Gsa Auctions

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds important context: the probing nature, cost implications (free vs. BYO key for Anthropic), and the output structure. 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 compact yet comprehensive: it front-loads the main function and scoring, then explains options, output, and use cases in a logical flow. Every sentence adds value, and it is well-structured for quick reading.

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

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

Given no output schema, the description adequately explains the return format (per-model score, confidence, signals, raw_response + combined view) and use cases. It does not cover error handling or rate limits, but the annotations and schema descriptions compensate. Overall, it provides sufficient context for the tool's complexity.

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

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

Schema coverage is 100%, and the description adds meaning beyond the schema. It explains that 'workers-ai' is the free default, that '_apiKey' is specifically for Anthropic, and that 'context' helps disambiguate entities. This provides useful guidance beyond the parameter descriptions.

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

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

The description clearly states the tool probes LLMs for knowledge about a business/brand/product/topic and scores visibility (0-100). It specifies the default model and optional Anthropic probing, and lists the return structure. This is specific and distinguishes it from sibling tools like compare_entities or entity_profile.

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

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

The description explicitly states the tool is useful for AI-marketing audits, pre-launch brand checks, and competitive monitoring. It explains when to use the free default model vs. when to provide an API key for Anthropic. It does not mention alternatives or when not to use, but the context is sufficiently clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds that it returns structured answers with stable citation URIs, routes to one of 5,245 tools, and works on every tier. No contradictions.

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

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

The description is lengthy but well-structured with examples, clear differentiation from siblings, and a logical flow. Could be slightly more concise, but every sentence adds value.

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

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

Given the complexity (6 alias parameters, no output schema), the description thoroughly covers routing behavior, citation format, example queries, and relationships to sibling tools. It provides 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 description coverage is 100% and all parameters are aliases for 'question'. The description doesn't add semantic detail beyond what's in the schema, but it does provide guidance on what types of questions to ask, which is helpful context.

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

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

The description clearly states the tool routes questions to verified sources for structured data with citations, and explicitly says to prefer it over web search. It distinguishes from siblings like ask_pipeworx_grounded and deep_research, and provides extensive examples.

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 (factual questions) and when-not-to-use (use ask_pipeworx_grounded for hallucination-resistant answers, deep_research for broad questions). Also lists example queries and identifies it as the default entry point.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds context: it is currently identical to ask_pipeworx (no active candidate), confirms it is a full working router (not a fallback), and explains the experimental nature.

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

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

The description is a single paragraph with all necessary information, but slightly verbose with specific details like the retirement date. Every sentence contributes value, and it is front-loaded with 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 the tool's complexity (beta vs stable, experimental router) and the rich annotations, the description covers the key aspects: identity, usage, current behavior, and experimental purpose. No output schema exists, but description doesn't need to detail return values as it's a router.

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

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

Schema coverage is 100% and the description notes 'same arguments' without adding new semantic details. The schema already documents all parameters, so the description does not significantly enhance understanding beyond the schema.

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

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

Clearly states it is a beta version of ask_pipeworx, an identical universal router with the same tools and arguments. Distinguishes from the stable ask_pipeworx by noting it includes candidate routing improvements for testing.

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, and that results are compared to the stable router to decide merges. Implicitly advises against using it if stability is required, implying the alternative ask_pipeworx.

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

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

Annotations already indicate readOnly, idempotent, and non-destructive. The description adds significant behavioral context: how the tool selects tools, fetches data, extracts answer only from result, and returns explicit refusal reasons. No contradiction with annotations.

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

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

Description is well-structured with purpose first, then mechanism, usage, return format, refusals, and cost. Every sentence adds value, though slightly verbose in listing domains. Good front-loading.

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 fields (answer, evidence, confidence, source, fetched_at, refusal_reason) and refusal reasons. Covers cost and comparison to sibling. Complete for a complex tool with high-stakes 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% with all parameters described as aliases for question. The description adds little beyond 'natural language' but does not clarify format or scope. Baseline of 3 is appropriate since schema is sufficient.

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 is a 'hallucination-resistant answer mode for high-stakes reads' and distinguishes from ask_pipeworx by detailing the grounded extraction behavior and extra LLM call. The verb 'extracts' and resource 'tool result' are specific.

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

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

Explicitly specifies when to use (high-stakes reads where answers will be quoted, cited, or acted on, e.g., financial verdicts, legal claims) and when not to (prefer ask_pipeworx for casual lookups). Provides clear context with domains and 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?

The description goes beyond annotations by detailing behaviors such as low-confidence match handling, closed market handling, wide-spread markets, resolution-rule risk, and response shapes. All information is consistent with annotations (readOnlyHint, idempotentHint).

