Arcgis Harrisburg

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds behavioral context: default model is free, Anthropic requires a BYO key and costs apply, and returns per-model results. No contradiction with annotations.

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

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

Single paragraph but dense and informative. Front-loaded with action and output. Includes examples. Could be slightly more structured, but no wasted words.

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

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

Given no output schema, description adequately summarizes return format. Covers default model, optional Anthropic with key requirement, and use cases. Minor omission of rate limits or error handling, but overall sufficient for agent decision.

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

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

Schema coverage is 100%, but description adds value by explaining 'entity' with examples, clarifying default value for 'models', specifying when '_apiKey' is needed, and giving usage guidance for 'context'. Enhances schema meaning.

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

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

Description uses specific verb 'probe' and resource 'LLMs', states output 'score 0-100 per model', and distinguishes from sibling tools which focus on entity profile or deep research. Clearly answers 'what does this tool 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?

Description lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) but does not explicitly state when not to use it or name alternatives among siblings. Provides context for when to use, but no 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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds significant context: routes to 4,552 tools across 1,160 sources, returns stable pipeworx:// citation URIs, works on all tiers, one fast call. No contradictions.

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

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

Description is lengthy but well-structured: starts with priority statement, then lists domains, explains routing, gives usage cues, examples, and hierarchical tool selection. Every sentence adds value; front-loaded with key info.

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

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

For a complex tool routing to thousands of sources, the description covers purpose, usage guidelines, behavioral traits, parameter semantics, and alternatives. No output schema exists but tool's return type (structured answer with citations) is implied. Complete enough for an agent to use correctly.

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

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

Schema description coverage is 100% with 6 parameters all described as aliases for 'question'. Description mentions natural language input and provides examples, but does not add substantial meaning beyond what the schema already provides. 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 is for asking factual questions about current/historical structured data across many domains (SEC, FDA, economic stats, etc.), returning answers with citations. It distinguishes from web search and sibling tools like ask_pipeworx_grounded and deep_research.

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

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

Explicitly says 'PREFER OVER WEB SEARCH', provides query phrase patterns ('what is', 'look up', 'find', etc.), and gives specific when-to-step-up guidance (use ask_pipeworx_grounded for hallucination-resistant answers, deep_research for broad multi-part questions).

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, idempotent), description details the extraction process, return structure with explicit refusal reasons, and the extra LLM call cost. No contradiction with annotations.

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

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

Description is well-structured, front-loaded with purpose, then mechanism, return format, refusal reasons, and usage guidance. Every sentence adds value despite moderate 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 complexity (4552 tools, 1160 sources, high-stakes), the description covers process, output format, failure modes, and usage recommendations. No output schema exists, so return values are fully 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% with clear parameter descriptions. The tool description adds minimal extra meaning beyond identifying the parameter as the question, which is already implied. 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?

Description clearly states it is a 'hallucination-resistant answer mode' that extracts answers only from tool results, distinguishing it from the sibling ask_pipeworx. The verb+resource (answer extraction) and scope are explicit.

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

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

Provides explicit when-to-use (high-stakes reads, cited answers) and when-not-to-use (casual lookups prefer ask_pipeworx). Mentions cost trade-off and alternatives clearly.

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

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

The description discloses extensive behavioral traits beyond annotations: fan-out process, resolver contract, parent event extraction, news fallback, safety short-circuits, resolution-rule risk, and tradeability notes. No contradiction with annotations.

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

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

The description is very long and dense, lacking clear structure or bullet points. While comprehensive, it could be more concise and front-loaded with the most 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 tool's complexity and lack of output schema, the description is remarkably complete, covering edge cases, error states, resolution confidence, market status, and detailed behavioral explanations.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds context about internal behavior but does not provide additional meaning for the parameters themselves beyond what's in 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 researches Polymarket bets by pulling Pipeworx data, specifying input types (slug, URL, question text) and outlining the process. It distinguishes from siblings by detailing its unique behavior, though not explicitly naming alternatives.

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?

Explicit usage examples are given ('should I bet on X', etc.) and extensive guidance on when results are reliable (e.g., low-confidence short-circuits). However, it doesn't explicitly mention when not to use it or recommend alternative tools.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds rich behavioral context: sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, result sorting by primary metric, and return format including paired data and citation URIs. No contradiction with annotations.

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

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

The description is a single paragraph but packs a lot of information efficiently. It could be slightly better structured (e.g., bullet points), but every sentence adds value and is clearly written.

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 there is no output schema, the description adequately explains what is returned: paired data and pipeworx:// citation URIs. It also covers the data sources and sorting behavior. The tool has only 2 required parameters, and the description fully covers their meaning and constraints.

