: action, default behavior, optional enhancement, output summary.

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

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

Given the tool has no output schema, the description adequately explains the return structure (per-model {score, confidence, signals, raw_response} + combined view). It also covers use cases and configuration. Missing are potential error cases (e.g., invalid API key), but the description is sufficient for typical 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?

The input schema has 100% description coverage, but the description elaborates on parameters: it explains the default model for `models`, clarifies that `_apiKey` is only needed for Anthropic and how it's used, and that `context` helps disambiguate. This adds meaningful 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 clearly states the tool probes LLMs for knowledge about an entity and returns a visibility score (0-100). It specifies the default model, optional Anthropic probing, and the return structure. The purpose is distinct from sibling tools like 'scan_competitor_ai_presence' or 'search_within', as it focuses on AI visibility scoring.

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 lists use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It implies when to use, but does not explicitly state when not to use or compare to alternative tools. However, the context is clear enough for an agent to infer appropriate scenarios.

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

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

Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive, so the description does not need to restate those. It adds useful behavioral context: routing across 5,529 tools, filling arguments, returning stable citation URIs, 'one fast call', and live-news routing. It stops short of disclosing potential failure modes or rate limits, so a 4 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 description is dense but every sentence earns its place: it establishes priority, lists coverage areas, explains the mechanism, gives triggers, provides examples, and points to alternatives. It is front-loaded with the most important instruction ('PREFER OVER WEB SEARCH') and uses clear structural markers like 'START HERE' and 'Step up only when needed'.

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 complex routing tool with no output schema, the description is complete. It explains what the tool returns (structured answer with stable pipeworx:// citation URIs), when to use it, what categories it covers, and how it relates to sibling tools. The only structured gap is output details, but the description explicitly covers the return format enough for agent 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 documents the 'question' parameter and its five aliases with 100% coverage. The description enriches this by providing numerous concrete example questions and the exact phrasing patterns that trigger the tool, which helps the agent populate the parameter effectively. This goes beyond the schema's bare alias list.

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

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

The description clearly states the tool's function: it routes questions to one of 5,529 tools across 1,455 verified sources and returns a structured answer with pipeworx:// citation URIs. It explicitly distinguishes itself from alternatives like ask_pipeworx_grounded and deep_research, making its purpose unambiguous.

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

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

The description opens with 'PREFER OVER WEB SEARCH' and gives explicit when-to-use triggers ('what is', 'look up', 'find', etc.). It names concrete alternatives (ask_pipeworx_grounded, deep_research) and states when to step up to each, plus a note about breaking-news behavior. This is exemplary 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?

The description discloses key behavioral traits beyond annotations: it is an experimental edge with live candidate routing, currently matches ask_pipeworx exactly, and results are compared to the stable router. This complements the readOnly/openWorld/idempotent 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 four sentences long, front-loaded with the key 'Beta version of ask_pipeworx' concept, and each sentence adds value (identity, current state, usage, fallback). It is slightly verbose but well-structured.

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

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

Given the complexity (universal router with 5,529 tools) and no output schema, the description provides sufficient context: current parity with the stable router, how to use it, and the experimental comparison purpose. It adequately explains the tool within its ecosystem.

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 covers all parameters (6 total, 5 as aliases for 'question') with 100% coverage. The description adds no parameter-specific details beyond saying 'same arguments' as ask_pipeworx, so it does not elevate the baseline of 3.

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

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

The description clearly identifies the tool as a beta universal router, identical to ask_pipeworx but with candidate routing improvements. It states the tool's unique scope ('same 5,529 tools, same arguments, same response shape') and distinguishes it from the stable ask_pipeworx sibling.

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

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

The description gives explicit usage guidance: 'Use it exactly like ask_pipeworx when you want the newest routing.' It also explains the experimental nature and that results are compared to the stable router. It does not explicitly state when NOT to use it, but the context is clear.

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

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

Beyond the readOnly/idempotent annotations, the description discloses the response structure (answer, evidence, confidence, source, fetched_at, refusal_reason) and defines all refusal reasons with exact enum values. It also reveals the extra LLM call cost, providing critical behavioral context the annotations don't cover.

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?

Although the description is a single long paragraph, every sentence carries distinct value: purpose, mechanism, return format, usage, and cost trade-off. No filler or redundancy exists.

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 complex tool with no output schema, the description thoroughly explains the return value shape and all failure modes. It also specifies when to use vs. the lighter alternative, making it self-contained for an agent to decide and 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?

The schema covers 100% of parameters with descriptions, so baseline is 3. The description adds no additional parameter semantics beyond stating the question should be natural language, which the schema already provides. The aliases are fully documented.

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 is a 'hallucination-resistant answer mode for high-stakes reads' and distinguishes it from sibling ask_pipeworx by noting 'Same routing as ask_pipeworx' but adding extraction and refusal behavior. It clearly communicates the verb (extract groundable answer) and resource (Pipeworx 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?

