Arcgis Puyallup

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

The description adds valuable behavioral context beyond the annotations, such as how the tool probes multiple models, the scoring range (0-100), and that Anthropic requires a BYO key with direct payment. It aligns with the annotations (readOnlyHint, idempotentHint) and provides no contradictions.

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

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

The description is a single, well-structured paragraph that fronts the purpose, then adds necessary details. Every sentence serves a purpose, with no fluff or redundancy.

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

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

Despite lacking an output schema, the description explains the return format per model (score, confidence, signals, raw_response) and a combined view, making it complete for the tool's complexity. All four parameters are adequately addressed.

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 meaning by explaining the default model, the role of _apiKey (passed through to Anthropic), and the context parameter for disambiguation, enriching 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 ('probe', 'score') and resources ('LLMs', 'visibility') to clearly state the tool's function. It distinguishes itself from sibling tools by focusing on AI visibility scoring, which is unique among the listed tools.

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

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

The description provides clear usage contexts (AI-marketing audits, pre-launch checks, competitive monitoring) and explains the default vs. paid model choice. However, it does not explicitly state when not to use the tool or compare it to siblings, though the context is sufficient for most cases.

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

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

Annotations already declare safe (readOnly, idempotent). Description adds that it returns structured answers with citation URIs, no contradictions. Some behavioral details (e.g., routing logic) are asserted but not deeply explained, still good 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?

First sentence immediately gives preference instruction. Packed with useful info but slightly long; could be trimmed without losing substance. Still very effective for an AI agent.

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

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

Describes return format (structured answer with citations) and usage scope. Lacks mention of error handling or limitations (e.g., length constraints), but adequate for a single-parameter tool with no output schema.

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

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

Schema coverage is 100%, and description adds natural language usage details, examples, and alias clarification (q, text, input, etc.). Adds significant value beyond schema.

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

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

Clearly states the tool answers questions about current/historical data from authoritative sources, routing to 5,008 tools. Distinguishes from siblings by specifying when to use grounded or 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' and provides extensive when-to-use guidance with examples. Also tells when to step up to alternatives, leaving no ambiguity.

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

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

Annotations already indicate readOnly and idempotent (safe). Description adds beta nature and source-preference ranking behavior, but does not significantly expand beyond what annotations cover. 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 detailed and well-structured, front-loading key information about beta status and behavioral differences. However, slightly verbose with parenthetical clarification.

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 sibling tools like ask_pipeworx and ask_pipeworx_grounded, this description effectively differentiates. Mentions fallback behavior and confirms it is a full working router. Adequate for expected use.

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

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

Input schema has 100% coverage with descriptions for all parameters (all aliases for 'question'). Description does not add new semantics beyond schema, meeting baseline but not exceeding.

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 identifies as a beta version of ask_pipeworx with identical routing but candidate improvements enabled. Distinguishes itself from the stable ask_pipeworx by specifying source-preference ranking behavior, making purpose unambiguous.

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

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

Explicitly states to use it exactly like ask_pipeworx when wanting the newest routing. Provides context for experimental use, but lacks explicit when-not-to-use or alternatives beyond implying comparison with the stable router.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds rich behavioral details: picks tool from 5008 sources, fills arguments, extracts answer only from tool result, returns specific fields with refusal reasons. No contradiction.

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

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

The description is a single dense paragraph, but every sentence adds value. It is front-loaded with the key concept. Slightly long but justified by the complexity of what the tool does.

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 behavior, when to use, trade-off with sibling, return format, failure modes, and cost. No output schema, but the description explicitly states return fields and refusal reasons, making it 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 all parameters as aliases for 'question.' Description adds that the tool automatically fills arguments and performs internal routing, which goes beyond schema. However, the schema already describes the main parameter 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's purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It distinguishes itself from ask_pipeworx by specifying that it extracts answers only from tool results and is for quotes/citations, while ask_pipeworx is for casual lookups.

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

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

Explicitly states when to use: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' Also gives exclusion: 'prefer ask_pipeworx for casual lookups.' Mentions extra cost trade-off.

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

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

Annotations indicate a safe, read-only, idempotent, non-destructive tool. The description goes far beyond this, detailing resolver contracts, safety mechanisms (low-confidence short-circuit, closed-market handling), response shapes, and resolution-rule risks. 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 quite long and detailed, which is partly justified by the tool's complexity. It is front-loaded with the main purpose and has clear sections. However, it could be more concise without losing 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?

