Arcgis Charleston

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. Description adds valuable context: default model is free, Anthropic requires BYO key, and return structure includes per-model and combined views. 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?

Three sentences with no filler; front-loads purpose and outcome. Every sentence adds unique information relevant to selection and invocation.

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

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

Despite no output schema, description clearly states return format (per-model and combined view) and handles complexity of multiple models and optional parameters. Adequate for an AI agent to understand usage and output.

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

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

Schema coverage is 100% with descriptions for all 4 parameters. Description adds extra semantics: default model is Workers AI, _apiKey is for Anthropic, context disambiguates. This goes beyond what schema alone provides, earning above baseline.

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

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

Description uses specific verb 'probe' and resource 'LLMs' to clearly state it scores visibility for entities. It distinguishes from siblings by focusing on AI visibility scoring with specific model options.

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

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

Provides explicit use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to use _apiKey for Anthropic. However, lacks explicit guidance on when not to use compared to sibling tools like scan_competitor_ai_presence.

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 that it routes to 4,552 tools, returns structured answers with stable citation URIs, and acts as a default entry point. No contradictions, and it provides useful 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 relatively long but well-structured and front-loaded with the key preference statement. Each section adds value: scope, alternatives, examples, and step-up guidance. Could be slightly condensed but no wasted sentences.

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

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

Given no output schema, description explains return type (structured answer with citations) and mentions argument filling. Covers the broad question types and source coverage. Slight gap in exact return structure details, but sufficient 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 coverage is 100% with all parameters as aliases for 'question'. Description adds examples of natural language usage, but the schema already includes examples and descriptions. Minimal extra value beyond 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?

Description clearly states the tool routes questions to many tools/sources and returns structured answers with citations. Distinguishes from siblings like ask_pipeworx_grounded and deep_research with specific use cases. Examples illustrate 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?

Explicitly recommends this tool over web search, lists when to use alternatives (grounded for single authoritative answer, deep_research for broad multi-part), and instructs to start here for most questions. Provides clear when-to-use and when-not-to-use guidance.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable extra context: the extra LLM call cost, the exact refusal reasons, and the return format. However, it doesn't elaborate on all possible behavioral traits 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 front-loaded with key information and structured into clear sections. Every sentence adds value, though it could be slightly more concise. Overall well-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?

Given the tool's complexity (4,552 tools, 1160 sources) and lack of output schema, the description fully explains the mechanism, usage, and return formats (both success and refusal). It is complete for an AI 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%, so baseline is 3. The description does not add new semantics beyond stating that the question is in natural language. The schema already describes each alias, so no additional 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 explicitly states it is a 'Hallucination-resistant answer mode for high-stakes reads' and distinguishes it from ask_pipeworx for casual lookups. It clearly defines the tool's role versus its sibling.

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

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

Provides explicit guidance on when to use this tool (high-stakes reads, facts that must be direct) and when to prefer ask_pipeworx (casual lookups). Includes concrete examples like financial verdicts and legal claims.

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

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

Annotations already declare readOnlyHint and idempotentHint, but the description adds extensive behavioral details: fan-out logic, response shapes (market_match_confidence, parent_event, news fallback), safety short-circuits (low_confidence_match, market_closed_or_inactive), and resolution-rule risk. This far exceeds what annotations provide, with no contradictions.

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

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

The description is lengthy but well-organized into clear sections (classifiers, fan-out examples, response shapes, etc.) and front-loaded with the core purpose. Every sentence adds necessary detail for a complex tool; while not concise, it earns its length.

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 lack of an output schema, the description is highly comprehensive. It covers input formats, fan-out logic, response fields (market_match_confidence, parent_event, news fallback), safety mechanisms (low-confidence short-circuit, closed markets, wide spreads), and resolution-rule risk. No crucial gaps remain.

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

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

Schema coverage is 100% (all three parameters described), so baseline is 3. The description adds value by explaining accepted formats for 'market' (slug, URL, question text), the behavior of 'depth' (quick vs thorough), and the impact of 'include_raw' (response size control). This enriches the schema definitions.

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

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

The description states the tool's purpose clearly: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the verb (research), resource (Polymarket bet), and distinguishes from sibling tools like polymarket_edges and validate_claim by focusing on a single bet deep dive and providing use cases ('should I bet on X').

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 use cases ('Use for 'should I bet on X', 'what does the data say about Y', or 'is there edge in Z''), implying when to use. It does not explicitly state when not to use or name alternatives among siblings, but the context is clear and the examples guide the agent.

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 significant behavioral context beyond annotations: it reveals pulling from SEC EDGAR/XBRL and FAERS, handling off-calendar fiscal years, and returning paired data with citation URIs. Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, which are consistent. No contradictions.