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

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

Schema coverage is 100% with good descriptions. The description adds extra context: for type enum it explains what data each pulls; for values it explains tickers vs drug names and the min/max items limit. This adds value beyond the schema.

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

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

The description uses specific verbs and resources: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call'. It clearly distinguishes from siblings like entity_profile (single entity) and other search tools by emphasizing the comparative and parallel nature.

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

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

The description explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and provides example queries. This gives clear guidance on when to use this tool vs 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?

Annotation readOnlyHint and openWorldHint are present; description adds critical context: account required, return format (evidence, confidence, source, citations, gaps[]), expected time (15-60s), and behavior for unfound topics (empty gaps[]). No contradiction with annotations.

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

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

Well-structured with critical info front-loaded (account requirement). Every sentence adds value, though slightly long. Still efficient and organized.

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

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

Covers all essential aspects: auth, purpose, scope, alternatives, behavior, return format, limitations, and time estimate. No output schema but description adequately explains output. Complete for a complex tool.

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

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

Schema coverage is 100% and description adds value by explaining depth enum values (quick=3, standard=5, thorough=8 paid) and clarifying that question can be broad/multi-part natural language.

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 performs grounded multi-source research across structured data sources, decomposing questions into facets and routing to tools in parallel. It distinguishes itself from siblings like ask_pipeworx which is for single lookups or breaking news.

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 specifies when to use (broad/multi-part questions over structured data) and when not to (single lookups: use ask_pipeworx; breaking news: prefer ask_pipeworx). Also mentions account requirements and paid tier for 'thorough' depth.

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

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

Annotations indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds that results include 'full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed', enhancing behavioral understanding 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 front-loaded with the main purpose. It lists many domains, which adds utility but slightly increases length. Overall well-structured and informative, though could be trimmed slightly.

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 33 sibling tools and the meta-tool nature, the description fully explains what the tool does, when to use it, and what output to expect. No output schema is needed as the description is self-contained.

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

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

Schema coverage is 100%, so baseline is 3. Description does not add significant parameter meaning beyond the schema, though it implicitly covers query via examples. No extra value added.

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 'Find tools by describing the data or task' and provides specific examples ('look up FDA drug approvals', 'analyze housing market trends'). It distinguishes itself from siblings by positioning as a meta-tool to call first when exploring 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?

Explicitly specifies when to use: 'when you need to browse, search, look up, or discover what tools exist for' a list of domains. Also advises 'Call this FIRST when you have many tools available' to see the option set, providing clear context.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable context: the tool fans out across multiple sources (SEC, XBRL, USPTO, news, GLEIF), notes that patents soft-fail after May 2025, and that names are not supported. This enriches the behavioral model 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 dense paragraph that front-loads example queries and then details the fan-out and return fields. While every sentence adds value, it could be more structured (e.g., bullet points) for easier scanning, but it remains acceptably concise given the amount of 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?

In the absence of an output schema, the description thoroughly lists returned fields (cik, company_name, recent_filings with URIs, fundamentals, patents, news, LEI) and notes limitations (patents sunset, name unsupported). This provides sufficient context for a complex multi-source tool.

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

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

Schema coverage is 100% with descriptions for both parameters. The description further clarifies that 'value' can be a ticker or zero-padded CIK and explicitly states names are not supported, adding meaning beyond the schema's enum and property 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 states the tool provides a 'full cross-source profile of a US public company in ONE parallel call,' clearly specifying the verb (profile), resource (public company), and scope (cross-source). It distinguishes from sibling tools like resolve_entity by noting that names require prior resolution.

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

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

Explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view' and advises using resolve_entity if only a name is available. This 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 indicate destructive action; description confirms deletion and adds context about clearing sensitive data. No contradiction and 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?

Two informative sentences with no redundancy. Purpose, usage, and pairing are presented efficiently.

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

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

For a simple 1-parameter, no-output-schema tool, the description fully covers purpose, usage context, and interaction with related tools.

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

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

Single parameter 'key' is fully covered in schema (100%). Description does not add information beyond schema.

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

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

Description clearly states the action (delete) and resource (previously stored memory by key). It distinguishes from sibling tools like remember and recall.

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

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

Explicitly states when to use (stale context, task done, clear sensitive data) and pairs with alternatives (remember and recall).

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 as true and destructiveHint as false. The description adds valuable behavioral details such as fetching the page, extracting title/description/key links, and emitting standard markdown format. It also clarifies the output is a single text blob ready to drop at site-root/llms.txt, which goes 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 highly concise: two sentences followed by a bullet list of use cases. Every sentence adds value, and the structure is front-loaded with the main purpose. No wasted words.