It gives explicit usage guidance: 'Use whenever an answer will be quoted, cited, or acted on' and 'prefer ask_pipeworx for casual lookups.' This directly tells the agent when to choose this tool over the sibling, including the cost trade-off.

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

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

With annotations declaring readOnlyHint, idempotentHint, and not destructive, the description adds significant context beyond: it explains market resolution, classification, fan-out, response shapes, resolver contract, parent event extraction, news fallback, safety for low confidence, closed markets, wide spreads, and cancellation rules. There is 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 well-structured with sections and examples, but it is very long. It is front-loaded with the main purpose and every sentence is informative. However, it could be more concise.

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

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

Given the complexity of the tool, the description is exceptionally complete. It covers all inputs, the process, output structure, edge cases (low confidence, closed markets, wide spreads), safety, and cancellation rules. No output schema exists, so the description carries the full burden and succeeds.

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

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

The input schema has 100% description coverage, so baseline is 3. The description adds value by explaining how to use parameters (e.g., depth options 'quick' vs 'thorough' and include_raw behavior), enhancing 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 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the inputs (slug, URL, question text) and outputs (evidence packet, comparison). It distinguishes itself from sibling tools by being a dedicated research tool for Polymarket bets.

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.' It provides extensive examples of fan-out for different bet types, but does not explicitly state when not to use the tool or mention alternatives. This is clear context but lacks 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 readOnly, openWorld, idempotent, non-destructive. Description adds value by detailing data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, and citation URIs. No contradiction.

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

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

Description is relatively long but front-loaded with query examples; each sentence adds value. Minor redundancy ('side-by-side', 'one parallel call') but overall 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 no output schema, description explains return format (paired data, citation URIs, sorted by primary metric) and covers entity-specific behavior. Completely adequate for agent invocation.

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

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

Schema coverage is 100% with both parameters described. Description adds examples of valid values (tickers or drug names) and constraints (2-5 items), enhancing usability.

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 is for side-by-side comparison of 2-5 companies or drugs in one parallel call, with examples of queries like 'compare X and Y'. It distinguishes from siblings by emphasizing it should replace sequential single-pack lookups.

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 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing clear when-to-use. No when-not-to-use stated, but the context is sufficient.

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

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

The description reveals numerous behavioral traits beyond annotations: account requirement, paid tier for 'thorough', parallel decomposition/routing, return packet structure (verbatim evidence, confidence, source, fetched_at, pipeworx:// citations), explicit gaps[] with never-invented guarantee, contradiction[] scanning for standard/thorough, semantic excerpting, and expected latency. Even though annotations already indicate read-only/idempotent, the description adds critical operational details. 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 long but densely informative. It is front-loaded with the most critical operational constraint (account required) and alternates gracefully into usage scenarios, depth semantics, return format, and latency. Each sentence adds distinct value, but the sheer length and density might overwhelm an agent scanning for key details. Still, structure is logical and purposeful.

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 value (findings packet, gaps[], contradictions[], hop field, citation_uri). It also covers performance expectations, limitations on current-news topics, and how depth levels affect output. For a complex tool with two parameters, this description leaves no critical gaps for an agent to misuse the tool.

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

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

The schema already documents both parameters thoroughly (question and depth with enum). The description adds contextual semantics: example questions, what each depth level implies (standard re-angles gaps, thorough chases leads + contradictions), and that depth governs facet count and hop recovery. It doesn't restate the schema verbatim but enriches parameter understanding. Slight deduction because the description doesn't explicitly define 'quick' (only schema mentions 3 facets), so it's not perfect.

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

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

The description clearly identifies the tool's verb and resource: 'Grounded multi-source research across Pipeworx's 1455 STRUCTURED data sources' in one call. It explicitly distinguishes itself from siblings (e.g., 'this is NOT open-web search') and contrasts with ask_pipeworx for single lookups. This goes beyond a generic purpose statement, making the tool's niche concrete.

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

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

The description gives explicit when-to-use and when-not-to-use guidance: 'Best for broad/multi-part questions over structured data', 'For a single lookup use ask_pipeworx', and 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'. It also warns about account/tier requirements and directs the reader to an alternative if not signed in. This is exemplary 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 declare readonly and idempotent hints. The description adds valuable behavioral details: returns top-N tools with full schemas and examples, ready to call directly, no second lookup. 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 moderately concise, front-loaded with purpose, and contains no filler. Could be slightly shorter but 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?

Without an output schema, the description adequately explains the return format (top-N tools with names, descriptions, schemas). It covers usage context ('Call this FIRST') and parameter aliases.

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 beyond schema by explaining the query parameter accepts natural language and lists aliases, enhancing usability.

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 'Find tools by describing the data or task' and lists many domains, clearly distinguishing it from sibling tools which are specific to certain tasks (e.g., bet_research, search_articles).

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', providing clear context for when to use this tool. It does not explicitly state when not to use it, but the guidance is effective.