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

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

The description is somewhat lengthy (multiple sentences) but every sentence contributes value. It is front-loaded with query examples, making it scannable. Minor redundancy might exist, but overall it is well-structured and informative.

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 (two entity types, multiple data sources), the description is fairly complete. It covers data sources, sorting, citation URIs, and efficiency. No output schema exists, but the description mentions returned data. It lacks details on pagination or error handling, but those are not critical for this 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?

While the schema covers 100% with descriptions, the description adds meaning by explaining what each type returns (e.g., revenue, net income for companies; adverse events for drugs) and the format for values (tickers/CIKs vs. names). This goes beyond the schema's basic field 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 performs side-by-side comparison of 2-5 companies or drugs in one parallel call. It provides concrete examples of queries ('compare X and Y', 'X vs Y', etc.) and explains the data pulled per entity type, making the purpose unmistakable and distinguishing it from sequential lookups.

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

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

The description explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' offering strong positive guidance. It also specifies the number of entities (2-5), data sources, and sorting behavior. However, it does not explicitly mention when not to use it or provide alternative tools, but the guidance is clear and actionable.

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 (readOnly, openWorld, idempotent), description discloses decomposition into facets, parallel routing, findings packet structure with gaps, and expected latency. 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?

Long but well-structured, front-loading critical info. Every sentence adds value, though minor redundancy in sibling mentions.

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, description covers account/plan, data sources, parallel execution, return format, and latency. No output schema but explains findings packet comprehensively.

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 both parameters at 100%, but description adds meaningful details: depth numeric values (quick=3, standard=5, thorough=8) and question being natural language and broad/multi-part.

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 'Grounded multi-source research across Pipeworx's 1160 STRUCTURED data sources' and contrasts with siblings like ask_pipeworx for single lookups.

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

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

Explicitly says when to use this tool vs. alternatives: 'For a single lookup use ask_pipeworx' and 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'. Also notes account requirements.

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, idempotentHint) already indicate safe, non-destructive behavior. The description adds that results are ready to call with full schemas and curated examples, enhancing transparency beyond annotations.

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

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

The description is a single paragraph but front-loaded with purpose and examples. It lists many domains, which is necessary for completeness, but could be slightly more structured. Very 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, but description fully explains return value: top-N tools with names, descriptions, and full schemas. The domain list and alias details make it self-contained and complete.

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

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

Input schema has 100% coverage with descriptions. The description adds meaning by explaining aliases (task, q, search, description) and providing natural language examples for query.

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 finds tools by describing data/task, lists many domains, and specifies it returns top-N tools with schemas. It explicitly says 'Call this FIRST' to distinguish from siblings.

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

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

Explicitly states when to use: 'when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' Provides clear guidance.

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

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the description's primary behavioral disclosure is beyond that. It discloses that USPTO PatentsView API sunset in May 2025 and that the tool 'soft-fails' until reactivated. It also details the exact data sources and returned fields, adding useful context about the tool's behavior and 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 front-loaded with example queries and a clear summary of the tool's purpose. It is relatively long but every sentence adds value, covering use cases, data sources, return fields, and limitations. Minor redundancy (e.g., 'US public company' mentioned twice) prevents a perfect score.

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 with multiple data sources and no output schema, the description thoroughly lists return fields and data sources. It notes the patent function's soft-fail status. However, it lacks precise formatting details for fundamentals (e.g., exact field names) and does not specify the number of recent news mentions or LEI results. Still, it provides a solid mental model of the output.

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

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

Schema coverage is 100%, but the description adds significant value by providing concrete examples ('AAPL','0000320193'), reinforcing that value must be a ticker or zero-padded CIK, and explicitly stating that names are not supported. This goes beyond the schema's basic descriptions.

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

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

The description opens with concrete user query examples and clearly states it produces a 'full cross-source profile of a US public company in ONE parallel call,' listing all data sources and return fields. This directly distinguishes it from siblings like 'resolve_entity' (which only resolves names to identifiers) and 'compare_entities' (which compares multiple entities).

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

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

The description explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' giving a strong usage directive. It also states 'Names not supported — use resolve_entity first if you only have a name,' providing a clear alternative for a common scenario.

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

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

Annotations already provide destructiveHint=true and idempotentHint=true. Description adds 'clear sensitive data' but doesn't disclose new behavioral traits beyond what annotations convey.

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

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

Two sentences, front-loaded with action, no unnecessary words, efficiently communicates purpose and usage.

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 1-param tool with no output schema, the description fully covers what the tool does, when to use, and how it relates to siblings.