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

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

For a simple tool with 2 parameters and no output schema, the description fully covers what the tool does, what it produces, and how to use it. It even describes the output format and use cases, making it complete 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% with both parameters well-described. The description reinforces the purpose but does not add new semantic meaning beyond the schema. The baseline of 3 is appropriate when schema already documents parameters adequately.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, targeting AI crawlers. It uses a specific verb ('generate') and resource ('llms.txt file'), and distinguishes itself from siblings like ai_visibility_check and scan_competitor_ai_presence by focusing on llms.txt generation.

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

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

The description explicitly lists three use cases: getting a client's site indexed, drafting for own projects, and auditing competitors. While it doesn't mention when not to use it or alternatives, the use cases provide clear context for when to invoke this tool.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds value by listing specific output details (fields, geometry type, record count, capabilities) beyond what annotations provide, but does not cover every behavioral aspect (e.g., error handling).

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?

A single, concise sentence that front-loads the main action and includes all necessary components. No fluff or redundancies.

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

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

Given the tool's simplicity (one parameter, clear annotations), the description fully conveys the purpose, input, and output. No output schema exists, but the description enumerates return fields sufficiently.

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

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

The schema description covers 100% of the single parameter 'url' with an example. The description does not add additional semantic meaning beyond what is already in the schema, so baseline score of 3 is appropriate.

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

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

The description clearly states it retrieves schema information (fields, geometry type, record count, capabilities) from an ArcGIS layer URL. It uses a specific verb 'Get' and resource 'layer's schema', and distinguishes itself from sibling tools like 'query_layer' which handles data queries.

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

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

The description implies the tool is for schema retrieval from a layer URL, but does not explicitly state when to use it versus alternatives like 'query_layer' or 'search_datasets'. There are no usage exclusions or context provided about prerequisites.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds no new behavioral traits beyond stating it returns active subscriptions, which aligns with the safe read operation implied 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, no fluff. Every sentence provides value: first states purpose and output, second gives usage guidance. Front-loaded and efficient.

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

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

For a simple list tool with one optional param and no output schema, the description fully covers what the agent needs: what it does, what it returns, and when to use it. 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% for the single parameter (include_inactive), with a clear description. The tool description reiterates the default behavior ('active') but adds no additional meaning beyond what the schema provides.

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

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

The description explicitly states 'List the caller's active subscriptions' with a specific verb and resource, and lists the returned fields, distinguishing it from siblings like subscribe and unsubscribe.

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

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

The description provides clear usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' It doesn't mention alternatives or when not to use, but the context is sufficient for this simple tool.

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

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

The description adds behavioral context beyond annotations: it confirms the tool is free, doesn't count against quota, is rate-limited to 5 per identifier per day, and that feedback is read daily and directly affects the roadmap. No contradiction with annotations.

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

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

The description is a single paragraph but is well-organized, front-loading the purpose and then covering types, usage advice, rate limits, and quota. Every sentence is informative with no redundancy; it could be slightly more structured with bullets but is 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 simplicity (3 parameters, no output schema), the description covers all relevant aspects: types, context structure, message formatting, rate limits, quota, and how feedback is used. It is complete for effective tool usage.

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

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

With 100% schema description coverage, the description adds value by elaborating on the enum types with usage examples and providing writing guidance for the message parameter (be specific, 1-2 sentences). This goes beyond the schema's descriptions.

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

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

The description clearly states the tool is for sending feedback to the Pipeworx team, specifying four types of feedback (bug, feature, data_gap, praise). It uses specific verbs and distinguishes from sibling tools, none of which serve a feedback purpose.

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

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

The description explicitly tells when to use each feedback type and provides best practices (describe in terms of tools, avoid pasting user prompts). It mentions rate limits and that feedback influences the roadmap. However, it doesn't explicitly state when not to use this tool (e.g., for general questions).

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, openWorld. Description adds valuable context: data source (CF analytics-engine), no PII, caching window (5min-1h), and aggregation nature. No contradictions with annotations.

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

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

Well-structured: headline, details, use cases, notes. Though longer than minimal, every sentence adds value. Could be slightly more concise but maintains clarity.

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

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

Given simple single-parameter schema with 100% coverage, read-only annotations, and no output schema, the description fully covers: what is returned, how derived, privacy, caching, and use cases. 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 a well-described enum. Description adds: default value, guidance on choosing window based on need (hot vs steady-state). Contributes beyond schema.