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

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

The description is lengthy and thorough, which is justified given complexity. However, it could be more concise by reducing redundancy (e.g., repeating examples across paragraphs). Front-loading is good.

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

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

Without an output schema, the description fully explains return values (resolver contract, parent events, news fields) and covers safety, edge cases, and blocking conditions. It is contextually complete for an agent to use effectively.

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

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

Schema coverage is 100% with parameter descriptions already present. The tool description adds context (e.g., examples, default depth explanation) but does not significantly enhance parameter meaning 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: research a Polymarket bet by pulling Pipeworx data. It specifies input types (slug, URL, question text) and distinguishes from sibling tools by its research and data-packing functionality.

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 like 'should I bet on X', 'what does the data say about Y', or 'is there edge in Z'. It also includes classifiers and fan-out examples 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?

Beyond the annotations (readOnlyHint, etc.), the description discloses specific behavioral traits: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, result sorting by primary metric, and efficiency (replaces 8-15 sequential lookups). This provides rich actionable context without contradicting annotations.

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

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

The description is packed into one paragraph but remains efficient and information-dense. It front-loads examples and usage patterns. While every sentence adds value, the structure could be slightly improved (e.g., bullet points) without losing conciseness. Nevertheless, it is highly effective.

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

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

Given the tool has only 2 simple parameters, no output schema, and comprehensive annotations, the description fully covers what the agent needs to know: data sources, handling of edge cases (fiscal years), result ordering, and efficiency benefits. It is complete.

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

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

With 100% schema coverage, the description still adds significant meaning: it explains the enum values for 'type' (company vs drug) and elaborates on 'values' format (tickers/CIKs for companies, drug names for drugs). It also clarifies constraints like maxItems:5 and minItems:2. This goes beyond the raw 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 that the tool performs side-by-side comparison of 2-5 companies or drugs in a single parallel call. It includes explicit examples of natural language triggers (e.g., 'Compare X and Y', 'which is bigger') and specifies the entity types (company, drug). It distinguishes itself from sequential lookups, making the purpose unmistakable.

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

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

The description explicitly instructs 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It provides clear guidance on when to use each entity type ('type="company" pulls latest 10-K...', 'type="drug" pulls FAERS...'). This gives the agent unambiguous direction on tool selection and usage context.

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

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

Beyond the annotations (readOnlyHint, openWorldHint, etc.), the description details expected latency (15-60s, up to ~90s), output structure (gaps[], contradictions[], hop field, citations), and behavior (never invents, semantic excerpts). No contradictions with annotations.

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

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

The description is lengthy but efficiently packs dense information. Front-loaded with critical prerequisites (account, alternative tool) and follows a logical flow. Minor redundancy in explaining hops could be trimmed, but overall well-structured.

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

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

Despite no output schema, the description thoroughly explains the return format, behavior across depth levels, and constraints. Given the tool's complexity (many sources, parallel tools, multiple hops), the description is remarkably complete.

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

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

Schema coverage is 100%, and the description adds significant value by explaining the depth parameter's enum values (quick/standard/thorough) with their behavior (number of facets, hops, contradictions scan). The question parameter is self-explanatory as natural language.

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

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

The description clearly states the tool performs grounded multi-source research across structured data sources, decomposes questions into facets, and returns a findings packet. It explicitly distinguishes from sibling tools like ask_pipeworx, making the purpose unambiguous.

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

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

The description provides explicit guidance: use for broad/multi-part questions over structured data, avoid for single lookups (use ask_pipeworx) or breaking news (prefer ask_pipeworx). It also notes account requirements and a paid tier for 'thorough' depth.

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

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

Beyond annotations (readOnly, idempotent, non-destructive), the description adds that it returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed.' This describes output behavior and no side effects, consistent 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 efficient: front-loaded with purpose, then usage context, then output details, then when to call. Every sentence adds value with no fluff, striking a balance between brevity and completeness.

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

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

Despite no output schema, the description fully compensates by explaining what is returned (top-N tools with schemas) and provides example queries. It covers input, output, and usage context completely for a discovery tool.

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

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

Schema coverage is 100%, so each parameter already has a description. The tool description adds the list of example queries and mentions aliases but does not significantly deepen parameter understanding beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It lists specific domains and distinguishes from sibling tools by noting 'Call this FIRST when you have many tools available and want to see the option set.' This is a specific verb+resource with clear differentiation.

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

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

Explicitly says 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST...' implying when not to use (when you already know the tool or want a single answer). Provides clear usage context and alternatives.