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

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

Annotations already indicate read-only, open-world, and idempotent behavior. The description adds key behavioral context: patents soft-fail after May 2025, fundamentals are sorted by period_end DESC, and it fans out across multiple sources. 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 front-loaded with example queries, clearly states the tool's purpose, then lists outputs. It is slightly verbose but each sentence adds value. Could be streamlined slightly but is well-structured.

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

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

With no output schema, the description fully explains return values (cik, filings with URIs, fundamentals, patents, news, LEI). It covers limitations, data sources, and fallback behavior, making it complete for a tool of this 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 describes both parameters (type, value) but description adds critical nuance: type is only 'company', value must be ticker or zero-padded CIK, and names are not supported. Schema coverage is 100%, and description meaningfully extends 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 clearly states the tool's purpose: 'full cross-source profile of a US public company in ONE parallel call.' It lists specific outputs (cik, filings, fundamentals, patents, news, LEI) and contrasts with chaining single-pack lookups, distinguishing it from siblings like 'compare_entities' and 'deep_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 says when to use: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also provides a clear exclusion: 'names not supported (use resolve_entity first if you only have a name).'

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 destructiveHint=true and idempotentHint=true. The description confirms the delete action but adds no additional behavioral details beyond what is implied by the annotations and schema.

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 clear, front-loaded sentences with no redundant words. First sentence states purpose, second provides usage guidance and tool pairing.

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 adequately covers purpose and usage. It lacks details about return behavior, but given the tool's simplicity, this is acceptable.

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

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

The input schema has 100% coverage and describes the 'key' parameter as 'Memory key to delete'. The description only mentions 'by key', adding no new semantic 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 the action 'Delete a previously stored memory by key' with a specific verb and resource. It distinguishes from siblings by pairing with 'remember' and 'recall', indicating complementary functions.

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 three usage scenarios: when context is stale, when the task is done, or to clear sensitive data. It does not explicitly state when not to use, but the clear context suffices.

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 meaningful behavioral context by explaining the fetching, extraction, and markdown emission process, which complements the 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 a single concise paragraph that front-loads the action and purpose. Every sentence adds value, with no extraneous information.

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

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

Given the tool has only 2 parameters, no output schema, and the annotations cover safety and idempotency, the description fully explains the tool's behavior, inputs, and output format. No gaps remain.

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

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

Schema coverage is 100% with descriptions for both parameters (url, max_links). The description does not add new semantics beyond what the schema already provides, so a baseline of 3 is appropriate.

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

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

The description clearly states the tool generates a 'production-ready llms.txt file' for any URL, specifying the extracted fields (title, description, key links) and output format. This distinguishes it from sibling tools, none of which perform similar llms.txt generation.

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 three concrete use cases (indexing a client's site, drafting for one's own project, auditing a competitor), but does not explicitly state when not to use it or suggest alternative tools. Given the niche functionality and lack of overlapping siblings, this is adequate.

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, destructiveHint=false. The description adds context by naming the specific return fields and the default behavior of listing only active subscriptions, enhancing transparency 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 with no fluff; all information is relevant and front-loaded.

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

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

Given the single optional parameter, comprehensive annotations, and description listing return fields in lieu of an output schema, the description is fully complete for this simple read-only tool.

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

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

Schema coverage is 100% with a description for include_inactive. The description adds meaning by rephrasing 'include cancelled subscriptions' and notes the default false, complementing 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 lists the caller's active subscriptions and specifies the returned fields (id, type, params, etc.). It effectively distinguishes from sibling tools like subscribe and unsubscribe.

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 this to review what you're monitoring before adding more or to find an id to cancel,' providing clear when-to-use context and implicitly when not to use.

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

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

Beyond the annotations (readOnlyHint=false, etc.), the description discloses meaningful behaviors: rate limiting (5 per identifier per day), cost (free, no quota), return behavior (claim_token when no account), and the process (team reads digests daily). None of this contradicts the annotations; it substantially enriches what the agent can infer.

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 longer than average but every sentence carries distinct information: purpose, use cases, exclusions, token mechanics, rate limits, and cost. It is well-structured and front-loaded with the core purpose. It could be slightly trimmed, but the density is high and no sentence is 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 complexity (4 parameters, nested objects, no output schema), the description fully covers what the agent needs to know: what the tool does, when to use it, what to avoid, how the claim_token workflow operates, and the operational constraints. There are no significant gaps in understanding.

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 baseline is 3. The description adds value by explaining the claim_token parameter's purpose and usage pattern (passing it back to read resolution status), and by framing the type enum's categories in terms of real feedback scenarios. This goes beyond the raw schema descriptions without duplicating them.

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 opens with a specific verb+resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It then enumerates concrete use cases (bug, feature, data_gap, praise), making the tool's purpose unmistakable and clearly distinct from sibling tools, none of which are feedback 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 provides explicit when-to-use guidance with examples for each feedback category, and gives a critical exclusion: only for Pipeworx-owned tools, not other MCP servers. It also tells users when not to use it ('file it with that server instead') and how to handle claim tokens, making the decision boundary exceptionally 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 indicate read-only, idempotent, non-destructive behavior. The description adds valuable context: self-aggregating from CF analytics-engine, no PII, cache durations 5min-1h. 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?