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

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

Schema coverage is 100%, so baseline 3. Description's 'Memory key to delete' roughly matches schema description, adding no extra meaning about key format 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 'Delete a previously stored memory by key', specifying the action and resource, and distinguishes from siblings 'remember' (store) and 'recall' (retrieve).

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

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

Explicitly states when to use (stale context, task done, clear sensitive data) and pairs with remember and recall, providing clear usage guidance 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?

Describes fetching the page, extracting title/description/key links, and emitting markdown. Annotations already provide readOnlyHint, idempotentHint, etc., and the description adds useful behavioral context without contradiction.

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

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

Two sentences plus a use-case line; front-loaded with the primary function. Efficient and clear, though the use-case sentence could be integrated.

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 2-parameter tool with rich annotations, the description covers purpose, usage, output format, and use cases completely. No missing information.

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

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

Both parameters are fully described in the input schema (100% coverage). The description does not add additional semantics beyond summarizing the output.

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

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

The description clearly states the tool generates an llms.txt file for a given URL, specifies the target AI crawlers, and the output format. It distinguishes from sibling tools by focusing on this specific task.

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 use cases: getting a client's site indexed, drafting your own, or auditing competitors. No explicit when-not-to-use, but the examples are 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 already declare readOnlyHint=true and idempotentHint=true, covering safety. The description adds value by specifying the exact return content (fields, geometry type, record count, capabilities) 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 a single, concise sentence that is front-loaded with the verb and resource. 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 read-only tool with one parameter, the description fully explains what the tool returns. No output schema is needed, and the context is complete.

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

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

Schema coverage is 100% with a clear description for the url parameter. The tool description adds a pattern example but does not significantly extend the schema's own description, warranting a baseline score of 3.

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

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

The description clearly states the verb 'Get', the resource 'ArcGIS Feature/Map Service layer's schema', and specifies the returned attributes (fields, geometry type, record count, capabilities). This distinguishes it from sibling tools like query_layer.

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

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

The description implies usage for schema retrieval but provides no explicit guidance on when to use this tool versus alternatives (e.g., query_layer for data queries). No when-not-to-use or prerequisite information is given.

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 behavior. The description adds context on return fields and default behavior (active only), but does not reveal additional behavioral traits not covered by 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: first states purpose and return fields, second provides usage guidance. No wasted words, well-structured for quick agent parsing.

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 read-only tool with annotations, one optional parameter, and no output schema, the description fully covers what the agent needs: purpose, return structure, and when to use. No gaps.

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

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

Schema coverage is 100% with the parameter description already explaining include_inactive. The description mentions 'active subscriptions' but does not add meaning beyond what the schema provides, so baseline score applies.

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

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

The description clearly states the tool lists the caller's active subscriptions and returns specific fields. It distinguishes from sibling tools like subscribe and unsubscribe by focusing on review and retrieval of existing subscriptions.

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 using this tool to review current subscriptions before adding more (subscribe) or to find an ID to cancel (unsubscribe), covering key use cases and clarifying when it should be used instead of 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?

Discloses rate limits ('Rate-limited to 5 per identifier per day'), cost ('Free; doesn't count against your tool-call quota'), and impact ('signal directly affects roadmap'). No contradiction with annotations (all false). Adds valuable 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?

Approximately 100 words, front-loaded with purpose and usage, then details. Every sentence contributes necessary information. No redundancy. Excellent conciseness.

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

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

Given 3 parameters, a nested object, no output schema, and no sibling overlap, the description completely covers input semantics, usage context, limitations, and expectations. No gaps remain.

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

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

Schema coverage is 100%, and description adds meaning by explaining the `type` enum values with concrete examples and advising on `message` specificity. The optional `context` object is also explained. Enhances clarity 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: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It lists specific categories (bug, feature, data_gap, praise) and distinguishes itself from sibling tools by being a feedback mechanism rather than an information retrieval or analysis tool.

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

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

Explicitly says when to use: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' Also provides guidance on what not to do: 'don't paste the end-user's prompt.' Clearly differentiates from siblings by function.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds useful context: no PII, aggregated counts, caching duration (5min-1h), and data source ('CF analytics-engine'). This goes 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 front-loaded with the core function and includes three bullet points for use cases. It is reasonably concise but could be slightly tighter; still, 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?