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

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

Description clearly states it returns trending tool/pack usage data from other AI agents, specifying exact outputs (top tools, top packs, call volume). Sets it apart from siblings like discover_tools (general discovery) and ask_pipeworx (question answering).

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

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

Explicitly lists three concrete use cases (hot data discovery, canonical tool confirmation, use-case alignment). Explains window parameter trade-offs. Does not directly mention alternative tools for when not to use, but the use cases provide 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, openWorldHint, idempotentHint. The description adds extensive behavioral detail: algorithms used, semantic anchor (Jaccard similarity >=0.30), partition filter (placeholder slugs), fill check (theoretical vs realizable edge), and warnings about not trading when realizable <=0. No contradiction with annotations.

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

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

The description is fairly long but well-structured with clear headings (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK) and front-loaded mode selection. Every sentence provides useful information, though some redundancy could be trimmed. Overall effective for the tool's complexity.

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

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

Despite high complexity (multiple modes, algorithms, filters) and no output schema, the description explains the return structure (opportunities[] with fields, partition_check in event mode) and key behaviors (fill check, semantic anchor, partition filter). It also references polymarket_fill_risk for custom sizing, completing the context.

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

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

Schema description coverage is 100%; the description adds significant value beyond schema. For 'event', it explains acceptable formats (slug or full URL), provides examples. For 'topic', it gives example seed questions. It clarifies the behavior change between modes.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It specifies the resource (Polymarket) and the method, and distinguishes it from sibling tools like polymarket_edges and polymarket_fill_risk.

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

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

The description provides explicit guidance on when to use each mode: no args for trending scan, 'event' for specific event, 'topic' for cross-event scan. It explains the differences and suggests 'event' as recommended for a specific market. However, it does not explicitly state when to avoid this tool in favor of specific siblings.

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 extensive behavioral context beyond annotations: it explains the three model families, response structure (by_segment, diagnostics), caching (1h KV-level), the Fed bets exclusion, and details on how edge filters work. No contradictions with annotations (readOnlyHint, idempotentHint, etc.).

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

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

The description is long but well-structured with sections for model families, knobs, and diagnostics. It is front-loaded with purpose and each part adds necessary detail for an AI agent. While not extremely concise, the structure compensates.

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 9 parameters and no output schema, the description is extremely complete. It describes the response top-level fields, segments, edge fields, diagnostic counters, and caching behavior, effectively substituting for an output schema.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining how parameters interact (e.g., min_kelly applies only to single-leg, min_partition_leg_kelly filters per-leg Kelly in partitions) and provides practical context like slippage assumptions. Not every parameter is elaborated in the description, but the added nuance justifies a 4.

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

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

The description explicitly states the verb ('Scan'), resource ('top Polymarket markets'), and purpose ('return opportunities where Pipeworx data disagrees with market price'). It also includes the intended use case ('what should I bet on today') and distinguishes this tool from siblings by describing its unique model-driven edge detection approach, unlike other sibling tools like polymarket_arbitrage or polymarket_edge_tracker.

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

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

The description clearly indicates when to use the tool (daily discovery of betting opportunities) and explains the output segments. However, it does not explicitly compare with alternatives or state when not to use it. The context of sibling tools is available but not referenced in the description.

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, openWorldHint, idempotentHint, destructiveHint. Description adds: reads daily snapshots (cache-miss gaps), decay from daily closes (not intraday), history bounded by 60-day TTL. No contradictions. Explains output structure with computed fields (trend, decay_pp_per_day, lifespan_days).

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

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

Single paragraph, front-loaded with purpose, then response structure, then limits. Every sentence adds value. Slightly dense but not overly verbose. Could be broken into shorter sentences for readability, but still 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?

No output schema, but description extensively explains all returned fields (tracked[], expired[], snapshot_dates[]) and their meanings. Covers limitations (history depth, snapshot gaps, decay computation). For a complex telemetry tool, this is fully 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 descriptions for both 'days' and 'window'. Description adds default values (14, '1wk'), clamp range for days (2-30), clarifies 'window' as snapshot family. This goes beyond schema, enabling correct invocation without external lookup.

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

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

The description clearly states the tool's function: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It distinguishes from siblings like 'polymarket_edges' by focusing on time-series analysis and decay metrics. The verb 'answers' and explanation of response fields (tracked, expired, snapshot_dates) solidify purpose.

