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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the description builds on that by specifying the real-time ADS-B return types and that it returns every aircraft within a radius. 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 two sentences are front-loaded, every sentence adds value: first describes what it does and returns, second gives concrete use cases. No wasted words.

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

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

The description covers the tool's purpose, return data, and use cases. It does not mention pagination, limits, or data freshness, but given no output schema and a simple tracking tool, these gaps are minor.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds no additional parameter semantics beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: live aircraft tracking returning real-time ADS-B positions (altitude, ground speed, heading, vertical rate) for planes flying overhead or near a location. It uses specific verbs and resource, and the context of sibling tools (e.g., get_aircraft, military_aircraft) is implicitly handled by this focus.

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 for "what is flying above me" / "planes near <place>' which provides clear context for when to use the tool. However, it does not explicitly mention when not to use it or list alternatives, though the sibling tools exist.

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

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

Annotations (readOnlyHint true, etc.) are fully consistent. The description adds cost context (BYO key for Anthropic) and data flow (passed straight through). 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?

Description is a concise paragraph of five sentences, front-loaded with purpose, then details. No unnecessary words.

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

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

Despite no output schema, the description explicitly states per-model return fields (score, confidence, signals, raw_response) and combined view. 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?

Schema coverage is 100%. The description adds context beyond schema: explains default model for 'models', BYO key for '_apiKey', and disambiguation for 'context'. Adds meaningful value.

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

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

The description clearly states the tool probes LLMs for brand visibility and scores it 0-100. It uses the specific verb 'probe' and resource 'LLMs', and distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on 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 explicit use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It explains default vs. optional Anthropic probe. However, it lacks explicit when-not-to-use or alternative tool comparisons.

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 context about routing to 3,547 tools and returning structured answers with citation URIs, which enhances 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?

The description is slightly long but well-structured: starts with a strong recommendation, lists domains, explains the mechanism, then provides usage patterns and examples. It is front-loaded and every sentence contributes.

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 aliases, no output schema, many siblings), the description covers purpose, usage, behavioral details, and examples comprehensively. It leaves no critical gaps.

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

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

Schema coverage is 100% with descriptions for all 6 parameters (all aliases for question). The description adds value by providing example queries and clarifying the natural language input, which helps the agent understand how to formulate questions.

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 that this tool should be preferred over web search for questions requiring authoritative structured data with citations. It lists specific domains and provides numerous examples, clearly distinguishing it from sibling tools.

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

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

The description gives explicit guidance on when to use this tool: 'PREFER OVER WEB SEARCH' and 'Use whenever the user asks...' followed by a list of query patterns. It also provides concrete examples, making it easy for the agent to decide.

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

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

The description extensively details behavioral aspects beyond annotations: resolution process, classification, fan-out examples, response shapes (market, analysis, evidence), resolver contract with confidence levels, parent event extraction, news fallback handling, safety suppression for low-confidence matches, closed market handling, and illiquidity warnings. 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 long but well-structured with sections and examples. Every sentence adds relevant information for a complex tool. It is front-loaded with the core purpose. Could be slightly more concise, but given the tool's intricacy, it is appropriately sized.

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 explains all response fields, edge cases, error states, and safety mechanisms. It covers low-confidence matches, closed markets, wide spreads, parent events, news fallback, and resolver contract. Completely adequate for an AI agent to understand the tool's behavior.

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 meaning by explaining how parameters work together (e.g., depth controls fan-out, market_input accepts multiple formats) and providing examples. This adds value beyond the schema descriptions.

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

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

The description begins by clearly stating the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the verb 'research' and the resource 'Polymarket bet', and distinguishes from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on data fan-out for a single bet.

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 use cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' While it provides clear context for when to use, it does not explicitly state when not to use this tool versus siblings, though the sibling list implies alternatives.

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

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

The description goes beyond annotations (readOnlyHint, idempotentHint) to detail data sources (SEC EDGAR/XBRL for companies, FAERS/adverse events for drugs), handling of off-calendar fiscal years, sorting by primary metric, and the inclusion of citation URIs. No contradictions with annotations.