Given one optional parameter, high schema coverage, and good annotations, the description covers returns, use cases, source, privacy, and caching. It lacks explicit mention of output format or pagination, but the mention of 'top tools, top packs, and total call volume' gives sufficient 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 schema covers 100% of parameters with a description for the `window` parameter that explains the enum values and their trade-offs. The description's mention of the window parameter does not add significant new meaning beyond the schema, meeting the baseline for full 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 that the tool 'returns the top tools, top packs, and total call volume' over a recent window, distinguishing it from sibling tools like ask_pipeworx or discover_tools. It specifies a specific verb ('returns') and resource ('trending signals'), making the purpose unambiguous.

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

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

Three explicit use cases are provided, covering discovery, confirmation, and alignment. The trade-off between short and long windows is explained. However, no explicit guidance on when not to use the tool or direct comparisons to siblings is given, though 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 idempotentHint=true, but the description adds rich behavioral context: explains the algorithms (monotonicity violations, partition-sum checks), the partition filter (drops placeholder slugs), the semantic anchor (Jaccard similarity), and the fill check against live CLOB depth. 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 the core purpose and then structured into modes. It is thorough but slightly verbose; some details like the fill check and associated tool reference could be omitted or moved to a separate note. However, given the tool's complexity, the length is justified and well-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?

With no output schema, the description fully documents the response format (opportunities[], partition_check, fill_check) and covers edge cases (placeholder filtering, low similarity, thin legs). It also links to polymarket_fill_risk for custom sizing, making it self-contained enough for an agent to use correctly.

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

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

Both parameters have schema descriptions (100% coverage), but the tool's description adds significant extra meaning: it defines the recommended use for each parameter with examples (e.g., 'fed-decision-may-2026' for event), explains the behavior of no-args mode, and clarifies the cross-event search logic for topic. This goes well beyond the schema.

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

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

The description starts with a clear verb+resource statement: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinctly describes the tool's purpose and differentiates between three modes (no args, event, topic), which helps the agent understand 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?

Provides explicit instructions for each parameter: 'Call with NO args for a trending_scan... pass event for the strongest per-event partition_check, or topic for a themed cross-event scan.' It also advises when not to trade (realizable_edge_pp ≤ 0). However, it does not explicitly contrast with sibling tools like polymarket_edges or polymarket_edge_tracker, which could confuse an agent about which tool to choose.

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 thoroughly discloses behavioral traits beyond the annotations. It details the three segments (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, CONCENTRATED_LONGSHOT), edge calculation net of slippage, Kelly fraction caps, tradeable-edge knobs, diagnostic output, and caching (1h KV). It also notes limitations like Fed bet unreliability without paid data. This far exceeds the annotation's basic readOnly and idempotent flags.

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, using numbered segments and bold key terms. It front-loads the main purpose. However, some details like exact model formulas (e.g., 'lognormal barrier from 90d FRED log-returns') are excessive and could be trimmed for conciseness without losing essential information.

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

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

Given the complexity (9 parameters, no output schema), the description covers the input parameters, output structure (by_segment, diagnostics), edge fields, and caching. It explains the Fed bets exclusion and filter_skips diagnostics. However, the output is described narratively rather than formally, which might leave some ambiguity 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?

All 9 parameters have descriptions in the input schema (100% coverage). The description adds extra context: it explains the relationship between min_kelly and min_partition_leg_kelly, the tradeable-edge knobs like min_liquidity and max_spread_pp, and the no-fee note for slippage_pp. This adds meaningful semantics beyond the schema, though the schema descriptions are already quite good.

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: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It is built for 'what should I bet on today' and distinguishes itself by explaining the three model segments. However, it does not explicitly differentiate from sibling tools like polymarket_arbitrage or polymarket_edge_tracker, missing a clear comparative statement.

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

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

The description provides a clear use case ('what should I bet on today') and mentions that agents can discover opportunities without paging hundreds of markets. However, it does not specify when to use this tool versus its siblings, nor does it explicitly state when not to use it or provide alternative tools for different scenarios.

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

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

The description provides extensive behavioral details beyond annotations: response structure (tracked, expired, snapshot_dates), limitations (60-day TTL, snapshot start date), and computational details (decay from daily closes, not intraday). Annotations already indicate reads, so this adds significant 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?

The description is comprehensive but verbose, with dense paragraphs that could be more streamlined. It front-loads purpose effectively, but the response details could be more succinct. Adequate structure but not maximally concise.

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

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

Despite no output schema, the description fully explains the return shape (tracked[], expired[], snapshot_dates[]) and handles edge cases (gaps in snapshots, lifespan median). It also covers limits, making it complete for understanding the tool's behavior and output.

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

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

Schema descriptions are already informative (e.g., 'days' with default and clamp, 'window' with options and default). The description adds context about window meaning ('snapshot family'), climbing above the baseline of 3. No extra detail for 'days' beyond schema, but overall helpful.