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

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

Discloses behavior beyond annotations: fans out across multiple sources, mentions USPTO sunset with soft-fail, specifies ordering of fundamentals, news fallback chain, and URI format for filings. 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 lengthy but all sentences are informative and relevant. It front-loads purpose with example queries. Given the tool's complexity (multiple data sources), the length is justified and well-structured.

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

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

Despite no output schema, the description thoroughly explains what is returned: cik, company_name, recent_filings (with URIs), fundamentals (specific fields and sorting), patents, news, LEI. Covers fallback behavior and corner cases (USPTO sunset).

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 repeating and clarifying that 'type' is only 'company' and 'value' is ticker or CIK, with examples. Provides additional context beyond schema but schema already describes parameters adequately.

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

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

The description explicitly states the tool provides 'full cross-source profile of a US public company in ONE parallel call' and lists specific data sources (SEC, XBRL, USPTO, news, GLEIF). It differentiates from siblings by advising to prefer this tool over chaining individual lookups 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?

Includes example queries, specifies accepted inputs (ticker or CIK), notes that names are not supported, and directs users to 'resolve_entity' first if only a name is available. Provides clear when-to-use guidance for holistic company profiles.

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

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

Annotations already declare destructiveHint=true and idempotentHint=true, so the description's 'delete' aligns but does not add new behavioral insights beyond what annotations provide. 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?

Two sentences, no wasted words. The first sentence delivers the core action; the second provides usage guidelines. Front-loaded and efficient.

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

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

Given the simple one-parameter tool with no output schema, the description fully covers purpose, usage, and necessary context. Complete for its complexity.

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

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

Schema coverage is 100% for a single parameter 'key' with a clear description. The tool description adds no additional meaning beyond the schema, meeting the baseline.

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

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

The description clearly states the verb 'Delete' and the resource 'previously stored memory by key'. It distinguishes itself from sibling tools like 'remember' and 'recall' by specifying the action of deletion.

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

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

Explicitly provides three scenarios for use: stale context, task completion, and clearing sensitive data. Also suggests pairing with 'remember' and 'recall', giving clear context on when to use this tool versus alternatives.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: it fetches the page, extracts title/description/key links, and emits standard llms.txt markdown format, as well as noting the output is a single text blob. 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 two concise sentences followed by a bullet list of use cases. Every sentence adds value: the first explains the core function and output, the second details the process, and the list clarifies practical applications. 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 low complexity (2 parameters, no nested objects, no output schema), the description is complete. It explains what the tool generates, the input required, the process, and the output format. There is no missing information needed 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?

Both parameters have full descriptions in the input schema, so schema coverage is 100%. The description adds examples for the 'url' parameter (e.g., 'https://example.com') and specifies defaults and limits for 'max_links' (default 25, max 50), providing extra 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 clearly states the specific action: generating a production-ready llms.txt file for any URL. It explains the process (fetches, extracts, emits) and the output format, distinguishing it from sibling tools like 'scan_competitor_ai_presence' which focus on scanning rather than generating. The verb 'generate' plus resource 'llms.txt file' is precise.

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 projects, auditing competitors. This helps the agent select the tool. However, it does not contrast with alternatives or give when-not-to-use guidance, which leaves some ambiguity among AI visibility tools.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by listing the full set of return fields (item name, descriptions, dates, bids, etc.), which is behavioral context beyond the annotations.

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

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

The description is one sentence that front-loads the primary purpose, then enumerates return fields. It is efficient but the long list of fields could be condensed or omitted since it's a list; overall, it's clear and not verbose.

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

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

With no output schema, the description compensates by listing 16+ return fields, providing significant detail. It covers input (sale_no, lot_no) and output comprehensively. Minor gap: no mention of behavior when the lot doesn't exist or error cases.

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

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

Schema coverage is 100% and both parameters are documented. The description mentions 'identified by its sale number and lot number' but adds no semantic detail beyond what the schema already provides (name, type, description). 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 it returns 'full detail for a single GSA Auctions lot' and lists the specific fields returned. It clearly differentiates from siblings like gsa_search_auctions (search/filter) and gsa_auctions_closing_soon (time-limited list).

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

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

The description implies usage when you have a specific sale number and lot number, but provides no explicit guidance on when to use this tool versus siblings, no prerequisites (e.g., you need to first search for a sale), and no when-not-to-use conditions.

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 (readOnlyHint, etc.), the description adds that it returns specific fields (sale/lot number, item name, location, end date, status, high bid, reserve) and is sorted by end date ascending. 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?

Two efficient sentences: first defines what the tool does, second states use case and return fields. No fluff, well front-loaded.