Despite lacking an output schema, the description provides exhaustive information about return structures, fields, edge cases, and safety. It covers all necessary context 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% with clear descriptions for each parameter. The tool description adds context (e.g., market input varieties) but largely repeats schema info. Baseline of 3 is appropriate as description does not significantly enhance parameter understanding beyond what schema provides.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types and the process of resolving, classifying, and returning evidence. This distinguishes it from sibling tools like polymarket_edges or polymarket_arbitrage, which focus on different aspects.

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

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

The description explicitly recommends usage for questions like 'should I bet on X', 'what does the data say about Y', or 'is there edge in Z'. While it does not directly contrast with sibling tools, the context implies this is for in-depth research rather than quick edge scanning. The guidance is clear and relevant.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. Description adds valuable context: data sources (SEC EDGAR, FAERS), handling of off-calendar fiscal years, sorted results, 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 dense paragraph but every sentence adds value: example phrases, usage guidance, data source details, output format, and efficiency comparison. No filler words.

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

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

Despite no output schema, description explains return format (paired data + citation URIs). Covers both entity types, data pulled, sorting, and fiscal year handling. All essential information is present 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%. Description adds meaning: explains that 'values' are tickers/CIKs for company and drug names for drug, with min/max items. The 'type' parameter is clarified with examples. Goes beyond schema by showing usage patterns.

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

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

Description explicitly states the tool performs side-by-side comparison of 2–5 companies or drugs in one call, with example phrases like 'Compare X and Y' and 'X vs Y'. It clearly distinguishes between entity types and data sources, leaving no ambiguity.

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?

Strong guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It specifies when to use (comparing 2-5 entities) and implicitly when not to use (single entity or more than 5). The context of siblings like entity_profile reinforces this.

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 read-only, open-world, idempotent, non-destructive. Description adds: account required, paid tier for 'thorough', parallel decomposition, findings packet structure, gap recovery, contradictions, execution time. 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?

Very detailed but slightly verbose with some repetition (e.g., contradictions mentioned twice). Information-dense, but could be more streamlined without losing content.

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

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

No output schema, but description fully explains return structure (findings packet with evidence, confidence, source, citation, gaps, contradictions). Covers execution time, error scenarios (empty gaps for unsupported topics), and second-hop iteration. Comprehensive given complexity.

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

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

Schema coverage is 100%, but description adds rich detail: depth levels explained (quick=3, standard=5, thorough=8 facets) and question parameter as natural language broad/multi-part. Significantly enhances understanding beyond schema.

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

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

The description clearly states the tool's purpose: 'Grounded multi-source research across Pipeworx's 1318 STRUCTURED data sources in ONE call'. It distinguishes from sibling tools like ask_pipeworx (single lookup) and ask_pipeworx (current 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?

Provides explicit guidance: use for broad/multi-part questions over structured data, avoid for breaking news or current events (prefer ask_pipeworx). Also mentions account requirement and alternative for non-signed-in users.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the bar is lowered. The description adds useful behavioral context: results are ready to call directly with full schemas and curated examples, requiring no second lookup.

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

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

The description is front-loaded with the core purpose and usage context. It includes a list of domains which is informative but slightly verbose. Overall, it earns its place with minimal redundancy.

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

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

Given the tool's complexity (a discovery tool with many aliases, no output schema), the description fully explains what it does, what it returns, and when to use it. It is complete and 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% with full descriptions for each alias. The description does not add extra meaning beyond the schema, so the 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 explicitly states the tool's purpose: 'Find tools by describing the data or task.' It distinguishes itself from siblings by being the tool to call first when exploring available options, and lists many domains it covers.

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 guidance on when to use: 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available.' It implies not for specific single-answer tasks but does not explicitly list when-not-to-use or name alternatives.

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

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

Annotations already indicate readOnlyHint, idempotentHint, etc. Description adds value by detailing fan-out across sources, specific return fields, soft-failure for patents, news fallback, and constraints on parameter inputs.

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 with example queries first, followed by detailed behavior and outputs. While lengthy, each sentence provides unique information; minor reduction possible but not detrimental.

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

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

Given no output schema, the description thoroughly explains all return fields (cik, company_name, recent_filings with URIs, fundamentals, patents with sunset note, news, LEI). Also covers constraints and fallbacks, making it complete for an AI agent.

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

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

Schema coverage is 100%, baseline 3. The description adds meaning by explaining type is only 'company' for now, value accepts ticker or zero-padded CIK, and names are unsupported, which goes 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 it provides a full cross-source profile for US public companies, with examples like 'tell me about X' and 'company profile for Microsoft'. It explicitly distinguishes itself from chaining other tools by saying 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view'.