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: tracking edge persistence and decay from daily snapshots. It uses specific verbs ('answers how long has this edge existed and is it shrinking?') and distinguishes from sibling tools like 'polymarket_edges' by focusing on time-series telemetry rather than current edges.

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

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

The description implies usage context (e.g., 'a fresh wide edge and a 3-week-old wide edge are different trades') but does not explicitly compare with alternatives or state when not to use this tool. More explicit guidance would improve this dimension.

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 return fields for both modes (top_of_book, vwap_fill_price, slippage, shares_filled, verdict, etc.) and warns about partial fills converting arb positions into unhedged directional risk. Annotations (readOnlyHint, idempotentHint) are complemented, not contradicted.

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

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

The description is thorough but well-organized with clear sections for single-market and basket modes. All information is relevant; minor redundancy could be trimmed, but overall 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?

Given the tool's complexity and lack of output schema, the description fully specifies all return fields for both modes, explains edge cases (thin legs, forced directional risk), and provides usage warnings. No gaps in information.

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

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

Schema coverage is 100%, but the description adds valuable context: explains default behavior for side (auto for basket), interprets size_usd differently for single vs basket, and clarifies that market/event are required. Adds 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 checks realizable vs theoretical edge against live order book depth, distinguishing between single-market and basket modes. It explicitly instructs use before acting on arbitrage or edge signals, differentiating 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 explicitly states when to use: before any polymarket_arbitrage sell/buy-every-leg signal or any trade above ~$500. It explains the risk of partial fills and thin books, providing clear context for appropriate usage.

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

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

Annotations already declare readOnly, idempotent, non-destructive. The description adds extensive behavioral context: conditions where spreads are invalid (compatibility_warning, temporal_alignment), counters for dropped comparisons, and distinction between pre-mapped and actual tradeable spreads. No contradictions.

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

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

The description is detailed and well-organized with sections for modes, response, safety fields, and warnings. While thorough, it could be slightly more concise; however, every sentence adds value and front-loads critical information.

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

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

Given the complexity of cross-venue spread analysis and no output schema, the description fully documents the response structure (prices, top_spreads_pp, compatibility_warning, temporal_alignment, skip counters). It equips the agent to understand both normal and edge-case outputs.

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 basic descriptions. The description adds mode explanation, example values, and clarifies that explicit parameters override topic-mapped values. It goes beyond schema by explaining the two modes and how parameters interact.

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 computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It uses specific verbs and resources, and distinguishes from siblings like 'polymarket_arbitrage' which focuses on single-venue arbitrage.

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

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

It explicitly describes two modes (topic and explicit), provides examples, and warns when spreads are not meaningful (compatibility_warning, temporal alignment). It tells the agent when to avoid using the tool, e.g., when compatibility_warning fires due to mismatched event shapes or temporal gaps.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the description does not need to reiterate these. It adds value by stating that the tool returns attribute rows and geometry, which is beyond what annotations provide. No contradictions. The behavioral disclosure is adequate.

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 that are front-loaded with the main action and resource. Every sentence adds value: the first explains the core functionality, the second gives a concrete usage tip. No wasted words 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?

Given the 6 parameters, no output schema, and rich annotations, the description covers the essential aspects: what parameters do, how to use them (with example), and where the URL comes from. It could explicitly mention that geometry is returned only if out_fields includes geometry fields, but the schema doesn't have a geometry parameter, so it's acceptable. Overall, it's fairly complete for a query tool.

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

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

The input schema has 100% coverage with parameter descriptions. The description adds meaningful usage patterns: 'SQL-like where', 'comma-separated out_fields', and a practical example (sampling). This goes beyond the schema and helps the agent understand parameter semantics more deeply.

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: query an ArcGIS Feature/Map Service layer by URL. It specifies the SQL-like parameters and what they do, and distinguishes from siblings like search_datasets (which finds datasets) and layer_info (metadata). The verb 'query' and resource 'ArcGIS layer' are specific and unambiguous.

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

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

The description provides clear usage context: the URL comes from search_datasets, and it gives an explicit example of how to sample data (where="1=1" + out_fields="*"). It does not explicitly state when not to use or provide alternative tools, but the sibling list implies other tools exist for metadata or discovery. The guidance is sufficient for the typical use case.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is clear. The description adds context about scoping and usage pattern but does not contradict annotations. It adds value beyond annotations but is not extensive.

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 main action and each sentence adds value. It is slightly verbose but still efficient for the information conveyed.

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 implicitly covers return values (retrieved value or list). It also explains scope and usage pattern. For a simple retrieval tool, this is complete.