Two sentences with no redundancy. First sentence states core functionality, second bulletizes use cases. Appropriate length and front-loaded.

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

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

No output schema exists, but the description thoroughly explains return types (top tools, top packs, total call volume) and caching behavior. Complete for a simple trending tool with a single optional 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?

Schema description coverage is 100%. The description adds meaning beyond enum values by explaining that shorter windows surface what's hot and longer windows show steady-state demand.

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 returns 'top tools, top packs, and total call volume over a recent window,' with a clear verb-resource relationship. It differentiates from sibling tools by focusing on Pipeworx usage trends rather than general tool discovery.

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?

Three specific use cases are listed (discovering hot data sources, confirming canonical tool, checking alignment). It explains window trade-offs but does not explicitly contrast with siblings like 'discover_tools' or give when-not-to-use advice.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description elaborates with details on monotonicity checks, partition_sum, Jaccard similarity filter, partition placeholder filter, fill check against live CLOB depth, and response structure. 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 comprehensive but slightly verbose; it front-loads the main purpose and then details modes. Each sentence adds value, but some technical depth could be streamlined 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 modes, algorithms, fill check) and absence of an output schema, the description is remarkably complete. It explains response structure, trade conditions, and edge cases like placeholders and Jaccard similarity.

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 both parameters. The description adds significant value beyond schema: explains recommended usage, accepted formats (URLs), semantics of slug vs topic, and when to use each. This allows precise invocation.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes between event mode, topic mode, and a trending scan with no args, making the purpose evident and differentiating 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 provides explicit guidance on when to use each parameter mode (event for specific market, topic for cross-event, no args for trending). It also explains fill check limitations and references polymarket_fill_risk for custom sizing, but does not explicitly contrast with all sibling tools like polymarket_edges.

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, non-destructive behavior. The description adds valuable context: caching at 1-hour intervals, detailed model families, and diagnostic output. 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 front-loaded with the core purpose but becomes verbose with extensive technical details on model families and internal logic. While well-structured, it could be more concise.

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

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

Given the complexity (9 parameters, no output schema), the description is remarkably complete. It explains the three opportunity segments, edge calculation, filters, diagnostics, and caching behavior, leaving little ambiguity 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 coverage is 100%, so the baseline is 3. The description does not elaborate on individual parameters beyond what the schema provides. It focuses on high-level segments rather than parameter nuances.

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 a specific verb ('scan' and 'return') and resource ('Polymarket markets'), and explains its use case for discovering betting opportunities. However, it does not explicitly differentiate this tool from siblings like polymarket_arbitrage, which share overlapping functionality.

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

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

The description implies usage for 'what should I bet on today' and describes the opportunity categories, but it does not explicitly say when to use this tool over alternatives or provide when-not-to-use guidance. The context of alternatives is absent.

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

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

The description details response structure (tracked, expired, snapshot_dates) and explains derived fields like trend and decay_pp_per_day. It also notes limitations (60-day TTL, daily closes, not intraday), adding significant value beyond readOnlyHint and idempotentHint 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 lengthy but well-structured, explaining each response field in separate paragraphs. It front-loads the core question. Though verbose, every sentence adds value for a complex tool.

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

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

Given no output schema, the description thoroughly covers return values and their meanings. It also addresses limitations like data gaps and snapshot TTL. All essential aspects for using the tool are present.

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 default values and range for 'days', and explicitly lists allowed values for 'window' (24hr, 1wk, 1mo), which are not enums in the schema. This extra context improves parameter understanding.

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

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

The description clearly states the tool provides edge persistence and decay telemetry, answering 'how long has this edge existed and is it shrinking?'. It distinguishes from siblings like polymarket_edges by focusing on time-series analysis.

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

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

The description implies when to use (to differentiate old vs. new edges) but does not explicitly contrast with alternatives or state when not to use. It provides context through examples but lacks explicit usage boundaries.

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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint = false. The description adds behavioral context: walks the ladder, returns verdicts (clean/degraded/cannot_fill), forced directional risk, and the dominant loss mode (partial basket fills). 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?

Description is a single long paragraph but effectively front-loaded with purpose and requirements. Could be more structured (e.g., bullet points for modes), but each sentence provides necessary information. Not overly verbose given the tool's 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?

Despite no output schema, the description thoroughly covers both modes, default behaviors, return fields (top_of_book, vwap_fill_price, slippage, verdict, etc.), and risks. It is complete enough for an AI to understand and invoke the tool correctly.

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

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

Schema description coverage is 100%, so baseline is high. The description adds operational details: default side for basket is auto from partition sum, size_usd interpreted as settlement notional in basket mode, clamp 10–1,000,000. Adds meaning 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 it is a 'realizable-vs-theoretical edge check against live CLOB order-book depth,' specifying two modes (single-market and basket) and listing specific return fields. It distinguishes from sibling tools like polymarket_arbitrage and polymarket_edges.