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

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

The description is front-loaded with key trigger phrases and packs considerable detail without repetition. While it is somewhat lengthy, every sentence contributes useful information, balancing comprehensiveness with efficiency.

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

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

Given no output schema, the description explains the high-level return format ('paired data + pipeworx:// citation URIs per entity') and the sorting behavior. It could be more specific about the exact structure of the paired data, but overall it provides enough context for an agent to understand the tool's inputs and behavior.

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 provides descriptions for both parameters (100% coverage). The description adds significant value by explaining what each enum value returns (company vs drug) and providing concrete examples for the values array (tickers/CIKs for company, drug names).

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

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

The description starts with multiple natural language triggers (e.g., 'Compare X and Y', 'X vs Y') and clearly states it performs 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call'. It specifies the two entity types and the data each fetches, distinguishing it from sibling tools like 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 explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing clear guidance on when to use this tool. It also gives context on natural language queries but does not explicitly list when not to use it or mention alternatives beyond the general preference.

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

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

Annotations indicate safe, read-only, idempotent. Description adds that it returns top-N relevant tools with full schemas and examples, ready to call. No contradictions; adds behavioral context beyond annotations.

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

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

One paragraph, front-loaded with main purpose. Lists domains and outputs efficiently. Every sentence adds value; no fluff.

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

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

For a discovery tool with high schema coverage and annotations, description explains purpose, usage, and output format. Lacks details on pagination or result interpretation, but sufficient for its role.

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

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

Schema coverage is 100%, so baseline is 3. Description adds examples and mentions limit default/max, but these are also in schema. Minimal additional meaning beyond what schema provides.

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

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

The description clearly states 'Find tools by describing the data or task' and lists specific domains, differentiating it from sibling tools that are specific tools. It positions itself as a discovery tool to be called first.

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

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

Explicitly says 'Use when you need to browse, search, look up, or discover' and 'Call this FIRST when you have many tools available'. It lacks an explicit 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: soft-fails for patents, data sources (SEC EDGAR, XBRL, USPTO, GDELT→GNews, GLEIF), and return format details. 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?

The description is information-dense and front-loaded with examples. Every sentence adds value, though slightly verbose for the amount of content. Still efficient overall.

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

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

No output schema exists, but the description thoroughly explains what is returned (CIK, company_name, filings with URIs, fundamentals, patents, news, LEI) and mentions caveats like patent soft-failure. Complete for a complex 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 both parameters described. The description adds meaning: explains type='company' only, value accepts ticker or zero-padded CIK, and explicitly states names not supported, providing examples.

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 uses specific verbs like 'Tell me about', 'research', 'brief me on', and clearly states it provides a 'full cross-source profile of a US public company'. It lists return fields and distinguishes from sibling tools like resolve_entity.

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

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

Explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' and indicates when user asks for a holistic view. It also specifies that names are not supported and advises to use resolve_entity first.

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 as true/false. The description adds significant behavioral context by specifying it returns live real-time ADS-B data, which goes beyond annotations. There is no contradiction, and the description clarifies the real-time nature.

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 short, front-loaded sentences. The first sentence states the main action, the second specifies the output, and the third provides a key distinction. No unnecessary words.

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

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

For a simple single-parameter tool with comprehensive annotations and no output schema needed, the description is complete. It explains the input, the output fields, and the live nature. No gaps are apparent.

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 clear description of the 'callsign' parameter. The description repeats the example and meaning but does not add new semantic information beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool tracks a flight by callsign and returns live ADS-B position data. It specifies the exact fields (lat/lon, altitude, speed, heading) and distinguishes from schedule lookups. The verb 'track' and resource 'flight by callsign' are explicit.

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

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

The description provides an example and notes it returns live position, not a schedule lookup, implying when to use. However, it does not explicitly mention when not to use this tool or compare it to alternatives like 'get_aircraft' or 'military_aircraft'. The guidance is implicit but insufficiently detailed.

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 destructiveHint=true and readOnlyHint=false. The description confirms deletion by key but adds no additional behavioral context beyond what annotations and schema provide. Adequate but no extra value.