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

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

Schema coverage is 100% and the description adds meaning by explaining the behavior when the key parameter is omitted (list all keys) and mentions scoping. This is useful context beyond the schema's parameter description.

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

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

The description uses specific verbs 'retrieve' and 'list' with the resource 'value saved via remember'. It clearly states the tool's dual functionality: retrieving a specific key or listing all keys. This distinguishes it from sibling tools like remember and forget.

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

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

The description explains when to use the tool ('to look up context the agent stored earlier') and mentions scope ('scoped to your identifier'). It pairs with remember and forget but does not explicitly state when not to use it or provide 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 (readOnlyHint, idempotentHint, destructiveHint) are consistent. The description adds critical behavioral context: mark_read flag causes stateful changes (flags events as read) and explains the feed persistence. 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, no fluff. Each sentence adds distinct value: purpose and return fields, filtering and mark_read, polling and alternative access.

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

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

Despite no output schema, the description explains return fields and key behaviors (filtering, mark_read, polling). It covers the main use case but omits mention of limit and unread_only parameters, which are in the 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?

With 100% schema coverage, baseline is 3. The description adds value by giving a concrete example for type ('sec_8k'), specifying 'ISO timestamp' for since, and explaining the effect of mark_read. However, it does not mention the limit or unread_only parameters explicitly.

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 a specific verb-resource pair 'Pull fired events from your subscription feed' and details the return fields (source, citation_uri, raw payload). It distinguishes itself from sibling tools like list_subscriptions or subscribe by focusing on event retrieval with filtering options.

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 that 'Polls work fine' and provides an alternative external URL for scripts/dashboards, offering some context on when to use this tool. However, it does not explicitly compare to sibling tools or state when not to use it, leaving some ambiguity.

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, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds significant behavioral context beyond these: it fans out to multiple sources, has a fallback mechanism (GDELT→GNews when rate-limited), mentions that USPTO is soft-failing until reactivated due to API sunset, and describes the return format (changes[] grouped by source + total_changes + citation URIs). This provides rich 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 packed with valuable information and is front-loaded with example queries. However, the list of examples could be slightly condensed without losing clarity. Every sentence earns its place, but there is minor redundancy (e.g., multiple example queries convey the same intent). Still, it is well-structured and clear.

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

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

Given no output schema, the description compensates by describing the return format (structured changes[] grouped by source with total_changes and citation URIs). It covers the fallback behavior, API sunset issue, and points to the sibling 'entity_profile' for static data. All relevant aspects are addressed, making it complete for a tool of this complexity.

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

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

Although schema description coverage is 100%, the description adds meaning beyond the schema. For 'since,' it explains both ISO date and relative shorthand formats with examples ('7d', '30d', '3m', '1y') and suggests typical values. For 'value,' it clarifies ticker or CIK format. For 'type,' it confirms only 'company' is supported. This adds actionable guidance.

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

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

The description starts with example queries ('What's new with X', 'latest on Y') and explicitly states the tool is a 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It also lists the data sources it fans out to (SEC EDGAR, GDELT→GNews, USPTO) and directly distinguishes itself from sibling tool 'entity_profile,' making the purpose very specific and clear.

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

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

The description provides explicit when-to-use examples ('What's new with X', 'latest on Y', etc.) and also explicitly states when not to use it ('Use entity_profile instead when you want the static profile...'). It explains the fallback behavior from GDELT to GNews and provides guidance on the 'since' parameter ('Use "30d" or "1m" for typical monitoring'). This gives clear context for appropriate use.

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

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

Discloses scoping by identifier, persistence differences (authenticated vs anonymous sessions) and idempotency (idempotentHint=true) beyond annotations. 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?

Concise at 4 sentences, front-loaded with purpose, and logically structured. Slightly more verbose than minimal, but every sentence adds value.

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

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

Completely covers usage context, behavior, and integration with sibling tools for a simple write operation with no output schema. No gaps.

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

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

Schema coverage is 100% with examples in property descriptions. The description rephrases these examples ('ticker, address, preference') but does not add new semantic 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?

Clearly states 'Save data the agent will need to reuse later', uses specific verb 'save', identifies resource as 'key-value pair', and distinguishes from sibling tools ('recall', 'forget').

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

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

Explicitly states when to use: 'when you discover something worth carrying forward' and provides 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=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds context that each call cascades through multiple endpoints and replaces manual lookups, which is valuable 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 well-structured with examples, bullet-like type breakdowns, and a summary of internal behavior. It is slightly verbose but every sentence adds value; minor redundancy could be trimmed.

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 multiple entity types, input forms, output details (including citation URIs), and the internal cascading behavior. It is comprehensive given the tool's complexity and lack of output schema, leaving no major gaps for an agent to understand.