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 (holistic view) and when not to (if only have a name, use resolve_entity first). Provides clear context and alternatives, making it easy for the agent to decide.

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

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

Annotations already provide destructiveHint=true and idempotentHint=true, covering safety. The description adds context about clearing sensitive data and stale context, but does not disclose additional behavioral traits such as authorization requirements or irreversible effects. It adds moderate 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 sentences, front-loaded with action, followed by usage guidance. Every sentence is purposeful; 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 simplicity (single parameter, no output schema) and the presence of relevant annotations, the description provides sufficient context: purpose, when to use, and relation to siblings. No gaps remain.

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

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

Parameter schema coverage is 100%, and the description says 'by key' which is already conveyed in the parameter description. The tool description adds 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 'Delete a previously stored memory by key' and distinguishes from siblings by mentioning 'Pair with remember and recall.' It uses a specific verb (delete) and resource (memory), making the purpose unambiguous.

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

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

Provides explicit use cases: 'when context is stale, the task is done, or you want to clear sensitive data.' It also suggests pairing with remember and recall, but does not explicitly state when not to use the tool.

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

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. The description details the process: fetch page, extract title/description/key links, emit standard markdown. Fully consistent with annotations and adds behavioral context.

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

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

The description is concise, front-loaded with the main purpose, and includes additional details and examples without 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?

With 2 parameters and no output schema, the description explains inputs, process, output format, and use cases thoroughly, making it complete for agent understanding.

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

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

Schema coverage is 100%. The description adds examples for 'url' and default/max for 'max_links', beyond what the schema provides, justifying a score above the baseline of 3.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the verb 'generate', the resource 'llms.txt file', and the context for AI crawlers. It distinguishes from siblings, which are mostly research and betting tools.

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

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

The description provides explicit use cases: getting a client's site indexed, drafting for own project, auditing competitors. It lacks explicit when-not-to-use but the scenarios are specific and helpful.

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

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

Annotations already mark it as read-only, idempotent, and non-destructive. The description adds value by specifying the precise return information (fields, geometry type, count, capabilities), which supplements the annotation hints without contradicting them.

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

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

The description is a single, well-structured sentence that immediately conveys the tool's purpose and output. No unnecessary words or repetition.

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

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

Given the simple parameter and no output schema, the description adequately covers what the tool returns (fields, geometry, count, capabilities). It is fully complete for a schema retrieval tool with robust annotations.

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

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

There is only one parameter (url) with 100% schema coverage. The schema description already explains the URL format with an example. The tool description does not add additional meaning beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states it retrieves an ArcGIS layer's schema by URL, listing specific details like fields, geometry type, record count, and capabilities. This is specific and distinct from siblings like query_layer which would query data.

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

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

The description explains the tool is for getting schema by URL and provides an example format. While clear, it does not explicitly state when to use it versus alternatives like query_layer, nor does it mention exclusions.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds the specific fields returned (id, type, etc.) and the default behavior of returning only active subscriptions unless include_inactive is true.

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 effectively communicate purpose, returned fields, and usage guidance. No wasted words, front-loaded with key 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 simple nature of the tool (one optional parameter, no output schema), the description fully covers what the tool 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% with one parameter described. The description does not add additional meaning beyond the schema; it merely confirms the default behavior.

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

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

The description clearly states the tool lists the caller's active subscriptions, listing the specific fields returned. It distinguishes itself from siblings like subscribe and unsubscribe by focusing on listing.

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

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

The description explicitly states when to use: 'to review what you're monitoring before adding more or to find an id to cancel.' It provides clear context but does not 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?

Discloses behavioral traits beyond annotations: rate-limiting, free of charge, daily digest processing, roadmap impact. Annotations are all false, so description carries full burden and does so comprehensively. 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?

Description is moderately concise at 4 sentences, front-loading the main purpose. Could be slightly tighter, but no unnecessary words. Every sentence provides distinct value.

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

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

Given the tool's simplicity (3 params, no output schema), the description covers all necessary context: usage scenarios, constraints, cost, and team response. No gaps detected.

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 detailed descriptions for each parameter. The description adds value by specifying message format guidelines ('1-2 sentences typical, 2000 chars max') and emphasizing specificity. This goes beyond the schema, slightly improving usability.

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

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

The description explicitly states the tool's purpose: sending feedback about bugs, missing features, or praise. It clearly identifies the action ('Tell the Pipeworx team') and the resource (Pipeworx tools/packs), distinguishing it from siblings with context about what constitutes each type.