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

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

Explicitly instructs to use before acting on polymarket_arbitrage sell/buy-every-leg signals or polymarket_edges trades above ~$500, explaining why (theoretical overround not capturable on thin books, partial fills create unhedged positions). Also states the prerequisite of providing either market or event.

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?

Goes beyond annotations by detailing compatibility_warning scenarios, temporal alignment, skipped cross types, and the fact that most spreads are not tradeable. No contradiction with readOnlyHint, openWorldHint, idempotentHint, destructiveHint.

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 lengthy but well-structured: starts with core behavior, then modes, response details, and safety fields. A bit verbose but appropriate for the tool's 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?

Given no output schema, the description comprehensively explains response fields (leg-by-leg prices, spread array, safety fields) and edge cases (compatibility warnings, temporal alignment). Covers all necessary context for correct 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% with good property descriptions. The tool description adds context: explains how topic overrides work and the interaction between parameters, but schema already lists them clearly.

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 computes cross-venue spread between Kalshi and Polymarket for similar questions, with two modes (topic shortcuts and explicit tickers). It differentiates from sibling polymarket tools like polymarket_arbitrage by focusing on spread across venues.

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 explains when to use each mode, warns that pre-mapped topics often return compatibility warnings, and describes conditions where spread is meaningful vs not (e.g., temporal alignment, bet shape equivalence). This guides appropriate invocation.

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

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

Annotations already indicate safe read-only idempotent behavior. The description adds valuable context: scoping to user identifier, pairing with sibling tools, and the ability to list all keys. 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?

Three sentences front-loaded with purpose, then usage, then scoping. No wasted words; each 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?

For a simple retrieval tool with one parameter and no output schema, the description covers purpose, usage, scoping, and sibling relationships. Return values are implied but not explicitly described.

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

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

The only parameter, 'key', is fully described in the schema (100% coverage). The description restates the schema's note about omitting the key to list all, adding 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 that the tool retrieves a saved value or lists all keys, using specific verbs and resource terms. It provides concrete examples (ticker, address, research notes) and distinguishes itself from siblings like remember and forget.

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

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

The description explicitly advises when to use (when needing previously stored context) and how to list all keys (omit key argument). While it mentions pairing with remember/forget, it does not explicitly state when not 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.

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

Annotations indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds behavioral context: mark_read:true flags events as read, returns only newer ones on next call. No contradictions. Also describes feed persistence and alternative access.

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

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

Three sentences, front-loaded with main action, no unnecessary words. Each sentence adds value: purpose, return fields, filtering/mark_read, and alternative access.

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 parameter-rich list tool (5 optional params, no output schema), description covers return fields, filtering, mark_read behavior, polling suitability, and alternative endpoint. Complete enough 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 100%, and description adds concrete examples: 'Filter by type (e.g. "sec_8k")', 'since (ISO timestamp)', 'set mark_read:true to flag returned events read'. This provides practical usage 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 it pulls fired events from subscription feed. It specifies return fields (source, citation_uri, raw payload) and filtering options. The tool is distinct from siblings like list_subscriptions or search_articles.

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 says 'polls work fine' and mentions alternative endpoint for scripts/dashboards, implying when to use tool vs. direct API. However, it does not explicitly contrast with siblings like search_articles or list_subscriptions for similar use cases.

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

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

The description details the tool's behavior: it fans out to multiple sources, explains the GDELT→GNews fallback, mentions the USPTO soft-fail due to API sunset, and states the return structure (changes grouped by source, total_changes count, citation URIs). This goes well beyond the annotations which only indicate read-only, idempotent, etc.

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 efficiently conveys purpose, sources, parameter details, and sibling distinction. It is front-loaded with the core action. Minor improvement could be adding bullet points for readability, but it's still concise and well-structured.

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

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

Despite no output schema, the description adequately explains the return format (changes grouped by source, total count, citation URIs). It also covers edge cases like the USPTO soft-fail. The description is complete 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?

While the input schema already describes all parameters (100% coverage), the description adds helpful context: examples of ISO dates and relative shorthand for 'since', and that 'value' accepts ticker or CIK. This adds 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 provides a change feed for a company by querying multiple sources (SEC EDGAR, GDELT/GNews, USPTO). It includes example queries and explicitly distinguishes from the sibling tool 'entity_profile'.

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

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

The description gives explicit usage scenarios ('What's new with X') and directly tells when to use the sibling tool 'entity_profile' instead. It also describes fallback behavior between GDELT and GNews.

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 idempotentHint=true and destructiveHint=false. Description adds scoping by identifier, persistence differences for authenticated vs anonymous users. Does not mention overwrite behavior if same key is used again. 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?

Two sentences with no wasted words. Front-loaded with primary purpose, then context and alternatives. 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?

