musicurainz

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

Annotations (readOnlyHint, idempotentHint) are consistent. Description adds value by detailing return structure ({score, confidence, signals, raw_response} + combined view) and explaining the _apiKey passthrough. 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?

Description is highly concise with 4 sentences covering purpose, default behavior, return structure, and use cases. No redundant or irrelevant information. Front-loaded with the core action and result.

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 4 parameters, no output schema, and no nested objects, the description adequately covers purpose, usage, and return format. It lacks detail on scoring methodology or 'signals' content, but provides enough for an AI agent to understand and invoke the tool correctly.

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

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

Input schema has 100% coverage for all 4 parameters. Description adds context: default model for 'models' parameter, and clarifies that _apiKey is passed directly to Anthropic. This additional explanation elevates understanding 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?

Description explicitly states the verb 'probe' and resource 'LLMs' with the specific purpose of scoring visibility (0-100) per model. The tool is clearly distinguished from siblings like 'scan_competitor_ai_presence' by focusing on multiple LLM probing and 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?

Provides clear guidance on default model (Workers AI Llama-3.3-70b) and how to probe Anthropic via _apiKey. Lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring). Does not explicitly state when not to use or mention alternative tools, 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?

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool picks the right data source and fills arguments automatically, handles natural language questions, and returns results. However, it lacks details on limitations (e.g., rate limits, error handling, or specific data sources), which slightly reduces transparency.

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

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

The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by key capabilities, and ends with concrete examples. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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

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

Given the tool's complexity (natural language processing with automatic tool selection) and no output schema, the description is mostly complete: it explains the input, process, and result. However, it could improve by mentioning output format or potential limitations, but the examples partially compensate for this gap.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by explaining the parameter's purpose beyond the schema: it specifies 'question' should be in 'plain English' or 'natural language' and provides examples like trade deficits or adverse events, enhancing understanding of expected input format and content.

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: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and distinguishes from siblings by emphasizing natural language input without needing to browse tools or learn schemas. The examples further clarify the 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 explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It provides clear alternatives (implicitly suggesting other tools for structured queries) and includes practical examples to guide usage, making it easy to distinguish from sibling tools like search_artists or get_release.

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

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

The description adds substantial behavioral context beyond annotations: it describes the internal fan-out process (resolving market, classifying bet, selecting packs) and the output format. No contradiction with annotations (readOnlyHint=true implies read-only, which aligns with 'pull 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 a single dense paragraph, but every sentence adds value. It front-loads the purpose and efficiently conveys inputs, process, use cases, and product benefit. Could be slightly more structured, but not overly verbose.

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

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

Given the tool's complexity, the description covers inputs (with parameter details), internal fan-out process, output (evidence packet + comparison), and use cases. Annotations handle read-only and idempotency. No output schema is needed as the description explains the return.

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 enhances this by explaining that 'market' accepts slug, URL, or question text, and 'depth' controls thoroughness. It adds practical usage examples that clarify parameter semantics.

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

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

The description explicitly states the tool researches a Polymarket bet by pulling Pipeworx data. It specifies input types (slug, URL, question) and outputs (evidence packet, comparison). It is clearly distinct from sibling tools like ask_pipeworx or compare_entities, which serve different purposes.

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

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

The description provides clear use cases ('should I bet on X?', 'what does the data say?', 'is there edge?') and explains that agents using this tool convert better. It does not explicitly mention when not to use it or compare to alternatives, 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?

No annotations are provided, so the description must cover behavioral traits. It discloses that it returns paired data with resource URIs and lists specific fields per entity type. However, it does not mention read/write nature, error handling, or rate limits.

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

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

The description is concise with three sentences that efficiently convey purpose, inputs, and output benefit. No redundant words, and key 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?

Given no output schema, the description adequately covers the return type (paired data + URIs) and data fields per type. However, it lacks details on potential errors, pagination, or format specifics, which could be needed for a comprehensive 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% and the description adds value by providing examples for each parameter (e.g., '["AAPL","MSFT"]' for company, '["ozempic","mounjaro"]' for drug), clarifying usage beyond the schema.

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

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

The description clearly states the tool compares 2-5 entities side by side, specifying different data fields for 'company' and 'drug' types. It distinguishes from sibling tools which are entity lookups (e.g., get_artist, resolve_entity) by offering batch comparison.