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

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

Provides explicit when-to-use guidance for each feedback type (bug, feature, data_gap, praise). Includes instructions on what not to do ('don't paste the end-user's prompt'), rate limits (5 per identifier per day), and cost (free, no quota). Clearly differentiates usage from sibling 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?

Beyond annotations (readOnly, idempotent, etc.), the description adds important behavioral details: self-aggregating, no PII, caching duration (5min-1h), and data source (CF analytics-engine).

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 bullet-like use cases, but could be slightly more concise; still earns its sentences.

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

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

Given the simple parameter set, good annotations, and no output schema, the description fully covers what the tool returns and its caching behavior.

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

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

Schema coverage is 100% with one parameter; the description adds interpretive context about shorter vs. longer windows, enhancing meaning beyond the schema's enum 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 returns top tools, top packs, and total call volume over a window, specifying verb+resource and distinguishing from siblings through example use cases.

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

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

Three explicit use cases are listed, providing clear guidance on when to use the tool, though it lacks explicit 'when not to use' statements.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds significant behavioral context: it explains the algorithms (monotonicity violations, partition-sum checks), the SEMANTIC ANCHOR (Jaccard similarity filter), PARTITION FILTER (placeholder slugs), and FILL CHECK (depth verification). No contradictions with annotations.

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

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

The description is front-loaded with a clear purpose and then structured into modes and additional details (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK). While lengthy, every section adds necessary information for correct usage. Could be slightly more concise but appropriate 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?

Despite no output schema, the description fully explains the response format: 'opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context)' and partition_check details. It also describes the fill_check behavior and when not to trade. All relevant aspects are covered.

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 adds substantial meaning: it explains that 'event' is for single-event mode with slug/URL and is recommended, and 'topic' is for cross-event mode with seed questions. It provides examples and clarifies usage beyond the schema.

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

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

The description begins with a specific verb+resource combination: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It clearly distinguishes the tool from sibling tools like polymarket_edges and polymarket_fill_risk by focusing on arbitrage detection through specific algorithms.

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 mode: 'Call with NO args for a trending_scan... pass event for... or topic for...' It also provides an alternative: 'For custom sizing use polymarket_fill_risk.' This gives clear context and 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 declare readOnlyHint, openWorldHint, idempotentHint as true and destructiveHint as false. The description adds substantial behavioral context: caching at KV level with 1h TTL, diagnostics for empty segments (why a segment is empty), detailed edge calculation (net of slippage, kelly_fraction capped at 0.25), and note on Fed bets exclusion. No contradictions with annotations; description significantly enhances transparency.

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

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

The description is front-loaded with the core purpose but then becomes very dense with technical details about model families, response segments, and knobs. Although every sentence adds value, the length and lack of clear section breaks reduce readability. It could be restructured for easier scanning.

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 9 parameters and no output schema, the description thoroughly explains the response structure (by_segment, fed_candidates, _diagnostics) and the fields in each opportunity (edge_pp_net, kelly_fraction, etc.). It also covers edge cases like empty segments and diagnostics for debugging. The tool's complexity is high, and the description is complete enough for an agent to use correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the rationale behind key knobs (e.g., 'tradeable-edge filter', 'skips opportunities too small'), and it discusses parameter interactions (e.g., min_partition_leg_kelly for partition arbs). While the schema already details each parameter, the description provides context that helps agents choose settings appropriately.

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 by stating the tool scans top Polymarket markets for opportunities where Pipeworx data disagrees with market price, using a clear verb ('Scan') and specific resource ('Polymarket markets'). It distinguishes itself from siblings like polymarket_arbitrage and polymarket_edge_tracker by focusing on disagreement-driven discovery, and the detailed breakdown of model families further clarifies its unique scope.

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

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

The description says it's 'Built for what should I bet on today' and explains how agents use it to discover opportunities without paging hundreds of markets. It provides explicit knobs for filtering (min_liquidity, max_spread_pp, etc.) and explains tradeable-edge filters. However, it doesn't explicitly mention when not to use this tool or compare it directly to alternatives, so guidance is clear but not exhaustive.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds substantial context: snapshot gaps meaning nobody scanned, history depth limited by 60-day TTL, and trend computation details. No contradiction with annotations.

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

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

The description is lengthy but well-structured: purpose first, then detailed output format, then limitations. Every sentence adds information, but could be slightly more concise by separating responsibilities more clearly.

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

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

Despite no output schema, the description thoroughly defines the response structure (tracked[], expired[], snapshot_dates) and tells what each field means. Also explains limitations (TTL, snapshot start). Complete for a read-only telemetry 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?