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 context on when to use: comparing fresh vs. aged edges ('a fresh wide edge and a 3-week-old wide edge are different trades'). Implicitly contrasts with raw 'polymarket_edges' tool. Includes limits (history depth, snapshot TTL, daily closes). Lacks explicit when-not-to-use but sufficient for 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 destructiveHint. The description adds behavioral details such as walking the ladder, returning specific fields (slippage, verdict), and warning about partial baskets converting arb into directional risk. 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 lengthy (about 150 words) but well-structured with clear sections, capital key points, and bullet-like formatting. It could be slightly more concise, but the density of information is justified by the tool's complexity.

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

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

Without an output schema, the description fully documents the return fields for both modes (verdict, slippage, profit, thin_legs, etc.) and explains error conditions (thin books, partial fills). It covers all necessary information for an agent to use the tool correctly.

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

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

The schema covers all parameters with descriptions, and the description adds significant context: explains the interaction between market/event (mutually exclusive), defaults for side and size_usd, and the meaning of size_usd as settlement notional in basket mode. This 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 it performs a 'realizable-vs-theoretical edge check against live CLOB order-book depth.' It details two distinct modes (single-market and basket), and explicitly distinguishes from sibling tools like polymarket_arbitrage and polymarket_edges by specifying when to use this tool before those actions.

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 usage guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also explains the risks of partial fills and unhedged positions, helping the agent decide when to use this tool.

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

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

Annotations mark readOnlyHint true, which is consistent. The description extensively discloses behavior: response structure (leg prices, top spreads), safety fields (compatibility_warning conditions, temporal_alignment, skipped counts), and conditions when spreads are meaningless. No contradictions with annotations.

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

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

The description is long but well-structured with sections (TWO MODES, RESPONSE, SAFETY FIELDS). It front-loads purpose and adds necessary detail. Some redundancy exists (e.g., repeating examples), but overall every sentence adds value.

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

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

For a 3-param tool with no output schema, the description is exceptionally thorough. It covers input modes, response fields, edge cases (compatibility warnings, temporal alignment), and counter metrics. It preempts common misconceptions (pre-mapped ≠ tradeable). Complete for the tool's complexity.

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

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

Schemas have 100% coverage, but the description adds significant meaning: lists the 10 topic shortcuts, explains explicit ticker override, and gives examples. This goes well beyond the schema’s minimal descriptions, making parameter semantics fully clear.

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

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

The description clearly states the tool computes cross-venue spreads between Kalshi and Polymarket, specifies two modes (topic shortcuts and explicit tickers), and distinguishes this from other polymarket tools by being cross-venue. The purpose is unambiguous and well-defined.

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 (comparing same outcome across venues) and two modes for different needs. It warns that pre-mapped topics often yield compatibility warnings, implying they may not always be tradeable. It does not directly compare with sibling tools like polymarket_arbitrage, but the context is strong.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. The description adds useful behavioral context: returns attribute rows and geometry, accepts SQL-like where, and provides a common sampling example.

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: purpose and key parameters, parameter list, and example. No fluff, but the structure could be slightly improved by separating the example into a distinct sentence.

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

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

With no output schema, description adequately states 'returns attribute rows (and geometry)'. The example and clear parameter descriptions make the tool sufficiently complete for typical usage.

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

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

Schema description coverage is 100% (all parameters described). The description adds minor value by referencing search_datasets for url and giving examples for where and out_fields, but does not significantly extend beyond schema descriptions.

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

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

Description clearly states the tool queries an ArcGIS Feature/Map Service layer by URL, specifying the verb (query), resource (layer), and source (from search_datasets). It distinguishes itself from siblings like search_datasets 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?

The description indicates the tool is used after search_datasets to query a specific layer, implying a workflow. However, it does not explicitly state when not to use it or provide alternatives for similar tasks like layer_info.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds scoping to identifier and the ability to list all keys, which is additional 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?

Three concise sentences with no redundancy. First sentence states core function, second gives use cases, third covers scoping and pairing. Front-loaded and efficient.

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

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

Given simple schema (1 optional param) and no output schema, the description covers all needed aspects: operation, when to use, scoping, and related tools.

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

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

Schema coverage is 100%, so baseline 3. Description adds value by explaining the effect of omitting the key (list all) and providing real-world examples of what keys represent, thus enhancing clarity.

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

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

Clearly states verb (retrieve/list) and resource (saved memories). Distinguishes from siblings like remember and forget.

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

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

Explicitly says when to use (look up stored context) and provides examples of use cases. Also mentions pairing with remember/forget, giving clear context.

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

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

The description discloses key behavioral traits beyond annotations: the mark_read side effect (marking events as read to affect future calls), the feed structure, and the availability of a REST endpoint. However, there is an annotation contradiction: readOnlyHint=true conflicts with the described state modification via mark_read, which could mislead agents.