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

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

Two concise sentences with no wasted words, front-loading the core action and usage guidance.

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

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

For a single-parameter tool with full annotation coverage and no output schema, the description provides complete context: purpose, when to use, and related tools.

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

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

Schema coverage is 100% and the description's 'Memory key to delete' adds no new meaning beyond the schema's 'Memory key to delete'. Baseline 3 is appropriate.

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

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

The description clearly states 'Delete a previously stored memory by key' with a specific verb (delete) and resource (memory by key), distinguishing it from siblings like remember and recall.

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

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

Explicitly provides when to use the tool: when context is stale, task is done, or to clear sensitive data. Also advises pairing with remember and recall, giving clear context and 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 indicate read-only, idempotent, non-destructive. Description adds details about fetching, extracting title/description/links, and output format, which provides useful context without contradicting annotations.

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

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

Three focused sentences front-load the main action and provide essential details. No filler or 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?

Covers input, process, output format, and use cases. No output schema exists, but description explains output sufficiently.

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

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

Schema coverage is 100%, so parameters are already described. Description references url and max_links but adds no new meaning beyond the schema.

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

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

Description clearly states it generates an llms.txt file for any URL, specifies the process (fetch, extract, emit), and distinguishes from unrelated sibling tools.

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

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

Explicitly lists three use cases (client indexing, personal project, competitor audit). Could be improved by stating when not to use, but sibling differentiation is clear.

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

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

Annotations already indicate read-only, idempotent, open-world behavior. The description adds valuable context: it returns real-time ADS-B position, not schedules, and only if the aircraft is currently transmitting. This helps the agent understand data freshness and potential null responses beyond what annotations provide.

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

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

The description is two sentences with zero filler. The first sentence states the purpose and gives an example; the second clarifies the output type and timeliness. Every word earns its place, and the most critical information is 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?

With no output schema, the description sufficiently explains the return fields (lat/lon, altitude, speed, heading) and the condition for data availability. It could mention error handling for invalid hex codes, but for a simple lookup tool with one parameter, the coverage is adequate and leaves 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% and the parameter description already includes the hex format and example. The description repeats this same information without adding new semantics. Since the schema carries the full burden, a baseline score of 3 is appropriate – the description adds no extra meaning.

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

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

The description clearly states the verb 'track' and the resource 'specific aircraft by its 24-bit ICAO hex address', with an example. It explicitly contrasts with sibling tools by specifying the identifier type (hex vs callsign vs location), making it easy for an agent to select this tool when a hex address is available.

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 this tool (when you have an ICAO hex address and want live position), and the context of sibling tools like aircraft_near and find_by_callsign provides implicit guidance. However, it does not explicitly state when NOT to use it or list alternatives, so it falls short of a perfect score.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds context about real-time tracking via ADS-B and output fields, but does not disclose additional traits such as data limits or update frequency.

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

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

Two concise sentences: first states the tool's purpose, second describes the output. No wasted words; 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?

For a parameterless tool with good annotation coverage, the description sufficiently explains what the tool does and what data it returns. Could optionally mention pagination or result limits, but not essential.

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

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

Tool has zero parameters, so the description carries minimal param burden. It adds value by specifying the output fields (lat/lon, altitude, speed, heading), compensating for the lack of an output schema.

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

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

Description clearly states the tool returns live military/government aircraft positions via ADS-B. It uses a specific verb ('Returns') and resource ('military and government aircraft'), and distinguishes from sibling tools like 'aircraft_near' and 'get_aircraft' by specifying the government/military 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?

Description implies usage for retrieving live military aircraft data but provides no explicit when-to-use or when-not-to-use guidance. No alternatives are mentioned despite siblings like 'aircraft_near' or 'find_by_callsign' existing.

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

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

Beyond annotations (all false), the description reveals key behavioral traits: free, doesn't count against tool-call quota, rate-limited to 5 per identifier per day, team reads digests daily, and signal affects roadmap. 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?