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

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

Schema coverage is 100%, but the description adds significant meaning: it explains what each entity type returns (e.g., company returns ticker, CIK, company_name, citation URI; drug returns RxCUI, ingredient, brand, citation), and clarifies input flexibility (ticker, CIK, or name for company; brand or generic for drug). This goes far 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 resolves names to canonical identifiers with specific examples ('ticker', 'CIK', 'RxCUI'). It distinguishes itself from siblings by emphasizing its role as a foundational lookup that other tools require as input, though it doesn't explicitly name sibling differences.

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 a clear usage directive. It outlines supported types and input formats but doesn't specify when not to use or provide explicit alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral context: it probes each entity via ai_visibility_check, ranks by score, and surfaces most/least recognized. It does not mention potential external API calls (e.g., when 'anthropic' model is used with _apiKey), which is a minor gap, but overall adds value beyond annotations.

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

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

The description is three concise sentences: purpose, method, use case. It front-loads the primary action and is free of fluff. Every sentence earns its place.

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

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

Given the tool's moderate complexity (4 params, no output schema) and rich annotations (read-only, idempotent, open-world), the description sufficiently covers the purpose, method, return values (ranked list with score, confidence, signal density), and use case. 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 description coverage is 100% and already fully documents all parameters, including the keyword semantics for 'entities' (first as subject, rest as competitors). The tool description repeats this info without adding new parameter-specific details, so it meets the baseline but adds no extra 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 uses specific verb 'Compare' and resource 'AI visibility', and clearly distinguishes from sibling tools like 'ai_visibility_check' (single entity) and 'compare_entities' (generic comparison). It states the method ('probes each entity with ai_visibility_check') and the output (ranked list).

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

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

The description provides a clear use case ('competitive AI-marketing audits') and an example question. It implies that for single entity checks one could use ai_visibility_check, but does not explicitly state when not to use this tool or list alternatives. The guidance is good but lacks explicit exclusions.

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

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

Annotations already indicate read-only, idempotent, open-world, non-destructive. Description adds critical behavioral details: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, sources_failed list on timeout. This goes well 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?

Six sentences front-loaded with purpose and usage, then behavioral notes. Every sentence adds distinct value; no redundancy or waste.

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 (package name, version default), output (summary block fields, advisories, links, alternatives), edge cases (scoped packages, partial failures, latency), and limitations (NPM only). Without output schema, description fully compensates.

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 both parameters with descriptions (100% coverage). Description adds context like 'version history' but no new semantic details beyond what's in the schema. Baseline 3 applies.

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 performs a composite check for adding an npm package, covering license, advisories, version history, and bundle size. It distinguishes from sibling tools by being specific to npm packages and aggregating multiple data sources.

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 usage triggers: 'Use whenever an agent asks "is X safe/popular/small" or "what does adding lodash cost me"'. Also explicitly scopes to NPM ecosystem and directs other ecosystems to deps.dev:version directly, preventing misuse.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds context about the return format (name, summary, record_count, owner/org, url) and the recommended follow-up action, enhancing transparency beyond annotations.

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

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

Two well-structured sentences with no wasted words. The action and resource are front-loaded, and the follow-up usage is succinctly explained.

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 completely covers the tool's purpose, parameters, return structure, and next steps. It is fully adequate for an agent to select and use the tool correctly.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds value by giving example keywords, clarifying the limit range (1-50, default 20), and explaining that org_id overrides the default.

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 searches Charleston County GIS open geospatial datasets by keyword, lists example dataset types, and specifies return fields. This distinguishes it from sibling tools like query_layer and layer_info.

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 examples of keywords and explains that the returned url can be used with query_layer/layer_info. While it doesn't explicitly state when not to use it, the guidance is clear and actionable.

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 safety (read-only, idempotent). The description adds detailed behavioral traits: BGE-base-en embeddings, cosine similarity, 500-char windows, 200K char limit with truncation flagging, and return of passages with offsets for verification. This goes well 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 concise yet comprehensive. Every sentence adds value, starting with the core purpose, followed by usage advice, return format, and technical specifics. No wasted words, and the structure is logical.

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

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

Given no output schema, the description fully explains the return value (passages with offsets and similarity scores), embedding model, window size, character limit, and pairing with another tool. It is complete for a tool with 3 parameters.

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

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

With 100% schema coverage, baseline is 3. The description adds practical context such as example queries, clarifying that the text is 'already pulled' and that passages include offsets. It reinforces the schema descriptions without redundancy, justifying 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 performs semantic search inside a fetched record, using specific examples like SEC 10-K and articles. It distinguishes itself from general search by focusing on in-document search and mentions pairing with ask_pipeworx_grounded, making the purpose unambiguous.