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 (approximately 100 words) and front-loaded with the core purpose. Every sentence adds necessary information: returns, filtering, mark_read behavior, alternative access. No redundant or vague language.

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 read tool without output schema, the description sufficiently covers what is returned (source, citation_uri, raw payload) and how to use filtering. It mentions polling and alternative REST access. However, it could explicitly mention the default limit (50) and error behavior, though the schema covers limit.

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

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

With 100% schema description coverage, the baseline is 3. The description adds value by providing examples (e.g., type='sec_8k'), clarifying mark_read behavior, and noting default limit. However, it does not cover the unread_only parameter's meaning, so it's not fully comprehensive.

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 'Pull' (or 'Returns') and the resource: 'fired events from your subscription feed'. It distinguishes from siblings by focusing on alerts, and specifies returned fields (source, citation_uri, raw payload). This provides a specific and unique purpose.

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 context on when to use the tool, mentioning filtering options and that polling works fine. It also gives an alternative endpoint for scripts/dashboards. However, it does not explicitly state when not to use this tool versus other siblings like ask_pipeworx or recent_changes, so lacks full exclusion 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=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds beyond this: it discloses that the tool fans out to SEC EDGAR, GDELT→GNews fallback, and USPTO (with a soft-fail note). It also describes the return structure (changes[] grouped by source, total_changes count, pipeworx:// URIs).

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

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

The description is longer than average due to technical details (sources, fallback), but it is well-structured: it starts with example queries, then explains functionality, and ends with an alternative tool. The key information is front-loaded. A few words could be trimmed, but overall it is appropriately sized for the complexity.

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

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

For a tool of this complexity (multiple data sources, parameter formats, fallback logic), the description is complete. It covers when to use, how parameters work, what sources are queried, what the response includes, and how it differs from a sibling tool. There is no output schema, but the description adequately describes the return structure.

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

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

Schema coverage is 100%, so the schema already documents all three parameters. The description adds value by explaining accepted formats for `since` (ISO date or relative shorthand like '7d', '3m') and recommending '30d' or '1m'. It also clarifies that `value` can be a ticker or CIK, and `type` only supports 'company'. This enriches the schema, though the schema already provides 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 starts with example queries and clearly states it provides a 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It distinguishes from the sibling tool 'entity_profile' by directing static profile queries there. The verb 'retrieves' is implied and the resource (company recent changes) is explicit.

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

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

The description explicitly lists when to use this tool (e.g., 'What's new with X', 'latest on Y') and when not to ('Use entity_profile instead when you want the static profile'). It also provides guidance on the `since` parameter, recommending '30d' or '1m' for typical monitoring, and explains fallback behavior between GDELT and GNews.

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

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

Adds important behavioral context beyond annotations: scoped by identifier, persistence differences (authenticated vs. anonymous, 24-hour memory). No contradiction with annotations.

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

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

Four sentences, front-loaded with purpose, then usage, then behavioral details. Slightly verbose but each sentence adds value.

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

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

Given simple tool (2 params, no output schema), description fully covers purpose, usage, behavior, and sibling interactions. Complete for its complexity.

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

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

Schema provides full coverage (100%) with descriptions and examples for both key and value. Description adds no additional parameter details, so baseline 3 is appropriate.

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

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

Description clearly states 'save data the agent will need to reuse later,' specifying the verb and resource. It distinguishes from siblings recall and forget by explicitly mentioning them and their roles.

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

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

Provides explicit when-to-use guidance with examples like resolved ticker, target address, user preference. Does not explicitly state when not to use, but context is clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: it explains that each call cascades through multiple internal lookup endpoints, replaces 2-3 manual lookups, and details the specific return format for each entity type, including citation URIs. There is no contradiction with annotations.

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

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

The description is well-structured, starting with relatable examples in quotes, then stating the core purpose, usage guideline, and detailed type support. It is slightly verbose but every sentence adds value; could be tightened but remains effective.

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

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

Despite lacking an output schema, the description fully explains the return values (ticker, CIK, company name, citation URI for company; RxCUI, ingredient, brand, citation URI for drug) and notes the internal cascading. Given the tool's moderate complexity (two entity types, multiple lookups), the description is complete and leaves 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%, but the description goes further by explaining the meaning of the 'type' parameter (supported types and what they return) and providing concrete examples for the 'value' parameter (e.g., ticker, CIK, name for company; brand or generic for drug). This adds substantial value beyond the schema's descriptions.