Single efficient paragraph: purpose, when-to-use, how-to-phrase, team process, constraints. Front-loaded with the core action. No unnecessary words.

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

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

Fully covers purpose, usage, parameters, and behavioral context. No output schema needed; description is complete for a feedback submission tool.

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

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

Schema coverage is 100%, so parameters are well-defined. The description adds extra value by elaborating on the 'type' enum options and providing context on how to phrase the message, but does not introduce new semantic details beyond what the schema already conveys.

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 verb 'Tell' and the resource 'Pipeworx team', and lists specific use cases (bug, feature, data_gap, praise). It clearly distinguishes from sibling tools, which are mostly data retrieval/action 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?

Provides explicit when-to-use guidance for each feedback type, including examples like 'wrong/stale data (bug)' and 'tool you wish existed (feature/data_gap)'. Also gives formatting instructions (describe in terms of Pipeworx tools/packs) and notes rate limits and quota exemption.

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

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

Annotations already mark it as read-only, idempotent, and non-destructive. The description adds significant extra context: it's self-aggregating from CF analytics, no PII, and cached 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?

The description is two sentences plus a concise bullet list, front-loaded with the core function. Every sentence adds value—no filler or tautology.

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 one optional enum parameter and no output schema, the description is fully complete. It covers purpose, use cases, data source, privacy, and caching—leaving no gaps for the agent.

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

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

Schema coverage is 100%, so the description isn't required to explain parameters heavily. However, it adds semantic guidance ('shorter windows surface what's hot right now; longer windows show steady-state demand'), which aids selection.

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, packs, and call volume, with a clear verb-resource pairing and time window. It distinguishes itself from siblings like 'discover_tools' and 'ask_pipeworx' by focusing on trending data.

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

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

The description lists three concrete use cases (hot data sources, canonical tool confirmation, alignment checking) and mentions caching behavior. It does not explicitly state when not to use it, but the context is clear 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 indicate safe, read-only, idempotent behavior. Description adds depth: details on Jaccard similarity threshold, partition filter, and response structure (gap_pp, suggested_trade, etc.). 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?

Somewhat lengthy but well-structured: requirement first, then modes, then technical details. Front-loaded with key information. Could be slightly more concise while retaining substance.

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?

High complexity handled thoroughly: covers both modes, prerequisites, filters, and response format. No output schema but description compensates. Complements annotations and schema well.

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 description adds significant value: explains event slugs, URL acceptance, seed questions for topics, and the semantic anchor. Goes beyond schema documentation.

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 finding arbitrage opportunities via monotonicity violations and partition-sum checks. Differentiates between single-event and cross-event modes, providing specific examples of inputs.

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

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

Explicitly requires one of `event` or `topic` and warns that calling with no args fails. Recommends when to use each mode and explains the scenarios where cross-event mode catches patterns single-event misses.

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

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

The description goes far beyond annotations by detailing the three model segments, edge calculation, caching behavior (1h KV cache), diagnostic outputs, and conditions for skipping opportunities. It discloses limitations like unreliable Fed signals and the 'Market moved' warning.

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 purpose and structured into clear segments, response fields, and knobs. However, it is verbose with technical details (e.g., exact model parameters) that could be streamlined without losing clarity. Overall, it earns its length but has room for brevity.

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

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

Given no output schema, the description thoroughly explains the return structure: top-level fields by_segment, fed_candidates/fed_note, and _diagnostics. It also lists edge fields for each opportunity. All relevant aspects are covered for agent decision-making.

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 each parameter is documented. The tool description adds context for the 'knobs' like min_liquidity and max_spread_pp, explaining their purpose in filtering tradeable edges. This adds value beyond the schema's parameter descriptions.

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

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

The description clearly states the tool scans Polymarket markets to find opportunities where Pipeworx data disagrees with market price, and it is built for daily betting decisions. It distinguishes itself from sibling tools like polymarket_arbitrage by specifying its unique data source and model families.