Input schema has 100% parameter descriptions, giving baseline of 3. The description adds value by explaining 'lookback' for days, 'snapshot family' for window, and the default/max values (though some redundancy). Slight extra context.

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

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

The description clearly states the tool provides 'edge persistence and decay telemetry' from daily snapshots and answers a specific question about edge age. It uses a specific verb ('answers') and distinguishes from a sibling (polymarket_edges likely provides raw data).

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

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

The description implicitly guides usage by contrasting fresh vs. aged edges ('a fresh wide edge and a 3-week-old wide edge are different trades'). However, it does not explicitly state when not to use this tool or mention direct alternatives among 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?

Annotations (readOnlyHint, idempotentHint, etc.) are present, but the description adds significant behavioral details: walks the order book ladder, returns verdict, details per-leg fill, and warns of forced directional risk and thin legs. 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 long but well-structured with clear sections (SINGLE-MARKET, BASKET) and front-loaded with core purpose. Every sentence provides distinct information; no fluff. Slightly verbose for a model but justified by complexity.

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

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

Given the tool's complexity (two modes, 4 parameters, no output schema), the description is highly complete. It explains both modes, parameter semantics, return fields, and risk warnings. Compensates fully for lack of 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%, but the description adds context beyond schema: defaults (size_usd=1000), constraints (clamp 10–1,000,000), and mode-dependent behavior (side auto-detection for baskets, interpretation of size_usd). This adds value over the schema alone.

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

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

Clearly states the tool performs a 'realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes between single-market and basket modes with specific details. It differentiates itself from sibling tools like polymarket_arbitrage and polymarket_edges by being a pre-trade check.

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

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

Explicitly instructs to use this tool before acting on polymarket_arbitrage signals or trades above ~$500, and explains the risk of partial fills converting arb to directional position. Provides clear when-to-use guidance and alternatives.

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

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

Annotations provide readOnlyHint, idempotentHint etc. Description adds SAFETY FIELDS (compatibility_warning, temporal_alignment, skipped_cross counters) that explain behaviors beyond annotations. 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 well-structured with sections (overview, modes, response, safety fields). Every sentence adds information, though slightly long.

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 explains response structure (venue leg-by-leg prices, matched spread, safety fields) and covers edge cases (compatibility_warning, temporal alignment). Complete for complexity.

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

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

Schema coverage is 100%, so baseline 3. Description adds examples and explains 'Overrides the topic-mapped side' for explicit parameters, adding value beyond schema.

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

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

The description states 'Cross-venue spread between Kalshi and Polymarket for the same resolving question', with specific verb+resource. It distinguishes from siblings like polymarket_arbitrage by focusing on cross-venue 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?

Two modes (topic shortcuts and explicit tickers) are clearly described. It warns that most pre-mapped topics return compatibility_warning today, guiding when not to expect tradeable spreads. However, it doesn't explicitly contrast with all 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?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive nature. The description adds that it returns 'attribute rows (and geometry)', providing output context beyond annotations. It does not disclose error handling, authorization requirements, or rate limits, but the annotation coverage is strong.

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

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

The description is extremely concise: two sentences that front-load the core purpose and parameters, with a useful usage tip. Every sentence adds value with no redundancy or fluff.

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

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

Given 6 parameters, no output schema, and good annotations, the description covers the tool's purpose, parameter semantics, and a usage example. Minor gaps: it does not explain error cases (e.g., invalid URL or no data) or behavior when parameters are omitted beyond defaults, but overall is adequately complete for a query tool.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by summarizing the SQL-like nature of parameters and providing a practical usage tip ('where="1=1" + out_fields="*"'). It also mentions the output includes geometry, which is 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?

The description clearly states the tool queries an ArcGIS Feature Service / Map Service layer by URL, specifying the resource, action, and linking to the sibling tool 'search_datasets' for obtaining the URL. It distinguishes from siblings like 'layer_info' (likely metadata retrieval) and 'search_datasets' (dataset discovery).

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

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

Provides a concrete usage tip ('Use where="1=1" + out_fields="*" to sample'), indicating when to use it for sampling data. However, it does not explicitly state when to prefer this tool over alternatives (e.g., 'layer_info' for metadata) or when not to use it (e.g., for non-query operations).

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 scope details (anonymous IP, BYO key hash, account ID) and pairing behavior, which is useful context beyond annotations.

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

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

The description is three sentences with no redundancy. The first sentence immediately communicates the tool's purpose, and every sentence adds value.