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

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

The description explicitly states the tool's purpose: resolving a user-spoken name to a canonical identifier needed by other tools. It provides specific examples ('What's the ticker for…') and details the supported types (company, drug) with their return values, clearly distinguishing itself from siblings by noting it replaces 2-3 manual 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 includes a strong usage directive: 'Use FIRST whenever you have a name but need an ID.' This clearly indicates when to use the tool. However, it does not explicitly state when not to use it or mention alternative sibling tools, which would have made it a 5.

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

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

Description adds context beyond annotations: mentions internal call to ai_visibility_check, ranks by score, and returns score, confidence, signal density per entity. No contradictions with readOnlyHint or idempotentHint.

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

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

Three sentences, front-loaded with main action, no filler. Every sentence adds value.

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

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

For a tool with 4 well-documented parameters and no output schema, the description sufficiently explains behavior, input, and output (ranked list with fields).

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 description adds little beyond what's already documented in parameter descriptions. It reinforces the 'subject' first entry, but does not introduce new meaning.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, uses ai_visibility_check internally, and surfaces rankings. It distinguishes from sibling ai_visibility_check by emphasizing multi-entity comparison.

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

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

The description identifies use case (competitive AI-marketing audits) and implies when to use compared to single-entity checks. However, it does not explicitly exclude alternatives like compare_entities.

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

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

Annotations already mark it as read-only, idempotent, and non-destructive. The description adds behavioral details: bundlephobia's first measurement can take 5-30s, and sources_failed lists timeouts. It describes the return structure (summary block, advisories, links, alternatives). 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 but each sentence adds value. It front-loads the core purpose and then fills in specifics. Could be slightly more concise, but no wasted words.

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

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

Given the tool's complexity (composite call, external services, partial failures, timeout), the description is very complete. It explains the fan-out, what each service provides, and how failures are handled. No output schema, but it lists the returned fields.

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

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

Schema description coverage is 100%, so baseline 3. The description adds useful context: package is an npm package name, scoped packages accepted; version defaults to latest when omitted. This goes beyond the schema.

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

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

The description clearly states the tool's purpose: a composite check for deciding whether to add an npm package, combining deps.dev (license, advisories, version history) and bundlephobia (size, dependencies, ESM/tree-shaking). It distinguishes itself from sibling tools like deep_research by specifying the exact use case.

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

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

Explicitly states when to use: for queries about safety, popularity, or cost of adding a package. It also notes what not to use: other ecosystems (PyPI, Maven, etc.) should use deps.dev:version directly. Mentions partial failures and bundlephobia timeout behavior.

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, etc. The description aligns with these, adds no contradiction, and further discloses that results include a Feature Service URL for downstream use.

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 scope, second clarifies return format and next steps. No redundant words; every phrase 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 readOnly/annotations, 100% schema coverage, and no output schema, the description fully covers the tool's behavior, return value, and integration hints. 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 covers all 3 parameters with descriptions. The description adds context about the default org (City of Harrisburg GIS) and that org_id can override it, enhancing meaning beyond schema alone.

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

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

Description clearly identifies the tool as searching City of Harrisburg GIS open geospatial datasets by keyword, with specific examples (parcels, zoning) and return fields (name, summary, record_count, url). It distinguishes from siblings like query_layer and layer_info by stating the url can be passed to them.

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 (search by keyword) and what to do with the result ('pass that url to query_layer / layer_info'). Does not explicitly list exclusion criteria or alternative tools, but the context is sufficient for agent decision-making.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, etc. The description adds internal details: BGE-base-en embeddings, cosine similarity over 500-char windows, 200K-char cap with truncation flag, and character offsets for verification. This goes well beyond the annotations.

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

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

The description is tightly written with no wasted words. The first sentence states the core purpose, followed by usage guidance, technical details, and pairing suggestion. Every sentence serves a purpose in less than 100 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?

Without an output schema, the description covers return values (top-N passages with offsets and scores), internal details (embeddings, window size, cap), and usage context (pairing with ask_pipeworx_grounded). It is fully sufficient for an agent to use the tool correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by providing example queries for the query parameter, explaining the text parameter's max length, and clarifying the limit's default and range. It enhances but does not repeat 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 opens with 'Semantic search INSIDE a fetched record' using a specific verb and resource. It further clarifies the use case by contrasting with a sibling tool (ask_pipeworx_grounded) and explaining when to use search_within, making its purpose unmistakable.

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

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

The description explicitly says 'Use when the record is too big to cram into the prompt' and pairs it with ask_pipeworx_grounded, providing both when-to-use and an alternative approach. This gives clear guidance for tool selection.

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

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

Annotations already provide safety profile (readOnlyHint=false, destructiveHint=false, idempotentHint=true). The description adds valuable behavioral details: OAuth requirement, return of subscription id, delivery channel constraints (SMS cap, webhook signing), and auto-disable on webhook failures. No contradictions, and meaningful context beyond annotations.