Tool is simple with 2 params, no output schema. Description covers purpose, usage context, behavioral notes, and relationships with siblings. 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 has 100% description coverage, so baseline is 3. Description adds context by giving example key patterns and explaining that value can be any text. Provides meaning beyond schema via scope and persistence info.

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

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

The description clearly states the tool saves data for reuse, with concrete examples like 'resolved ticker', 'target address', 'user preference'. It distinguishes from siblings 'recall' and 'forget' by mentioning pairing.

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: 'when you discover something worth carrying forward'. Mentions alternatives: 'Pair with recall to retrieve later, forget to delete.' Provides 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, idempotentHint, and openWorldHint, but the description adds substantial behavior: it cascades through several lookup endpoints, degrades gracefully when GLEIF/OpenFIGI are unavailable, returns unresolved identifiers explicitly under 'unresolved', and labels every identifier with its source. This goes well 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 lengthy but front-loaded with relatable example queries and the core purpose. Each section adds value (supported types, degradation behavior, return format). There is minor redundancy (input formats repeated in schema and description), but overall it's appropriately sized for the tool's 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?

With no output schema, the description carries the responsibility of explaining return values. It specifies that identifiers are source-labelled, unresolved ones are explicitly listed, and a drug citation format is included. It also covers failure modes (graceful degradation), making it complete for a complex tool with two entity types and cascading lookups.

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 parameter descriptions in the schema already explain the accepted formats for both type and value. The tool description mostly repeats the same examples, adding little new parameter-specific meaning beyond what the schema provides. Hence no bonus above baseline.

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

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

The description clearly states the tool resolves user-supplied names to official identifiers, with explicit verb ('resolve') and resource ('name to canonical/official identifiers'). It distinguishes from siblings by instructing 'Use FIRST whenever you have a name but need an ID,' making it the unambiguous go-to for name-to-ID lookups.

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 FIRST whenever you have a name but need an ID,' giving clear when-to-use guidance. It also details the two supported types (company, drug), what inputs each accepts, and notes that the tool replaces 2-3 manual lookups, reinforcing its role. While it doesn't list alternatives, the 'FIRST' directive 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 flag readOnlyHint, idempotentHint, and openWorldHint, so safety and idempotency are known. The description adds that the tool probes each entity with ai_visibility_check and returns a ranked list, but provides no additional behavioral caveats (e.g., rate limits, data freshness).

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

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

The description is two sentences plus a brief example, front-loading the core purpose. Every sentence adds value; no redundancy or 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?

With 4 parameters fully described in schema and a clear output format hinted in the description, the tool definition is complete for an agent to correctly select and invoke it. No missing context.

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

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

Schema description coverage is 100%, so parameters are already documented. The description adds value by specifying that the first entity in the 'entities' array is treated as the 'subject' for narrative, and mentions default model behavior, which goes 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 compares AI visibility across multiple entities side-by-side, probes with ai_visibility_check, ranks by score, and surfaces recognition levels. This clearly distinguishes it from single-entity tools like ai_visibility_check.

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 a concrete use case ('competitive AI-marketing audits') with an example query, implying when to use. It does not explicitly call out alternatives or when not to use, but the context is clear.

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

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

Discloses partial failure behavior, timing caveats (5-30s initial measurement for bundlephobia), and graceful degradation. This adds significant context beyond annotations (readOnlyHint, idempotentHint) which already label it safe.

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: starts with the core purpose, then usage guidance, return details, limitations, and failure behavior. Every sentence adds value without repetition or 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?

The tool has no output schema, but the description fully details the return fields (summary block, advisories, links, versions) and covers edge cases (partial failures, timing). This provides complete understanding 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 coverage is 100%, so baseline is 3. The description adds extra value by clarifying that scoped packages (e.g., '@types/node') are accepted and providing concrete version examples (e.g., '18.3.1'). This slightly improves usability 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 defines the tool as a composite check for npm packages, combining deps.dev and bundlephobia data. It distinguishes itself from siblings by focusing specifically on npm package evaluation, and the verb 'scan' plus resource 'dependency' aligns well.

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: for questions about safety, popularity, size, or cost of adding an npm package. Also specifies NPM ecosystem only and points to deps.dev:version for other ecosystems, giving clear when-not 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 provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: the authoritative source (GDELT 2.0), update frequency (every 15 minutes), and return fields (URL, title, domain, etc.). No contradictions.

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

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

The description is concise and well-structured: it starts with purpose, then source, return fields, and query syntax. Every sentence adds value, with no wasted words. The front-loading of the preference over web search is 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 tool's complexity (6 params, output schema exists), the description is complete. It covers source, update frequency, return fields, query language, and usage context. The output schema is present, so the description's mention of return fields is an added bonus.

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 significantly enriches parameter meaning by detailing the query language (AND, OR, phrases, exclude, advanced filters like sourcecountry:, sourcelang:, theme:, near:). This goes well beyond the schema's 'GDELT query string' 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 explicitly states the tool searches articles and recommends it over web search for 'what did the news say about X' across global media. The verb 'search' and resource 'articles' are clear, and the distinction from sibling tools like web search is emphasized.