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

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

The description explains the tool's use case ('what should I bet on today') and mentions that it surfaces opportunities without manual paging. However, it does not explicitly compare itself to sibling tools or state when to use alternatives, which would strengthen 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 include readOnlyHint, idempotentHint, and destructiveHint. The description adds extensive behavioral details: compatibility warnings (two cases), temporal alignment, skipped comparison counters, and the note that most pre-mapped topics are not tradeable. 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 dense but effectively front-loaded with the main purpose. It could be slightly more concise given the length, but the detail justifies it 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?

No output schema, so the description fully explains return values (leg-by-leg prices, spread array, safety fields, temporal_alignment). All behavioral nuances are covered, making the tool's response predictable.

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

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

Schema coverage is 100% with descriptions for all parameters. The description adds context about modes and parameter usage (e.g., listing predefined topics and examples). It 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's purpose as a cross-venue spread calculator between Kalshi and Polymarket for the same resolving question. It distinguishes from siblings (no other spread tool in sibling list) and explains the two modes.

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

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

The description explains when to use each mode (topic shortcuts vs explicit pairings) and warns that pre-mapped topics may not be tradeable. It does not explicitly state when not to use it, but the context is clear and provides practical guidance.

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

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

Annotations already indicate read-only and idempotent; description adds value by explaining scoping to identifier and pairing with remember/forget, reinforcing safety without contradicting annotations.

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

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

Two sentences with front-loaded purpose, clear examples, and no wasted words. 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 one optional parameter and annotations present; description covers return behavior (value or list), scoping, and usage pattern, making it fully complete for agent invocation.

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

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

Schema coverage is 100% and schema property description already covers the behavior of omitting key to list all. Description does not add new detail beyond what schema provides.

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

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

Clearly states verb 'retrieve' and resource 'a value previously saved via remember', and distinguishes from siblings by naming '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?

Provides clear context for use (look up stored context without re-deriving) and scoping, but does not explicitly exclude cases or mention alternatives beyond the paired tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds context: parallel fan-out to multiple sources, rate-limit handling, window semantics, and return format details. No contradiction with annotations.

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

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

The description is a single dense paragraph that is front-loaded with example queries. Every sentence adds information with no redundancy. Could be slightly more structured (e.g., bullet points) but remains efficient.

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

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

Despite no output schema, the description explains the return structure (changes[] grouped by source, total_changes count, pipeworx:// URIs). Combined with annotations and schema, 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?

Schema coverage is 100%, but description adds value: explains 'type' is only 'company', 'since' accepts ISO date or relative shorthand with examples and a suggested default ('30d' or '1m'), and 'value' can be ticker or CIK.

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 uses specific verbs ('returns change feed') and identifies the resource ('company') and data sources (SEC EDGAR, GDELT→GNews, USPTO). It distinguishes from the sibling tool 'entity_profile' by contrasting dynamic vs static profile.

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

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

Explicitly lists example queries ("What's new with X", etc.) and provides when-not-to-use guidance: 'Use entity_profile instead when you want the static profile...' Also explains fallback behavior (GDELT→GNews) and soft-fail for USPTO.

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 non-readOnly (write), non-destructive, idempotent. The description adds valuable behavioral context: scope by identifier, persistence differences (authenticated vs anonymous sessions). 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 (about 5 sentences) and front-loaded with the core purpose. It is well-structured, covering purpose, usage, pairing, and behavior without unnecessary detail.

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

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

Given the tool's simplicity (2 parameters, no output schema), the description covers all necessary aspects: what it stores, how it scopes, persistence, and related tools. It is complete for an agent to select 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?

Schema coverage is 100% with descriptions for both key and value. The description adds examples of valid keys (e.g., 'subject_property', 'target_ticker') and clarifies that value can be any text. This goes beyond the schema, justifying an above-baseline 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?

The description clearly states the tool's purpose: 'Save data the agent will need to reuse later'. It uses a specific verb ('save') and resource ('data'), and provides examples of use cases (ticker, address, preference). It also explicitly distinguishes from siblings 'recall' and 'forget'.

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

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

The description gives explicit guidance on when to use: 'when you discover something worth carrying forward' and what kinds of data (resolved ticker, target address, etc.). It also tells when not to use by suggesting alternatives: 'Pair with recall to retrieve later, forget to delete.'