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

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

The description is front-loaded with the main purpose and prerequisites, then systematically covers types, delivery channels, and constraints. Though somewhat verbose, every sentence adds value, and the structure is logical. Could be slightly more concise but remains effective.

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

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

Given the tool complexity (nested params, delivery options, no output schema), the description thoroughly explains all aspects: required fields, type-specific parameters, delivery channels with constraints, return value, and failure behavior. This provides sufficient information for an AI agent to invoke correctly.

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

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

Input schema coverage is 100% with descriptions. The description adds significant meaning beyond the schema by providing concrete examples for each 'type' and 'params' combination, explaining delivery options, and clarifying constraints like SMS verification at /account. This greatly aids correct invocation.

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

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

The description clearly states the action ('Create a proactive monitoring subscription') and the resource ('to a live-data event stream'). It distinguishes itself from sibling tools like 'list_subscriptions' and 'unsubscribe' by focusing on creation, and mentions returning the new subscription id.

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

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

The description provides clear context for when to use the tool (proactive monitoring) and prerequisites ('Requires a Pipeworx OAuth account'). It does not explicitly list when not to use or compare to alternatives, but it is implicit given sibling names.

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 destructiveHint, so the safety profile is covered. The description adds valuable detail about the return format (category-bucketed example questions with tool+argument shape) and that it draws from a live catalog, going 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 relatively long but well-structured, with front-loaded purpose and clear sections. Each sentence adds value, though some lists could be slightly more concise. It effectively uses examples and formatting to improve readability.

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

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

Given the tool's simplicity (one optional parameter, no output schema, comprehensive annotations), the description is fully complete. It explains what the tool does, when to use it, how to use it with or without the topic argument, and what the return value contains. There are 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% (one optional parameter 'topic'). The description significantly enriches the schema by listing valid topic values (finance, pharma, economics, etc.) and explaining that omitting the parameter returns a cross-category spread. This adds substantial meaning beyond the schema's minimal 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 clearly states the tool is the onboarding entry point for learning what Pipeworx can do, returning category-bucketed example questions with exact tool calls. It distinguishes from sibling meta-tools like ask_pipeworx, entity_profile, and compare_entities by positioning itself as the first step to understand what the platform offers.

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

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

Explicitly says to use this '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 how to optionally narrow by topic. While it does not explicitly state when not to use it, the context makes the intended usage 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?

Adds significant context beyond annotations: explains that the row is deactivated (not deleted) and historical events remain via 'recent_alerts'. This confirms the non-destructive nature and side effects, aligning 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 compact sentences, each providing distinct value: action, ownership constraint, and consequence. 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?

Given the tool's simplicity (one parameter, no output schema), the description covers purpose, prerequisites, side effects, and related tool. Sufficient for correct invocation.

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

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

Schema coverage is 100% with a clear parameter description. The tool description merely repeats that it cancels 'by id', adding no new semantic information beyond the schema.

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

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

The description clearly states the verb 'Cancel' and resource 'subscription by id'. It distinguishes from siblings like 'subscribe' (creation) and 'list_subscriptions' (listing). The ownership constraint further clarifies 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 states ownership enforcement ('you can only cancel your own subscriptions'), which guides appropriate use. However, it does not explicitly contrast with alternative tools or mention 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 cover safety (readOnly, idempotent, non-destructive). Description adds value by detailing return format (verdicts, structured form, citation, delta) and data source (SEC EDGAR + XBRL). No contradiction with annotations.

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

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

Description is front-loaded with example queries, followed by purpose, domain, return details, and replacement value. Each sentence contributes meaning, though it could be slightly condensed without losing 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 one well-documented parameter and no output schema, the description provides sufficient context: purpose, usage, output format, domain constraints. It lacks explicit error handling or unsupported case behavior, but overall is complete enough for an AI agent to use correctly.

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

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

Schema already describes the single 'claim' parameter with examples at 100% coverage. The tool description further clarifies the supported claim types (company-financial) and provides additional context on what the tool expects, adding semantic value beyond the schema.

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

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

Description clearly states the tool performs natural-language claim verification against authoritative sources, with examples of queries it handles. It specifies the domain (company-financial claims for US public companies) and distinguishes it from sibling tools by noting it replaces multiple sequential calls, setting it apart as a compound verification 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 'Use whenever the agent needs to check whether something a user said is factually correct,' providing clear context. It also notes the current scope (v1 supports company-financial claims) but does not explicitly list when not to use or name alternative tools, leaving some gap.

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