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

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

The description provides explicit guidance: 'PREFER OVER WEB SEARCH for 'what did the news say about X' across global media.' It also includes query syntax and examples. While it doesn't contrast with all sibling tools (e.g., deep_research, timeline_tone), the context is sufficient for selecting this tool over web search, earning a 4.

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 technical details beyond annotations: uses BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flagged. Annotations already indicate read-only and idempotent; description adds significant 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 coherent paragraph but contains multiple valuable statements. Front-loads the core purpose, then usage, then technical details. Could be slightly more structured, but every sentence contributes meaning.

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

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

Despite no output schema, the description explains return values (passages with offsets and similarity scores). Covers limitations (char cap, truncation) and embedding technique, providing a complete picture for an agent to use the tool effectively.

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

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

Schema covers 100% of parameters with descriptions. Description adds examples for query and clarifies limits for text and limit parameters, providing extra practical guidance 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 function: semantic search inside a fetched record. It gives concrete examples (SEC 10-K, article) and distinguishes itself from sibling tools like ask_pipeworx by focusing on searching within an already-fetched text.

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 the record is too large for the prompt. Provides a pairing with ask_pipeworx_grounded for grounding over passages. Implies when not to use (small records can be directly included).

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

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

Annotations indicate readOnlyHint=false (write operation), openWorldHint=true (external interactions), idempotentHint=true, destructiveHint=false. The description adds behavioral context: returns new subscription ID, webhook secret returned once, auto-disabled after 10 consecutive failures, SMS cap. No contradictions with annotations. More detail on idempotency semantics would be beneficial but not required.

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 dense yet efficient, with every sentence contributing unique value. It starts with purpose and output, then prerequisites, followed by types and delivery channels in a logical flow. No filler; it balances detail with readability.

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 complex tool with 3 parameters, nested objects, and no output schema, the description covers prerequisites, type-specific parameters, delivery options, and limitations (SMS cap, webhook auto-disable). It mentions the return of subscription ID and webhook secret. Lacking a complete return structure outline, but still highly informative.

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 parameters are covered by schema descriptions (100% coverage). The description adds substantial meaning: for each type, it gives concrete examples (e.g., {ticker:'AAPL', items:['5.02']}), explains required vs optional fields, and details delivery channel behavior (e.g., webhook signing, phone verification). This far exceeds baseline.

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

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

The description clearly states the tool's purpose: creating a proactive monitoring subscription to a live-data event stream and returning a subscription ID. It distinguishes itself from sibling tools like list_subscriptions and unsubscribe by focusing on creation. The list of supported types and delivery channels further clarifies its scope.

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

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

The description provides explicit prerequisites (Pipeworx OAuth account with persist capability) and details on when to use each subscription type with examples. It also explains delivery channel options and their conditions (e.g., SMS requires phone verification, 10/day cap). It could further clarify when not to use this tool (e.g., for anonymous users) but is already strong.

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, idempotentHint=true, and openWorldHint=true. The description adds that it returns example questions with tool and argument shapes from the live catalog, but does not add significant safety or behavioral context beyond what the annotations provide. 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 but is packed with useful information: alternative phrasings, use case, output details, and parameter guidance. It is not overly verbose for its purpose, though a slight restructuring 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, the description fully explains the return value (category-bucketed example questions with tool+argument shapes derived from the live catalog). The parameter and usage are clearly documented. This is complete for an onboarding tool of this 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% (1 optional parameter with description), but the description adds meaning by providing example topic values ('finance', 'pharma', etc.) and explaining that omitting it gives a cross-category spread. This goes beyond the schema's brief 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 explicitly states this is 'the onboarding entry point' for an agent that wants to know what Pipeworx can do. It specifies the output (category-bucketed example questions with tool+argument shapes) and distinguishes itself from siblings by recommending 'Use this FIRST' and mentioning meta-tools like ask_pipeworx, entity_profile, etc.

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 clearly says when to use it: 'when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also explains how to call it (no args or with a topic). However, it does not explicitly say when not to use it or mention specific alternative tools beyond a list.

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, destructiveHint=false. Description adds detail: 'Returns datapoints with timestamp + tone value (-100 very negative .. +100 very positive, computed from GDELT's sentiment scoring of every article matching the query).' This explains the output format and derivation. No mention of pagination or rate limits, but sufficient.

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 plus a usage note. Front-loaded with core functionality. No filler words. Each sentence adds value: first defines the tool, second explains return and usage example, third pairs with sibling.

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

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

Given the tool has an output schema (not shown but indicated), the description explains return values (timestamp + tone value range). Covers usage context and pairing. Missing details on error handling or edge cases, but overall complete for a read-only query tool with good annotations.

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% (all parameters have descriptions). Description does not add much parameter-level detail beyond schema, but explains the tone value's meaning and range. Baseline 3 is appropriate as schema does the heavy lifting.