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

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

Despite no output schema, the description explains what the tool returns (a value or list of keys). It covers scoping, usage context, and paired tools. No gaps for this simple retrieval 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 clear parameter description. The description adds the context of being saved via 'remember', but does not significantly extend beyond what the schema 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?

The description specifies a clear action (retrieve/list) and resource (saved values/keys), and distinguishes from sibling tools like 'remember' and 'forget' by explicitly referencing 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?

The description states when to use (look up stored context) and pairs with alternatives (remember to save, forget to delete). It could be more explicit about when not to use, but the guidance is clear overall.

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=true and destructive=false. The description adds value by explaining the returned data (source, citation_uri, raw payload), the mark_read feature, and the existence of an alternative feed endpoint, enhancing transparency beyond annotations.

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

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

The description is four sentences, front-loading the core purpose, then detailing return fields, filtering, mark_read, and an alternative. Every sentence is necessary and well-organized, with no redundancy.

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

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

Despite no output schema, the description covers return fields thoroughly. It explains all five optional parameters, provides usage guidance, and mentions an alternative integration point, making the tool fully comprehensible for selection and 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?

With 100% schema coverage, baseline is 3. The description adds value by providing an example for 'type' ('sec_8k'), clarifying 'since' as ISO timestamp, explaining 'mark_read' behavior, and implicitly covering 'unread_only'. This enriches parameter understanding.

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

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

The description clearly states the verb 'Pull' and the resource 'fired events from your subscription feed'. It specifies return fields (source, citation_uri, raw payload) and filtering options, distinguishing it from sibling tools like 'list_subscriptions'.

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

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

The description provides clear context: 'Polls work fine' and mentions an alternative access method. It explains the mark_read parameter's effect on subsequent calls. However, it does not explicitly state when not to use this tool versus other sibling 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?

The description provides extensive behavioral context beyond the annotations: it reveals it fans out to multiple external APIs (SEC EDGAR, GDELT→GNews fallback, USPTO), describes fallback behavior, mentions API sunset and soft-fail, and details the return structure. Annotations already mark it as readOnly and idempotent, and the description is consistent.

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

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

The description is information-dense but well-structured, starting with example queries and then detailing sources and parameters. It is slightly long but every sentence adds value. Could be more concise by removing some example phrasing, but overall 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 complexity (multiple data sources, fallback logic, parameter flexibility) and the absence of an output schema, the description covers all necessary aspects: what it returns (changes array, counts, citation URIs), how errors are handled (fallback, soft-fail), and parameter usage. It is complete for selecting and invoking 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 already has 100% coverage for parameter descriptions. The description adds value by explaining the `since` shorthand options (ISO date, relative like '7d', '30d', '3m', '1y') and recommending typical values. It also clarifies that `value` accepts ticker or CIK. This goes beyond the schema's basic descriptions.

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

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

The description clearly states the tool provides a change feed for a company in the last N days, covering SEC filings, news, and patents. It distinguishes itself from the sibling entity_profile tool, which provides static profiles. The purpose is specific and immediately understandable.

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 this tool versus entity_profile ('Use entity_profile instead when you want the static profile'). It also includes example queries and recommends common values for the `since` parameter. However, it does not provide explicit exclusions or when-not-to-use cases beyond the single alternative.

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 (idempotentHint=true, readOnlyHint=false), description adds important behavioral details: key-value storage scoped by identifier, 24-hour retention for anonymous sessions, persistent storage for authenticated users. 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 purpose, usage guidance, and constraints. Slightly verbose but every sentence adds value. Front-loaded with core purpose.

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 explains storage behavior, scoping, and persistence. Complete for a simple write tool, though could mention if there is a confirmation response.

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 applies. Description provides examples for key (e.g., 'subject_property', 'target_ticker') and value (any text), which adds minor context but mostly restates schema info.

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

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

Description explicitly states the tool saves data for later reuse across conversations/sessions. It uses clear verb+resource 'Save data' and distinguishes from siblings like recall and forget.

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

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

Provides explicit guidance on when to use (discover something worth carrying forward) and mentions complementary tools (recall, forget). Also explains scoping and persistence differences between authenticated and anonymous users.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false. The description adds that each call cascades through multiple internal endpoints, auto-disambiguates for companies, and replaces manual lookups. 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 single focused paragraph that efficiently conveys purpose, usage, and type details. Every sentence adds value without redundancy. Could be slightly more structured into bullet points, but it's already concise and readable.

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 explicitly states what is returned for each type (ticker+CIK+name+citation for company; RxCUI+ingredient+brand+citation for drug). It covers both required parameters and the internal cascade behavior, making it complete for an agent to use.