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

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

The description mentions it replaces 8-15 sequential calls, implying efficiency for multi-entity comparisons, but does not explicitly state when not to use it or provide alternatives. No guidance on prerequisites or limitations.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it performs a search based on natural language queries and returns relevant tools. However, it doesn't mention potential limitations like rate limits, authentication requirements, or error conditions that would be helpful for comprehensive transparency.

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

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

The description is perfectly concise with two sentences that each earn their place. The first sentence explains the core functionality, and the second provides crucial usage guidance. There's no wasted language or redundancy, and the information is front-loaded effectively.

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 moderate complexity (search functionality with 2 parameters), no annotations, and no output schema, the description does well but has some gaps. It clearly explains the tool's purpose and when to use it, but doesn't describe what the output looks like (beyond 'most relevant tools with names and descriptions') or address potential search limitations or result formats.

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 schema already documents both parameters thoroughly. The description doesn't add any additional parameter semantics beyond what's in the schema. It mentions the tool accepts 'describing what you need' which aligns with the query parameter, but provides no extra details about parameter usage or constraints.

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

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

The description clearly states the tool's purpose with specific verbs ('search', 'returns') and resources ('Pipeworx tool catalog', 'most relevant tools with names and descriptions'). It distinguishes this from sibling tools like get_artist or search_artists by focusing on tool discovery rather than specific data retrieval operations.

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 this tool: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear context about the scale of tool availability and the primary use case for discovery versus direct tool invocation.

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

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

Discloses return format (pipeworx:// URIs) and efficiency benefit over sequential calls. No annotations provided, so description carries full burden; it adequately covers the read-only nature and bundling behavior.

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

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

Four sentences with front-loaded purpose, but the sentence about replacing calls could be integrated without losing clarity. Still effective and 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 complexity and no output schema, description provides sufficient detail on sources and output format. Minor gap: no mention of error handling or pagination, but adequate for agent selection.

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

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

Adds meaning beyond schema: explains value can be ticker or CIK, names not supported, and type enum limitation. Schema coverage is 100% but description still enriches 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 it provides a full profile across multiple packs, lists specific data sources, and distinguishes from sibling tool usa_recipient_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 advises when not to use (federal contracts) and offers alternative, plus mentions to use resolve_entity for name inputs.

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

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

No annotations are provided, so the description carries the full burden. It states 'Delete' implies a destructive mutation, but doesn't disclose behavioral traits such as whether deletion is permanent, requires specific permissions, returns confirmation, or handles errors for non-existent keys. This leaves significant gaps for a mutation tool.

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, clear sentence with zero waste—it directly states the tool's action and target. It's appropriately sized and front-loaded, making it easy to parse quickly.

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 destructive tool with no annotations and no output schema, the description is incomplete. It lacks critical context such as what happens post-deletion (e.g., confirmation, error handling), how it interacts with sibling tools, or any side effects. This is inadequate for safe and effective use by 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?

The schema description coverage is 100%, with the parameter 'key' documented as 'Memory key to delete'. The description adds no additional meaning beyond this, as it merely restates the parameter's purpose without details like key format or examples. Baseline 3 is appropriate since the 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?

The description clearly states the action ('Delete') and the resource ('a stored memory by key'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'recall' or 'remember', but the verb 'Delete' suggests a distinct destructive operation versus retrieval or creation.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing memory key), exclusions, or how it relates to siblings like 'recall' (which likely retrieves memories) or 'remember' (which likely creates them).

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

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

Annotations already indicate readOnly, idempotent, non-destructive behavior. The description adds value by detailing the process: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' This enriches the agent's understanding beyond annotations, but does not contradict them.

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

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

The description is two sentences, front-loaded with purpose, and efficiently covers process and use cases without extraneous information. Every sentence contributes meaningfully.

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 key aspects: purpose, process, output format, and intended use cases. It does not mention error handling or limitations, but given the simple tool nature and strong annotations, it is sufficiently complete 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 both parameters are well-documented in the schema. The description does not add new semantic details beyond what the schema provides (e.g., default and max for max_links are already in the schema). Thus it meets the baseline but does not exceed 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 verb 'Generate' and the resource 'llms.txt file for any URL', specifying the purpose for AI crawlers like ChatGPT, Claude, Perplexity. It is distinct from sibling tools like ai_visibility_check or scan_competitor_ai_presence, which focus on different aspects.