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

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

For a simple read-only list tool with one parameter, the description provides all needed context: purpose, sorting, return fields. No output schema exists, but return fields are listed, 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% for the single parameter 'limit', which is well-documented in the schema. The description does not add further semantic meaning about the parameter beyond the schema.

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

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

The description clearly states it lists GSA auctions ending soonest, sorted by end date ascending, with the specific purpose 'find last-chance bidding opportunities'. This distinguishes it from siblings like 'gsa_auction_lot' (single lot detail) and 'gsa_search_auctions' (search).

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 to find last-chance bidding opportunities'. While it doesn't state when not to use, the sibling tool names (gsa_auction_lot, gsa_search_auctions) imply alternatives for other needs.

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

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

Annotations already declare it's read-only, idempotent, open-world, and not destructive. The description adds that the upstream API has no server-side category filter, which is critical behavioral context. No contradictions.

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

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

Three sentences plus a note, each sentence adds unique value. Front-loaded with purpose and resource. 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?

No output schema, but the description lists returned fields (sale/lot number, item name, etc.). Covers all parameters and provides examples. Complete for a search tool with no required parameters.

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

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

Schema coverage is 100% with detailed descriptions. The addition adds value: limit default 25, state as 2-letter code, keyword matched against item name, status case-insensitive substring. Examples in the schema further clarify.

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

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

The description uses specific verbs and resources: 'Search current US federal government surplus auctions from GSA Auctions'. It clearly distinguishes from siblings like gsa_auction_lot (single lot) and gsa_auctions_closing_soon (subset). The mention of 'api.data.gov' provides context.

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

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

Explicitly states filtering by state, keyword, and status. Includes a NOTE about using keyword for category/type due to API limitations. Provides examples in the schema. Lacks explicit when-not-to-use guidance but is clear on context.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. Description adds return field detail and default behavior of include_inactive parameter, but does not contradict 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 efficient sentences: first states purpose and return fields, second provides usage context. Every sentence serves a purpose 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?

Fully covers the tool's function, return data, parameter behavior, and usage context. No gaps despite lack of output schema, as return fields are enumerated.

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 clarifies the default value of include_inactive (false) and its effect, adding value beyond the schema definition.

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

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

Clearly states it lists the caller's active subscriptions, with explicit return fields (id, type, params, created_at, last_fired_at, fire_count). This distinguishes it from sibling tools like subscribe and unsubscribe.

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

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

Explicitly advises use for reviewing existing subscriptions before adding more or finding an id to cancel, giving concrete when-to-use guidance.

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

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

Discloses rate limiting (5 per identifier per day), that it's free, and how feedback is used (daily digests, affects roadmap). Annotations only have readOnlyHint false etc., so description adds significant behavioral context 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?

Single paragraph, front-loaded with purpose, then usage guidelines, then behavioral notes. Every sentence adds value; no repetition or fluff. Efficiently communicates all necessary 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?

For a simple feedback tool with no output schema, the description covers purpose, when to use, how to write, constraints (rate limits, quota), and team response. 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?

Input schema has 100% coverage with descriptions, so baseline is 3. Description adds extra guidance on writing style (refer to tools/packs) and message length (2000 chars), enhancing semantics. Slightly above baseline.

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

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

Description clearly states the tool is for sending feedback to the Pipeworx team, enumerating specific subtypes (bug, feature, data_gap, praise). Distinct from sibling tools which are informational or query tools, so purpose is 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 tells when to use: for bugs, missing features, or praise. Provides clear instructions on how to write feedback (describe in terms of tools/packs, no end-user prompts) and notes rate limits and quota. Leaves 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?

Beyond the annotations (readOnlyHint, etc.), the description adds transparency about data source (CF analytics-engine), no PII, and caching behavior (5min-1h). 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 efficient: front-loaded with purpose, followed by use cases and technical details. No redundant 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 and no output schema, the description fully covers what the tool returns and its caching behavior. It is comprehensive for its complexity.

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

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

The single parameter 'window' has a clear enum and description in the schema. The description repeats this information but adds nuance about hot vs. steady-state demand. Schema coverage is 100%, so baseline 3 is appropriate.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over a recent window. It uses specific verbs and resources, distinguishing it from sibling tools like 'ask_pipeworx' or '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?

Three explicit use cases are provided, giving clear context for when to use the tool. However, it does not explicitly state when not to use it 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?

The description thoroughly discloses internal behaviors: monotonicity checks, partition checks, semantic anchor, partition filter, fill check, and response structure. This goes well beyond annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), adding critical details for agent decision-making.