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

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

Schema coverage is 100%, so the schema documents both parameters fully. The description adds examples of valid inputs (ticker, CIK, name for company; brand/generic for drug) and notes auto-disambiguation for companies, providing useful context beyond the schema.

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

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

The description starts with typical user queries ('What's the ticker for...') and clearly states the core purpose: resolve a name to a canonical identifier required by other tools. It distinguishes itself from siblings by being the first tool to use when needing an ID from a name.

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 FIRST whenever you have a name but need an ID.' It also notes that using this tool replaces 2-3 manual lookups, providing efficiency context. No sibling tool duplicates this resolution function.

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

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

The description adds significant behavioral context beyond annotations: it internally calls ai_visibility_check, ranks entities by score, and highlights most/least recognized. It also specifies that the first entity is treated as the subject. Annotations already cover readOnly, idempotent, etc., so the description complements them well.

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

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

The description is four sentences, front-loading the main action, then explaining process, use case, and output. Every sentence adds value with no redundancy.

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

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

Given the tool's complexity, annotations, and full schema descriptions, the description covers all necessary aspects: purpose, internal mechanism, input constraints, output structure, and use case. 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?

While schema coverage is 100%, the description adds critical meaning: 'First entry treated as the subject for narrative; rest are competitors' and explains the context parameter with an example. This goes beyond the schema descriptions.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, using ai_visibility_check internally. It distinguishes from siblings by focusing on multi-entity comparison and ranking.

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 it's useful for competitive AI-marketing audits and provides an example. It implies when to use it but does not explicitly contrast with the single-entity sibling ai_visibility_check, which could be clearer.

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

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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description adds critical behavioral context: it fans out to multiple sources, handles partial failures gracefully, and notes that bundlephobia's first measurement may take 5-30s. This transparency aids agent expectation management.

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

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

The description is dense but concise, covering purpose, usage, fan-out details, ecosystem limits, partial failures, and return structure in a compact block. No superfluous sentences; every sentence earns its place.

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

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

Given the tool's complexity (no output schema, composite nature), the description fully compensates by listing all return fields, explaining partial failure behavior, and specifying ecosystem limitations. It leaves no important aspect unaddressed.

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

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

Schema coverage is 100%, so the description does not need to add much. It provides examples (e.g., '18.3.1' for version, '@types/node' for scoped packages) which are helpful but not essential. Baseline 3 is appropriate.

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

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

The description clearly defines the tool as a composite check for npm packages, specifying the verb 'scan' and the resource 'dependency'. It distinguishes itself from siblings by focusing on package health (safety, popularity, size), which is unique among the listed sibling tools.

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

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

The description explicitly states when to use: when an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'. It also provides alternatives by noting that other ecosystems (PyPI, Maven, etc.) should use 'deps.dev:version directly', offering clear usage guidance.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds value by specifying the exact return fields (name, summary, record_count, owner/org, url) and the purpose of the url, complementing the annotations without contradiction.

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

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

The description is two sentences, front-loaded with purpose and scope, and efficiently includes output details and next steps with zero unnecessary words.

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

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

For a search tool with full parameter descriptions and good annotations, the description is complete: it specifies scope (City of Puyallup GIS), return fields, and usage pattern. No output schema is needed given the clarity of the description.

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

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

Schema coverage is 100% with descriptions for all three parameters (limit, query, org_id). The description does not add parameter-level details beyond the schema, so a 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 the tool searches City of Puyallup GIS open geospatial datasets by keyword, listing specific examples (parcels, zoning, etc.). It distinguishes itself from sibling tools like query_layer and layer_info by explaining how the returned URL feeds into 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?

It explains when to use: to search for geospatial datasets by keyword, and how to use the results (pass URL to query_layer/layer_info). It doesn't explicitly state when not to use or compare with other search tools like deep_research, but the context is clear and sufficient.

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

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

Annotations already declare readOnlyHint and idempotentHint. Description adds important behavioral details: returns character offsets and similarity scores, uses BGE-base-en embeddings with cosine over 500-char windows, 200K char cap with truncation and flagging. 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 approximately 5 sentences with clear structure: purpose, usage, technical details. Every sentence adds value without redundancy. Efficient and well-organized.

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

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

No output schema exists, so description must explain return values. It specifies 'top-N passages with character offsets and similarity scores', which is sufficient. Also covers truncation behavior. Could mention response format (e.g., JSON array) for completeness, but still adequate.

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

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

Schema coverage is 100%, so baseline is 3. Description adds examples for query and explains limit default, but the schema already covers all parameters adequately. No significant additional semantic value.