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 concrete use cases: getting a client's site indexed, drafting for own project, auditing competitor AI visibility. However, it lacks explicit guidance on when not to use this tool or comparisons to alternative tools, which would elevate it to a 5.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description mentions retrieving 'detailed information' and 'release list' but doesn't specify what that includes (e.g., biography, genres, images), whether it's a read-only operation, potential rate limits, error conditions, or response format. For a tool with no annotations, this leaves significant behavioral gaps.

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 appropriately concise with two sentences that are front-loaded: the first states the purpose, and the second provides usage guidance. There's no wasted text, and each sentence adds value. It could be slightly more structured by explicitly separating purpose from prerequisites.

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 moderate complexity (single parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and a prerequisite but lacks details on behavioral aspects (e.g., what 'detailed information' entails, error handling) and doesn't leverage the absence of annotations to provide richer context. It meets the minimum viable threshold but has clear 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?

The schema description coverage is 100% (the single parameter 'id' is documented as 'MusicBrainz artist ID (UUID)'), so the baseline is 3. The description adds minimal value beyond the schema by specifying 'Use the MusicBrainz ID from search_artists,' which provides context on where to obtain the ID but doesn't elaborate on parameter semantics like format constraints or usage 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: 'Get detailed information about an artist including their release list.' It specifies the verb ('Get'), resource ('artist'), and scope ('detailed information... including their release list'). However, it doesn't explicitly differentiate from sibling tools like 'get_release' or 'search_artists' beyond mentioning the ID source.

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 implied usage guidance: 'Use the MusicBrainz ID from search_artists.' This suggests a workflow dependency but doesn't explicitly state when to use this tool versus alternatives like 'search_artists' for finding artists or 'get_release' for release details. No explicit when-not-to-use or alternative scenarios are mentioned.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what the tool returns ('detailed information about a release including its full track listing') but doesn't mention potential limitations like rate limits, authentication requirements, error conditions, or response format. The description adds basic context but lacks comprehensive behavioral details.

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

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

The description is extremely concise with just two sentences that each serve a clear purpose: the first states what the tool does, and the second provides usage guidance. There's zero wasted language, and it's appropriately front-loaded with the core functionality.

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 moderate complexity (single parameter lookup), 100% schema coverage, but no output schema or annotations, the description is minimally adequate. It explains what information is returned but doesn't describe the response structure or format. For a tool that returns 'detailed information,' more context about the output would be helpful since there's no output schema.

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, with the single parameter 'id' documented as 'MusicBrainz release ID (UUID).' The description adds minimal value beyond this by mentioning 'Use the MusicBrainz ID from search_releases,' which provides usage context but no additional parameter semantics. This meets the baseline of 3 when schema coverage is high.

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

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

The description clearly states the tool's purpose: 'Get detailed information about a release including its full track listing.' It specifies the verb ('Get'), resource ('release'), and scope ('detailed information including full track listing'). However, it doesn't explicitly distinguish this from sibling tools like search_releases, which appears to be a search function rather than a detailed lookup.

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

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

The description provides clear context for when to use this tool: 'Use the MusicBrainz ID from search_releases.' This implies it should be used after obtaining an ID from the search_releases tool. However, it doesn't explicitly state when NOT to use it or mention alternatives like get_artist for artist information.

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 no annotations provided, the description discloses a rate limit (5 messages per day per identifier) and a prohibition on including the end-user's prompt. This adds useful behavioral context beyond the bare 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?

Three concise sentences, front-loaded with purpose, then usage constraints, then a key limitation. No unnecessary words 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?

For a simple feedback tool with no output schema, the description covers purpose, content guidelines, and a behavioral constraint (rate limit). It is fully adequate for the 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 all parameters have clear descriptions. The description adds value by clarifying the expected content of 'message' (be specific, 1-2 sentences) and the rate limit, 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 the tool is for sending feedback and enumerates specific use cases (bug reports, feature requests, missing data, praise), differentiating it from sibling tools which are query/entity-focused.

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 explains when to use (feedback submission) and what to include (what was tried, not end-user prompt). It does not explicitly mention when not to use or compare to alternatives, but context from sibling tools makes it 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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds significant value by revealing the data source (CF analytics-engine), privacy guarantee (no PII), and caching behavior (5min-1h). 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 concise—around four sentences—with a clear front-loaded purpose statement, then use cases, then technical details. No waste; every sentence adds value. The use of numbered items enhances 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?