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

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

The description is well-structured with sections like SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK. It front-loads the main purpose and each sentence adds value. Slightly verbose due to technical details, but necessary for the tool's complexity.

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

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

Given the tool's complexity and lack of output schema, the description is highly complete. It explains inputs, internal logic, response fields, fill check, and trading caveats. No gaps are apparent.

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

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

Schema coverage is 100% with descriptions. The description adds significant value: explains event slug format, that full URLs work, provides examples, and clarifies topic usage as a seed question. This exceeds what the schema provides.

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

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

The description clearly states it finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes modes (trending_scan, event, topic) and is unique among sibling tools like polymarket_edges and polymarket_fill_risk.

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

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

The description explicitly explains when to use each mode: no args for trending scan, event for specific market, topic for cross-event scanning, with recommendations. It provides context for each mode but does not explicitly exclude other tools or state when not to use this tool.

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

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

Aligns with annotations (readOnly, idempotent, etc.) and goes beyond by detailing caching (1h KV-level), filter diagnostics, response structure, and edge calculations. Discloses limitations like Fed bet signal reliability. 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 comprehensive but verbose with multiple paragraphs. While well-organized into sections, it could be trimmed without losing essential guidance. Every sentence adds value, but front-loading is adequate.

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

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

Given no output schema, the description fully explains return fields (by_segment, fed_candidates, _diagnostics) and all edge metrics (edge_pp_net, kelly_fraction, etc.). Covers knobs, segments, and diagnostics, making tool usage clear for agents.

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

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

All 9 parameters have schema coverage, and the description adds practical context (e.g., examples for min_kelly, slippage_pp defaults, partition arb Kelly behavior). Adds value beyond schema alone, though baseline is high due to full coverage.

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

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

The description clearly states the tool scans Polymarket markets for opportunities where Pipeworx data disagrees with market price, explicitly targeting 'what should I bet on today' use case. It details three response segments, distinguishing it from sibling tools like polymarket_arbitrage and polymarket_edge_tracker.

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

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

Provides explicit usage context ('what should I bet on today') and practical guidance on knobs like min_liquidity, max_spread_pp, and min_partition_leg_kelly. Also explains Fed bet exclusion and partition arb Kelly behavior. However, lacks explicit when-not-to-use or direct comparison with 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?

Beyond annotations (readOnly, openWorld, idempotent, non-destructive), the description fully explains the response structure with three arrays (tracked, expired, snapshot_dates) and their fields. It also details how decay and trend are computed, and notes limitations such as history depth bounded by 60-day TTL and daily close data source.

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

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

The description is well-structured with a clear lead sentence, then parameter details, response format, and limits. It uses bullet-like formatting for readability. While comprehensive, it could be slightly more concise by removing redundancy with the schema.

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 lack of output schema, the description thoroughly explains the return structure, including the purpose of each array and key fields like trend, decay_pp_per_day, lifespan_days, and snapshot_dates. It also covers limitations and data source nuances, making the tool self-contained for an agent.

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

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

Both parameters have schema descriptions; the description adds minor clarifications (e.g., max 30 for days, default values). Since schema coverage is 100%, baseline is 3; the description provides limited additional semantic value beyond what the schema already conveys.

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

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

The description clearly states the tool's purpose: analyzing edge persistence and decay over time using daily snapshots. It provides a specific question it answers and uses a concrete example to illustrate its value, effectively distinguishing it from sibling tool polymarket_edges which likely provides current edges only.

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

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

The description includes parameter details (days, window) with defaults and limits, and mentions constraints like snapshot TTL. It implies when to use this tool (for historical edge analysis) versus polymarket_edges (for current edges), but does not explicitly state scenarios where it should not be used or provide direct comparisons to 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=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context such as walking the order book ladder, returning verdicts (clean|degraded|cannot_fill), and highlighting forced directional risk. However, it contradicts the schema by implying one of market or event is required when schema leaves both optional, which slightly reduces clarity. 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 detailed and dense, using all caps for key terms to aid scanning. It is structured with a purpose statement, required parameter note, then separate mode explanations, and ends with when-to-use advice. While somewhat verbose, every sentence adds value and there is no redundant content. Could be slightly more concise but still efficient.

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

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

For a tool with no output schema, the description exhaustively lists return values (top_of_book, vwap_fill_price, slippage_pp, etc.) and explains the verdict. It covers both modes thoroughly, includes edge cases like thin legs and forced directional risk, and provides parameter constraints (size_usd clamp 10–1,000,000). This makes the tool self-contained and complete for agent usage.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds meaning beyond the schema: for size_usd it explains 'max spend on buys, target proceeds on sells' for single-market and 'settlement notional — shares per leg' for basket. It also describes side default logic for basket mode. This additional context justifies a score of 4.