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: it mentions cascading through several lookup endpoints internally and replacing 2-3 manual lookups, which goes beyond annotation data.

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 detailed but well-structured, with examples and bullet-like formatting for supported types. Every sentence contributes useful information without redundancy, though it could be slightly 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?

No output schema, so the description fully explains return values for each entity type, including citation URIs. It covers supported types, input formats, and internal behavior (cascading lookups), making it complete for a lookup tool with two parameters.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining exactly what 'value' can be (ticker, CIK, name for company; brand or generic name for drug) and what 'type' choices entail, including return details and auto-disambiguation.

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 resolves user-spoken names to canonical/official identifiers, with specific examples like ticker, CIK, and RxCUI. It distinguishes itself from sibling tools by being the first step for name-to-ID needs.

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,' providing clear context for when to use. It gives examples of queries that trigger this tool but lacks explicit when-not-to-use or alternatives, though 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?

Annotations declare readOnly and idempotent; description adds that it probes each entity with ai_visibility_check, ranks by score, and returns score, confidence, signal density. 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, every sentence adds new information. No fluff, highly efficient.

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

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

With 4 parameters and no output schema, description covers inputs and outputs (ranked list with score, confidence, signal density). Sibling tools provide additional context. Complete for intended use.

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

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

Schema coverage is 100% but description adds crucial meaning: first entity is 'subject', models default to workers-ai, _apiKey only for anthropic, context disambiguates. Adds value beyond schema.

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

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

Description uses specific verb 'compare' and resource 'AI visibility across multiple entities'. It clearly distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (general comparison) by detailing the process and output format.

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 use case: 'competitive AI-marketing audits'. Provides example question. Does not explicitly state when not to use or name alternatives, but sibling context implies ai_visibility_check for single entity.

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 behavioral traits beyond annotations, such as graceful degradation with partial failures and a timeout of 5-30s for bundlephobia's first measurement. It also details what the return includes, adding context beyond the readOnly and idempotent hints.

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 informative and front-loaded with the main purpose. Every sentence adds value, though it could be slightly tighter. Still, it is well-structured and avoids fluff.

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

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

Given the tool's complexity and the absence of an output schema, the description provides a complete picture: what it returns (summary block fields, advisory details, links, alternatives), partial failure behavior, and ecosystem limitations. An agent can fully utilize the tool based on this description.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by clarifying that scoped packages are accepted and explaining the default behavior for the version parameter. This extra context justifies a score of 4.

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

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

The description clearly states the tool's purpose: a composite check for npm packages covering safety, popularity, and size. It uses specific verbs and resources, and distinguishes itself from sibling tools by being the only npm dependency scanner.

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 tells when to use the tool: when an agent asks about package safety, popularity, or size. It also provides exclusions (NPM only; other ecosystems use deps.dev:version directly), guiding the agent to 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, destructiveHint. Description adds behavioral details: returns verdict, structured form, actual value with citation, and percent delta. Also mentions it replaces multiple sequential calls, providing richer 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 front-loaded with usage patterns and examples. It is slightly verbose but every sentence adds value. Structure is logical: examples, usage, scope, output, benefits.

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 compensates by listing return types (verdict, structured form, actual value, citation, delta) and specifying backend (SEC EDGAR+XBRL) and domain constraints (public US company financials). Complete for agent to understand tool capabilities.

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

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

Schema coverage is 100% with parameter description. Description adds examples and specifies the type of claims supported (natural-language factual claim, e.g., 'Apple's FY2024 revenue...'), enhancing understanding beyond schema.

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

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

The description clearly states the tool verifies natural-language claims against authoritative sources, specifically company-financial claims, using examples like 'Is it true that...' and 'fact check'. It distinguishes from siblings by focusing on factual verification of financial 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?

Explicitly says 'Use whenever the agent needs to check factual correctness' and gives domain scope (company-financial claims). No explicit when-not-to-use, but clear context implies appropriateness and differentiates from non-fact-checking siblings.

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