Given the tool's low complexity (one parameter, no output schema, no nested objects), the description is complete. It covers what is returned, use cases, parameter guidance, data source, and caching. It could optionally specify the return format, but that is not essential 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 the 'window' parameter having an enum and description. The description reinforces the enum values and adds nuance: 'shorter windows surface what's hot right now; longer windows show steady-state demand.' This adds interpretive 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 returns top tools, packs, and total call volume over a recent window. The verb 'returns' and specific resource listing make the purpose explicit. It distinguishes from siblings like 'discover_tools' by focusing on trending/aggregated signal.

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 use cases: discovering hot data sources, confirming canonical tools, and checking alignment. It also provides guidance on window selection (shorter for hot, longer for steady-state). No explicit exclusions or alternatives mentioned, 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 indicate readOnlyHint=true and non-destructive nature. The description adds operational transparency by explaining the monotonicity check and that it returns ranked opportunities with trade direction and reasoning. No contradictions or hidden 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 a single paragraph but well-organized: starts with core purpose, then lists two modes with clear labels and explanations. It is informative without being verbose, though slightly more structured presentation could improve scannability.

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 mentions the return type (ranked opportunities with reasoning), which is helpful. It does not detail output format or error handling, but given the tool's read-only nature and annotations, this is adequate for most use cases.

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 coverate is 100% with both parameters documented. The description adds significant value by explaining the purpose of each mode, providing example inputs, and clarifying the situational logic behind choosing one over the other, which goes beyond the schema's basic type 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 finds arbitrage opportunities on Polymarket via monotonicity checks. It distinguishes two modes (event and topic) and provides a specific use case, differentiating itself from sibling tools like polymarket_edges which likely focus on other aspects.

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

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

The description explicitly explains when to use each mode: event for a single event's child markets, topic for cross-event arbitrage. It gives a concrete example of why topic mode is needed when single-event mode misses cutoff comparisons, providing clear guidance on mode 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?

The description details the algorithm: scans top markets, groups by asset, fetches price history once per asset, computes model probability from FRED and coinpaprika, ranks by edge. It also explains the output structure. Annotations (readOnlyHint, openWorldHint) are consistent and the description adds rich behavioral context beyond annotations.

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

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

The description is well-structured in three paragraphs with front-loaded purpose. It is reasonably concise (about 120 words) and every sentence adds value, though minor tightening could be possible.

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

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

Given the tool's complexity (3 simple parameters, no output schema, no nested objects), the description is complete. It explains the algorithm, data sources, output format (edge magnitude and trade direction), and the intended use case. No gaps for an agent to invoke correctly.

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

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

The input schema has 100% description coverage for all three parameters (limit, window, min_edge_pp). The description does not add new meaning beyond what the schema already provides, 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 is very specific: it scans Polymarket markets and returns those with greatest disagreement between Pipeworx model and market price. It clearly states the domain (crypto-price bets), the process, and the output (top N edges with trade direction). This distinguishes it from sibling tools like polymarket_arbitrage or bet_research.

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

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

The description explicitly states it is built for the 'what should I bet on today' question and helps discover opportunities without manual paging. This gives clear context for when to use it, though it does not explicitly mention when not to use or list alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds that the spread typically ranges 2-25pp, describes output format (leg-by-leg prices in 0-1, spread in percentage points), and mentions this is a real arb signal, providing context beyond annotations.

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

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

The description is well-structured, front-loading the purpose and then detailing modes and output. It could be slightly more concise but is clear and organized.

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 output: leg-by-leg prices and spread. It covers all three parameters, explains topics list, and adds contextual value about arb signal.

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 meaning by explaining topics as pre-mapped macros and clarifying that kalshi_event_ticker and polymarket_event_slug override topic mapping, which 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 calculates cross-venue spreads between Kalshi and Polymarket. It uses specific verbs like 'cross-venue spread' and distinguishes from siblings like 'polymarket_arbitrage' by focusing on the same resolving question 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?

The description explains two modes (topic and explicit pairings) with clear examples, guiding when to use each. It does not explicitly state 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?

With no annotations provided, the description carries the full burden. It discloses that the tool retrieves or lists memories stored across sessions, which is useful behavioral context. However, it doesn't mention potential limitations like memory persistence, access controls, or error handling for invalid keys, leaving gaps for a tool with session-spanning functionality.

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 functionality in the first sentence and uses a second sentence to provide usage context. Every sentence earns its place with no wasted words, making it appropriately sized and efficient for understanding.