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

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

The description clearly states it performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes between single-market and basket modes. The verb 'check' and resource 'fill risk' are specific, and the detailed explanation of outputs differentiates it from sibling tools like polymarket_arbitrage or polymarket_edges.

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

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

Explicitly states when to use: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Provides rationale about theoretical overround not being capturable and partial basket fills causing directional risk. Also specifies required parameter conditions, making usage clear.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds rich detail on compatibility_warning conditions (shape mismatch, semantic mismatch), temporal_alignment, and skipped cross counters, going far beyond the annotations.

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

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

The description is structured with clear sections (TWO MODES, RESPONSE, SAFETY FIELDS) and each sentence adds necessary detail. While somewhat lengthy, the complexity of the tool warrants the length, and it remains well-organized.

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

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

Given no output schema, the description fully explains return values (leg-by-leg prices, spreads, compatibility_warning, temporal alignment, skip counters) and covers edge cases and interpretation, making it self-contained for a complex cross-venue tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the two modes, showing examples, and clarifying that explicit parameters override the topic-mapped side. This provides context beyond raw schema fields.

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: computing the cross-venue spread between Kalshi and Polymarket for the same question. It distinguishes itself 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?

The description explicitly outlines two modes (topic shortcuts vs explicit tickers) and explains when spreads are meaningful (aligned temporal, matched pairs) versus when warnings fire. It advises that most pre-mapped topics currently return compatibility warnings, guiding appropriate use.

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

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

Annotations already declare readOnlyHint and idempotentHint, so the tool's safety is clear. The description adds contextual behavior (scoping to user identifier, pairing with remember/forget) but does not disclose potential error handling or return format details.

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

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

The description is four concise sentences, front-loaded with the main purpose, and each sentence provides unique value (purpose, context, scoping, pairing). No redundant 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 simplicity (single optional parameter, no output schema), the description covers purpose, usage, and relationship to siblings. It could mention the return format, but this is a minor omission.

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

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

Schema coverage is 100% and the schema already explains the parameter well (describing both modes). The description reiterates this information without adding significant new semantics.

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

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

The description clearly states the tool retrieves saved values or lists all keys using a specific verb ('Retrieve' and 'list all saved keys'). It distinguishes itself from siblings ('remember' and 'forget') by explaining its complementary role.

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

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

The description provides clear usage context: use to recall previously stored information without re-derivation. It explains both retrieval modes (specific key or list all) and mentions scoping, but does not explicitly list situations 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?

The description discloses that setting mark_read:true flags events as read, altering state. This contradicts the readOnlyHint:true annotation, which implies no data modification. Also, idempotentHint:true is contradicted because mark_read=true makes subsequent calls return different results. Thus, description contradicts 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, front-loads purpose, and includes necessary details (return fields, filtering, mark_read behavior, alternative access). No redundant 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?

No output schema, so description must explain return. It mentions fields source, citation_uri, and raw payload. It covers filtering and mark_read effect. However, lacks details on ordering, pagination, or handling of limit beyond default. Still fairly complete for a simple list tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds valuable context: explains the effect of mark_read (side effect), gives an example type ('sec_8k'), and specifies ISO timestamp format for since. This extra meaning beyond schema justifies a 4.

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

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

The description clearly states the tool's purpose: 'Pull fired events from your subscription feed.' It uses a specific verb and resource, and the title 'Recent Alerts' aligns with this. The tool is distinct from siblings like list_subscriptions (which lists subscriptions themselves) and other search tools, though not explicitly differentiating.

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

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

The description explains when to use the tool (to get recent alerts) and how to filter by type and since. It mentions polling suitability and an alternative HTTP endpoint. However, it does not explicitly state when not to use it or name specific sibling alternatives.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds rich behavioral context: fallback from GDELT to GNews when rate-limited, USPTO soft-failure, parallel fan-out, return structure with grouped changes 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 that packs a lot of information. Every sentence adds value, but it could benefit from bullet points or better structure to improve scanability. Still, it's reasonably concise for the amount of detail.

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

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

Even without an output schema, the description explains the return structure (changes[] grouped by source, total_changes, citation URIs). It also covers limitations and fallback behavior, making it complete for an agent to understand the tool's capabilities and behavior.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining that 'since' accepts ISO dates or relative shorthand (with examples), and 'value' can be ticker or CIK. This provides additional context 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 provides a change feed for a company, combining SEC EDGAR, GDELT/GNews, and USPTO sources in one parallel call. It distinguishes from sibling tool 'entity_profile' by specifying the static profile use case.