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

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

The description explicitly advises using this tool when a record is too large to fit in the prompt, and suggests pairing with ask_pipeworx_grounded for grounded generation. It provides clear context but does not explicitly state when not to use it or list 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?

Description implies non-idempotent behavior by stating 'Returns the new subscription id', contradicting the idempotentHint=true annotation. No other behavioral traits disclosed beyond required account type.

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 purpose but is verbose due to necessary detail. Could be more structured (e.g., bullet points) but all information is relevant.

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

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

Covers all aspects: purpose, return value, supported types with examples, delivery channels with constraints, and prerequisites. No output schema, but return value is explained.

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

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

Schema coverage is 100%. Description provides detailed examples and constraints for each parameter, including type-specific filters, delivery options with phone verification and rate limits, and webhook HMAC signing details.

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 it creates a proactive monitoring subscription to a live-data event stream and returns the subscription ID. Distinguishes from sibling tools like list_subscriptions and unsubscribe.

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

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

Specifies when to use (creating subscriptions) and prerequisites (Pipeworx OAuth account). Lists supported types with examples, but does not explicitly exclude alternatives or state when not to use.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds value by stating the tool returns 'category-bucketed example questions' and 'tool + argument shape' from a live catalog. 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 a single paragraph but packs a lot of information efficiently. It starts with common user phrases, then explains the function and output, and ends with usage guidance. It could be slightly more structured, but it's front-loaded and 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?

Given the tool's simplicity (one optional parameter, no output schema), the description is complete enough. It explains what the tool returns (example questions with tool+argument shapes) and when to use it. The absence of an output schema is compensated by the clear description of the output format.

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 single parameter 'topic' is well-described in both the schema and the description. The description elaborates by listing example values (finance, pharma, etc.) and clarifies that omitting the parameter gives a cross-category spread. With 100% schema coverage, this adds helpful context.

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 an onboarding entry point for getting ideas of what to ask Pipeworx. It lists specific questions users might type (e.g., 'what can you do?') and describes the output as category-bucketed example questions. It distinguishes from siblings by positioning itself as the first tool to use when the agent doesn't know what Pipeworx can do.

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 to use this tool 'FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also explains the optional topic parameter for focus. While it doesn't list explicit alternatives, the context makes it clear when this tool is appropriate.

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

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

Annotations indicate write, non-destructive, idempotent. Description adds: ownership enforcement, deactivation (not deletion), and historical availability via recent_alerts. Provides 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?

Two sentences with no wasted words. First sentence states action and ownership. Second explains effect. Efficient and front-loaded.

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

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

For a simple 1-param, no-output-schema tool, description covers ownership, mutation effect, and historical data retention. Complete and actionable.

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

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

Schema has 1 param with description 'Subscription id (uuid) returned by subscribe.' Description does not add parameter details beyond schema, but schema coverage is 100%, so baseline 3 applies.

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?

Title and description clearly state 'Cancel a subscription by id.' The verb 'cancel' and resource 'subscription' are specific. It distinguishes from siblings like subscribe and list_subscriptions.

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?

States ownership enforcement: 'you can only cancel your own subscriptions.' Explains effect: deactivated not deleted, so historical events remain. Lacks explicit alternatives but sibling tools imply when not to use.

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

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

Annotations declare readOnlyHint, idempotentHint, openWorldHint true. The description adds significant behavioral details: returns verdict types (confirmed, approximately_correct, refuted, inconclusive, unsupported), extracted structured form, actual value with citation, and percent delta. It also explains the data source (SEC EDGAR + XBRL), going well 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 well-structured: starts with trigger phrases, states purpose, defines domain, and lists return values. It is concise without being terse, each sentence adds necessary information. Slightly lengthy but justified by the detail provided.

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 one parameter and no output schema, the description is fully comprehensive. It covers when to use, what it does, supported claims, return format, and efficiency benefit. The annotations further fill in behavioral context, resulting in no gaps.

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

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

Schema description coverage is 100% with a clear example for the 'claim' parameter. The description adds context by specifying supported claim types (company-financial), which adds meaning beyond the generic schema description. Baseline 3 is exceeded due to this added 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's purpose: 'natural-language claim verification against authoritative sources'. It provides specific trigger phrases and explicitly defines the scope as company-financial claims for US public companies. This distinguishes it from sibling tools by claiming it replaces multiple sequential 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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and gives the domain of company-financial claims. While it does not list alternative tools for other claim types, the context is clear and the scope is well-defined.

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