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

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

The description clearly states the specific verb 'search inside' and resource 'a fetched record'. It distinguishes from siblings like ask_pipeworx_grounded by explaining its use case for large records that cannot fit in a prompt.

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 context for when to use: when the record is too large for the prompt. Mentions pairing with ask_pipeworx_grounded, implying when not to use standalone. Could be more explicit about alternatives, but clear overall.

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 (idempotent, open world, not destructive), the description adds crucial behavioral details: requires OAuth, delivery channel capabilities, phone verification, webhook signing, and auto-disabling after failures. 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 somewhat lengthy but well-structured, front-loading purpose and return value. It organizes content by supported types and delivery channels with examples. Given the complexity, it is appropriately concise without wasted 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 (3 parameters with nested objects, no output schema), the description is exceptionally complete. It covers all types, parameters, delivery options, prerequisites, and constraints. An agent has enough information to invoke the tool correctly.

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

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

Schema coverage is 100% with descriptions for all parameters. The description adds value by providing concrete examples for each type and delivery channel, and constraints like phone verification and webhook secret. 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 verb (create), resource (proactive monitoring subscription to a live-data event stream), and return value (subscription id). It distinguishes from siblings like list_subscriptions and unsubscribe by emphasizing proactive monitoring and persistent subscription.

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

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

The description specifies when to use (proactive monitoring) and prerequisites (Pipeworx OAuth account). It also mentions constraints (phone verification, 10/day cap). However, it does not explicitly contrast with alternatives like recent_alerts for pull-based monitoring.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds that the tool returns category-bucketed examples drawn from a live catalog, and explains behavior with and without the topic parameter. 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 somewhat long but well-structured, starting with trigger phrases, then purpose, output, and usage details. Every sentence adds value, and it is not overly verbose. Good front-loading of key 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?

Despite lacking an output schema, the description fully explains the return structure (category-bucketed example questions with exact tool+argument shape) and usage. It covers when to use, how to use with/without topic, and the source (live catalog). Complete for a simple onboarding 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% (1 parameter fully described). The description adds meaningful context: omitting topic gives full spread, passing topic focuses; it also lists example topics. This goes beyond the schema's 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 an onboarding entry point for a newly connected agent, returning category-bucketed example questions with exact tool+argument shape. It distinguishes between calling with no arguments for full spread and with a topic for focus, effectively differentiating it from siblings like ask_pipeworx and discover_tools.

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

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

Explicit guidance: '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 explains when to pass topic vs not, though it doesn't explicitly state when NOT to use it. Still, the context is clear from 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 already indicate non-destructive, non-read-only, and idempotent behavior. The description adds important context about soft deletion ('deactivated, not deleted') and ownership enforcement, which complements the annotations without contradiction.

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

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

The description is extremely concise—two sentences that immediately state the purpose and key behaviors with no unnecessary words.

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

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

For a simple tool with one parameter and no output schema, the description covers purpose, ownership, deactivation behavior, and historical data availability, making it fully adequate for an agent to understand usage.

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

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

Schema coverage is 100%, so the schema already documents the parameter. The description does not add new semantic details beyond referring to the 'id', but it implicitly relates it to ownership context.

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

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

The description clearly states the action ('Cancel a subscription by id') with a specific verb and resource, and it distinguishes from sibling tools like 'subscribe' and 'list_subscriptions'.

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

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

It provides explicit ownership constraints ('you can only cancel your own subscriptions') and clarifies the effect ('deactivated, not deleted'), helping the agent decide when to use this tool versus alternatives. However, it does not explicitly state when not to use it.

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 read-only, open-world, idempotent, non-destructive. The description adds substantial behavioral context: two verification paths (financial vs other), exact percent-delta math, returns verdict with citation and reasoning, and replaces sequential calls. 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 information-dense with good front-loading of synonyms and key use cases. It could be slightly more concise, but the structure with dashes helps 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?

Despite no output schema, the description fully explains return values (verdict, actual value, reasoning, citation). It covers both claim types, tolerance parameter, and the replacement of multiple calls, making it 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?

The input schema covers both parameters (claim and tolerance_pct) with 100% description coverage. The description provides examples of claim values and explains the tolerance_pct default, adding slight value but not exceeding baseline for full schema coverage.

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

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

The description clearly specifies the tool's purpose: natural-language claim verification against authoritative sources. It provides multiple trigger phrases and distinguishes between company-financial and other claims, making it highly specific and unambiguous.

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

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

The description states 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains when to use the structured vs grounded pipeline, but does not explicitly compare to sibling tools or state when not to use.

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