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

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

The description explicitly tells when to use this tool vs entity_profile. It provides example queries and patterns. No explicit when-not, but the alternative is clearly stated, making it easy for an agent to choose appropriately.

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

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

Annotations already provide readOnlyHint false, idempotentHint true, destructiveHint false. Description adds persistence details: authenticated users get persistent memory, anonymous sessions 24 hours. No contradictions; adds 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?

Four sentences, no fluff, front-loaded with purpose, well-organized. 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?

For a simple 2-param tool with no output schema, the description covers purpose, use cases, storage behavior, and sibling integration completely.

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 descriptive parameter descriptions. The description reiterates examples (e.g., memory key patterns) but adds minimal new meaning beyond the schema. Baseline 3 justified.

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 saves data for reuse later, specifying key-value pair scoped by identifier. It gives concrete examples like resolved ticker or user preference, and distinguishes it from sibling tools 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?

Explicitly says when to use: when discovering something worth carrying forward to avoid re-lookup. Pairs with recall and forget. No explicit when-not, but context is clear.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable context: internal cascading through multiple endpoints, replacing 2-3 manual lookups. 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 concise and front-loaded with example queries. Every sentence adds value, from purpose to supported types to internal behavior. No wasted words.

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

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

Given no output schema, the description fully explains return values for both entity types. It covers supported input formats and the internal cascade process. Complete for an information-lookup tool.

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

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

Schema coverage is 100% with both parameters described. The description adds significant meaning beyond schema: it details the return values (ticker, CIK, company_name, citation URI for company; RxCUI, ingredient, brand for drug) and clarifies input formats for 'value' (ticker, CIK, name for company).

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 resolves names to canonical identifiers, with specific examples and supported types (company, drug). It differentiates from sibling tools by explicitly saying 'Use FIRST' when needing an ID, distinguishing it from entity_profile or compare_entities.

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

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

Provides explicit guidance: 'Use FIRST whenever you have a name but need an ID.' It lists supported types and explains that it replaces multiple manual lookups. Does not explicitly mention when not to use, but the directive is clear enough for an agent.

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

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

Annotations already declare readOnlyHint, idempotentHint, and no destructive action. The description adds that the tool probes each entity with 'ai_visibility_check', ranks results, and treats the first entity as the subject for narrative. It does not contradict annotations.

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

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

Three sentences with no waste: first states purpose and method, second gives use case and example, third describes return structure. Front-loaded with core action. 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?

The tool is complex (multiple probes, ranking), but the description covers key aspects: probe method, ranking by score, returned fields (score, confidence, signal density), and subject vs competitor roles. No output schema exists, but the return structure is adequately described.

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 meaning beyond schema: explains that the first entity is the 'subject' and rest are competitors, that 'models' defaults to workers-ai without _apiKey, and that 'context' disambiguates common names. This aids correct parameter usage.

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 'Compare AI visibility across multiple entities side-by-side', with explicit verb ('compare') and resource ('AI visibility across entities'). It distinguishes from sibling 'ai_visibility_check' (single entity) and 'compare_entities' (generic comparison) by specifying the competitive audit use case.

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

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

The description provides explicit usage context: 'Useful for competitive AI-marketing audits' and an example question. It implies when to use this over alternatives like 'ai_visibility_check' (single entity), though it doesn't explicitly state when not to use. This is clear enough for an AI agent.

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

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

The description adds significant behavioral context beyond annotations: fans out across deps.dev and bundlephobia, partial failures degrade gracefully, first measurement can take 5-30s, and 'sources_failed' lists timeouts. This complements annotations like readOnlyHint and idempotentHint 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 fairly long and packed with information, but it is front-loaded with the core purpose. Slightly verbose but still effective; a minor deduction for density.

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

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

Given no output schema, the description fully details the return format: summary block with fields, per-advisory detail, links, and recent alternative versions. Also covers graceful degradation and failure behavior. Very complete for a complex tool.

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

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

Schema coverage is 100%, yet the description adds extra meaning: scoped packages like '@types/node' are accepted for 'package', and version defaults to latest when omitted. These details help the agent use parameters correctly.

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 is a composite check for evaluating npm packages, covering license, advisories, bundle size, etc. The verb 'scan' and resource 'dependency' are specific. It distinguishes itself from sibling tools like 'deep_research' by focusing on npm package analysis.