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 moderate complexity (retrieval with optional listing), no annotations, and no output schema, the description is adequate but incomplete. It covers the basic purpose and usage but lacks details on return values (e.g., format of retrieved memories or key lists) and doesn't address potential edge cases, leaving room for improvement in contextual coverage.

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 description coverage is 100%, so the baseline is 3. The description adds value by explaining the semantics of omitting the key parameter: 'omit to list all keys,' which clarifies the dual functionality beyond the schema's technical specification. This elevates the score above the 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: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' It specifies the verb ('retrieve'/'list') and resource ('memory'), but doesn't explicitly differentiate from sibling tools like 'remember' or 'forget' beyond mentioning retrieval vs. saving 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?

The description provides clear context on when to use it: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' It explains the key parameter behavior (omit to list all), but doesn't explicitly state when not to use it or name alternatives among siblings like 'discover_tools' or 'search_artists' for different retrieval needs.

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 no annotations provided, the description carries the full burden. It transparently describes the parallel fan-out behavior across three sources, input format details (ISO date, relative), and the return structure (structured changes, count, URIs). No contradictions or omissions about side effects are noted, though potential limitations (e.g., rate limits) are not mentioned.

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, with the first sentence immediately stating the purpose. Every sentence adds essential information: the fan-out behavior, parameter formats, and output summary. There is no redundancy or extraneous text, making it efficient for an AI agent to parse.

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 (parallel queries to multiple APIs), the description covers inputs, behavior, and output structure sufficiently. The absence of an output schema is compensated by listing return fields. However, it does not explain error scenarios or what happens if a source fails, which would be valuable for complete 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?

The input schema already provides descriptions for all three parameters (100% coverage). The description adds value by giving concrete examples (e.g., '7d', '1y') and clarifying that 'value' can be a ticker or CIK. This extra context helps an agent choose correct parameter values beyond the schema's minimal descriptions.

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

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

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It elaborates with specific sources (SEC EDGAR, GDELT, USPTO) and distinguishes from siblings like entity_profile. The verb 'fan out' and concrete examples make the purpose unmistakable.

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 recommends use cases: 'Use for "brief me on what happened with X" or change-monitoring workflows.' This provides clear guidance on when to invoke the tool. However, it does not explicitly state when not to use it or contrast with alternatives like entity_profile, which would have been helpful.

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 no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the persistence mechanism (session memory), differences between authenticated (persistent) and anonymous (24-hour) sessions, and the tool's purpose for cross-call context. However, it doesn't mention potential limitations like storage capacity or key constraints.

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

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

The description is perfectly concise with two sentences that each earn their place: the first states the core functionality, the second adds crucial behavioral context about persistence. It's front-loaded with the main purpose and wastes no 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 tool with no annotations and no output schema, the description provides strong context about the tool's behavior, persistence model, and use cases. It adequately compensates for the lack of structured metadata, though it doesn't specify return values or error conditions, which would be helpful given the absence of an output schema.

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 schema already fully documents both parameters. The description adds minimal value beyond the schema by mentioning example key names ('subject_property', 'target_ticker', 'user_preference') and value types ('any text — findings, addresses, preferences, notes'), but doesn't provide additional semantic context beyond what's in the 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's purpose with specific verbs ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from siblings like 'recall' (retrieve) and 'forget' (remove). It explicitly identifies the storage mechanism and target location.

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 this tool ('save intermediate findings, user preferences, or context across tool calls') and distinguishes it from alternatives by specifying the persistence behavior for authenticated vs. anonymous users, helping the agent choose between this and other memory-related 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?

No annotations provided, so description carries full burden. It describes inputs and outputs (ticker, CIK, name, URIs) but does not disclose side effects, auth requirements, rate limits, or explicitly confirm read-only behavior. The behavioral context is adequate but not exhaustive.

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: the first states the purpose, the second details version, inputs, and outputs. No wasted words, and the key 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?

For a simple lookup tool with 2 parameters and no output schema, the description covers inputs, outputs, and version limitations. It lacks error conditions or edge cases but is fairly complete for its 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% with both parameters described. The description adds practical examples (e.g., 'AAPL', '0000320193', 'Apple') and explains the return value context, providing meaning beyond the schema.

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

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

The description clearly states the tool resolves entities to canonical IDs, gives a specific example for type='company', and lists accepted input formats (ticker, CIK, name). It distinguishes from siblings by noting it replaces 2-3 lookup calls.