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 'Day-by-day AVERAGE NEWS SENTIMENT for a GDELT query over time.' Specifies the verb (returns average sentiment) and resource (GDELT query over time). Differentiates from sibling timeline_volume by mentioning pairing for sentiment vs interest analysis.

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 for tracking sentiment shifts around a topic, person, country, or event.' Provides a concrete scenario: 'Pair with timeline_volume to chart sentiment vs interest — interest spike + sentiment drop = something bad just happened.' This gives clear guidance on when to use and how to combine with 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, openWorldHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds value by disclosing the output format (datapoints with timestamp and intensity as %), the daily granularity, and cost comparison ('cheaper than search_articles'), going beyond the annotations to inform the agent about behaviors.

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, each adding substantive value: the first defines the core function, the second lists use cases, the third provides a comparison with a sibling tool. It is front-loaded and contains no filler or 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 the tool's complexity (4 parameters, output schema present, rich annotations), the description is complete. It explains the output, use cases, and when alternatives are better. The presence of an output schema means return values do not need to be detailed, and the description covers the remaining aspects thoroughly.

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 already documents all four parameters. The description adds practical meaning through examples and explains the conditional usage of startdatetime/enddatetime with timespan=custom, which enhances understanding beyond the schema's 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 defines the tool's purpose: returning the daily share of global news attention for a query as a percentage. It distinguishes itself from sibling tools like timeline_tone and search_articles by specifying what the tool returns (volume curve) and when it's appropriate to use (when only the volume curve is needed, not source articles).

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

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

The description explicitly states when to use the tool ('detect news-cycle spikes', 'benchmark attention against history', 'pair with timeline_tone') and when not to ('cheaper than search_articles when you only need the volume curve, not the source articles themselves'). This provides clear guidance on alternatives and context.

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

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

Annotations confirm read-only, open-world, idempotent, non-destructive behavior. Description adds context on output format (tone bins, counts, summary) and query language, 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?

Concise and well-structured: opens with a clear verb+resource statement, then usage guidance, output summary, and query language note. Every sentence adds essential information without redundancy.

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

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

Given the tool's simplicity and the presence of detailed annotations and schema, the description fully covers output format, usage recommendations, and query language, leaving no gaps for an AI agent to misinterpret.

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

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

Schema descriptions for all four parameters are present (100% coverage), so baseline is 3. Description adds value by explaining the GDELT query language, the default timespan of '1m', and usage of start/enddatetime for custom timespan, raising the score.

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 returns a histogram of sentiment distribution for a GDELT query, specifies output components (bins, counts, summary), and distinguishes itself from the sibling timeline_tone tool which provides averages over time.

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

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

Explicitly advises preferring this tool over web search for specific queries like 'is coverage of X positive or negative' and 'news sentiment breakdown', and contrasts it with timeline_tone for complementary use.

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

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

Annotations indicate non-read-only, non-destructive, idempotent. Description adds detail: row is deactivated (not deleted) and references recent_alerts. Ownership enforcement is also stated, going 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 waste. First sentence states action, second explains behavioral effects and ownership. 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?

Single parameter, no output schema, annotations present. Description covers ownership, deactivation, and retention of historical events. Sufficient for correct 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 has 100% coverage with description for 'id'. Tool description adds ownership context, indicating the id must belong to the caller, which goes 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 action 'Cancel a subscription by id' with resource (subscription) and verb (cancel). Does not explicitly differentiate from sibling tools like 'subscribe' or 'list_subscriptions', but the ownership enforcement provides context.

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

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

Implies usage for own subscriptions via 'Ownership is enforced — you can only cancel your own subscriptions.' No explicit when-not or alternatives, but the constraint 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?

Beyond the annotations (readOnly, idempotent, openWorld), the description discloses critical behavioral nuances: could_not_verify means the check didn't happen and must not be interpreted as evidence, unsupported means no source was found, and it explains return structure with verdicts, citations, and reasoning. This is valuable context not available in 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 information-dense but well-structured, starting with trigger phrases, then usage, pipeline detail, return format, and a clearly marked 'IMPORTANT' caveat. It is longer than most, but every section contributes critical operational knowledge, so it earns a 4 rather than a 5.

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 routing, multiple verdicts, error semantics) and the absence of an output schema, the description provides a thorough account: it lists all verdicts, explains the meaning of ambiguous ones, states the source paths, and notes it replaces multi-step calls. This is complete enough for an agent to invoke and interpret results 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?

Input schema covers both parameters (claim and tolerance_pct) with descriptive text, so baseline is 3. The tool description does not add additional meaning about parameters beyond what the schema already provides, so no extra credit is warranted.

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 against authoritative sources, with specific verb 'verify claim' and examples. It distinguishes itself from siblings by describing the two routing paths (SEC EDGAR for financial, grounded pipeline for everything else) and mentions it replaces multi-step pipelines.

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 whenever the agent needs to check whether something a user said is factually correct,' providing clear context. It also details the two sub-paths (financial vs. other claims), but does not name alternative tools or state when not to use it, so it lacks explicit exclusions.

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