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 tells when to use: when an agent asks about safety, popularity, or size of an npm package. Also provides exclusion criteria: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.' This gives clear guidance on 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 provide safe-read info. Description adds specifics: embeddings model (BGE-base-en), similarity metric (cosine), window size (500-char overlapping), character cap (200K chars with truncation flag), and output details (character offsets, similarity scores).

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

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

Three concise sentences, each earning its place: purpose, usage, and technical details. No redundancy. Front-loaded with core action.

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

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

Covers all aspects needed for correct invocation: purpose, when to use, algorithm details, limits, pairing with sibling, and output description. No gaps given 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 covers all parameters with descriptions (100% coverage). Description adds extra context: max chars for 'text' and example queries for 'query'. Also explains output format, though no output schema exists.

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 'semantic search INSIDE a fetched record' with specific verb and resource. Explicitly distinguishes from sibling 'ask_pipeworx_grounded', 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?

Explicit when-to-use guidance: 'Use when the record is too big to cram into the prompt'. Also pairs with 'ask_pipeworx_grounded', providing clear 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?

The description adds significant behavioral context beyond annotations, such as returning a new subscription id, detailing delivery channel capabilities (feed always on, optional email/sms/webhook), and including constraints like SMS cap (10/day) and webhook auto-disable. No contradiction with annotations.

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

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

The description is well-structured with clear sections for purpose, prerequisites, types, and delivery channels. It is informative but could be slightly more concise; however, every sentence adds essential 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 complexity (three parameters, nested objects, no output schema), the description covers all necessary aspects: subscription types, parameter examples, delivery constraints, prerequisites, and return value (subscription id). No critical gaps.

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

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

Despite 100% schema coverage, the description greatly enriches parameter understanding with concrete examples for each subscription type (e.g., 'params:{topic:"fed"}, min_spread_bps?:500') and detailed delivery options including verification requirements and rate limits, far exceeding the schema summaries.

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 'Create a proactive monitoring subscription to a live-data event stream' with a specific verb, resource, and scope. It distinguishes itself by listing multiple subscription types and mentioning the return of a subscription id, making it distinct from sibling tools.

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

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

The description provides clear context and prerequisites, including 'Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions)'. It does not explicitly state when not to use or name alternatives, but the examples and constraints sufficiently guide usage.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds meaningful behavioral context: returns category-bucketed examples, drawn from live catalog, and the no-args vs topic-filter behavior. 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 front-loaded with common user questions and includes all necessary information in a single paragraph. While slightly dense, every sentence adds value and it's not excessively 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?

For an onboarding suggestion tool with one optional parameter and no output schema, the description fully explains the output format (category-bucketed examples with tool+argument shapes) and usage scenarios. It is complete given the 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 enriches the optional `topic` parameter by listing specific focus areas (finance, pharma, etc.) and explaining that omission gives a cross-category spread. It also clarifies the no-args 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 as 'the onboarding entry point for an agent that just connected' and explains it returns category-bucketed example questions. It distinguishes itself from siblings by being the first tool to use when the agent doesn't know what Pipeworx can do.

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

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

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you' and mentions optional topic focusing. Could be improved by explicitly stating when not to use it (e.g., for direct queries), but the context is clear.

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

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

Adds key behavioral details beyond annotations: ownership enforcement and soft-delete behavior, aligning with non-destructive hint.

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 efficient sentences with front-loaded purpose, 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?

Fully describes behavior, constraints, and side effects for a simple one-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 covers the single parameter fully; description merely echoes 'by id' without adding meaning beyond the schema.

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

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

The description clearly states the verb 'cancel' and resource 'subscription by id', distinguishing it 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?

Explicitly states ownership enforcement and that the row is deactivated not deleted, guiding when to use and linking to 'recent_alerts' for historical data.

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

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

Annotations already declare readOnly, idempotent, openWorld, non-destructive. The description adds value by detailing the dual pipeline (structured vs. grounded), verdict types, and citation format, going beyond what annotations provide.

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

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

The description is informative and efficiently front-loaded with example queries. It covers purpose, pipelines, output, and benefits 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?

Despite no output schema, the description explains return values (verdict, actual value, citation) and covers both financial and non-financial claim handling, making it complete for an agent to understand the tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds marginal context about tolerance_pct overriding implied wording, but most parameter meaning is already in the schema.

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

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

The description clearly states the tool is for fact-checking natural language claims with example queries like 'Is it true that...' and specifies two pipelines for financial vs. other claims, distinguishing it from sibling general-purpose tools.

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

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

Explicitly says 'Use whenever the agent needs to check factual correctness' and mentions it replaces multiple sequential calls, but does not explicitly exclude cases where other tools like ask_pipeworx_grounded might be preferred.

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