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

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

The description specifies that v1 supports only type='company' and implies usage for canonical ID lookup. It says it replaces multiple calls, but does not explicitly state when to avoid using it or mention alternative tools.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds operational details: uses ai_visibility_check, returns score/confidence/signal density, and notes API key requirement. 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?

Four sentences cover purpose, mechanism, use case, and output. No redundant information; each sentence adds distinct value.

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

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

Despite lacking an output schema, description adequately describes returns (ranked list with score/confidence/signal density). Coverse the algorithmic flow and parameter roles. At 4 params and 100% schema coverage, 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 has 100% description coverage, but description adds critical nuance: first entity is the 'subject', rest are competitors; explains default model and API key condition. These details exceed 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?

Description clearly states the verb (compare, probe, rank, surface) and resource (AI visibility across multiple entities). It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison).

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

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

Description provides a concrete use case for competitive AI-marketing audits. Implicitly guides when to use this over ai_visibility_check, but does not explicitly mention when not to use or list alternatives.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the database source ('MusicBrainz') but does not cover key behavioral traits such as rate limits, authentication needs, error handling, or response format. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond its basic function.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action and resource, making it easy to understand quickly. Every part of the sentence contributes to clarifying the tool's function.

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

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

Given the tool's moderate complexity (search function with 2 parameters), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and data source but lacks details on behavioral aspects, error cases, or result structure. Without annotations or output schema, more context would be beneficial for full completeness.

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 input schema already documents both parameters ('query' and 'limit') with descriptions. The description adds minimal value beyond the schema by implying the query is for artist names, but does not provide additional syntax, format details, or usage examples. This meets the baseline for high schema coverage.

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: 'Search for music artists by name using the MusicBrainz database.' It specifies the verb ('Search'), resource ('music artists'), and method ('by name'), but does not explicitly differentiate it from sibling tools like 'search_releases' beyond the resource type. This makes it clear but not fully sibling-distinctive.

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

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

The description implies usage context by mentioning 'by name' and 'MusicBrainz database,' but does not provide explicit guidance on when to use this tool versus alternatives like 'get_artist' or 'search_releases.' It lacks statements on when-not-to-use or direct comparisons, leaving usage somewhat inferred rather than clearly defined.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the search functionality without mentioning any behavioral traits such as rate limits, authentication needs, pagination, or what happens on no results (e.g., returns empty list). This is inadequate for a search tool that likely interacts with external 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 a single, efficient sentence: 'Search for albums and releases by title or query.' It is front-loaded with the core purpose and contains no unnecessary words, making it highly 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?

Given the complexity of a search tool with no annotations and no output schema, the description is incomplete. It lacks information on behavioral aspects (e.g., how results are returned, error handling) and doesn't compensate for the missing output schema. This leaves significant gaps for an agent to understand the tool's full 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?

The input schema has 100% description coverage, clearly documenting both parameters ('query' and 'limit'). The description adds no additional meaning beyond the schema, as it only mentions 'title or query' without explaining syntax or format. This meets the baseline score of 3 since the 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?

The description clearly states the tool's purpose: 'Search for albums and releases by title or query.' It specifies the verb ('search') and resource ('albums and releases'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'search_artists' beyond the resource type, which prevents a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_release' (which might fetch a specific release) or 'search_artists' (which searches for artists instead), nor does it specify contexts or exclusions for usage. This lack of comparative information leaves the agent without clear direction.

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

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

No annotations, so description carries full burden. It discloses use of SEC EDGAR + XBRL, return structure, and efficiency claim. Does not mention rate limits, auth, or potential errors, but sufficient for a fact-checking tool.

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

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

Three sentences, each adding value: purpose, domain/version, output details, efficiency note. No redundancy, 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, description explains return values (verdict, structured form, citation, delta). Covers core functionality but lacks error handling or limitations for non-financial claims.

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 the single parameter. Description adds examples and context beyond the schema description (e.g., 'Apple's FY2024 revenue'), enhancing 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 fact-checks a natural-language claim, specifies supported domains (company-financial), and lists outputs (verdict, citation, delta). This distinguishes it from siblings like 'compare_entities' or 'ask_pipeworx'.

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 indicates usage for company-financial claims and notes it replaces sequential agent calls, providing efficiency context. However, it does not explicitly state when not to use or mention alternatives.

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