Huggingface

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

Annotations already indicate readOnly, openWorld, and idempotent. The description adds behavioral context: default model is free, Anthropic probing requires a BYO key passed directly, and the return format includes per-model fields. It also implies no destructive side effects.

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

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

The description is two sentences, front-loaded with the core action and outcome. Every sentence adds essential information. No redundancy or 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?

Despite no output schema, the description specifies the return structure (per-model score, confidence, signals, raw_response + combined view). It covers common parameters and use cases. Minor omission: no error handling or edge cases, but overall complete for its complexity.

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

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

Schema coverage is 100% with descriptions. The description adds value beyond schema by explaining the default model, the BYO key mechanism, and the purpose of the 'context' parameter for disambiguation. This helps an agent understand parameter usage better.

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 a specific verb ('probe') and clearly states the resource ('LLMs') and outcome ('score visibility 0-100 per model'). It differentiates from siblings like 'scan_competitor_ai_presence' by focusing on visibility scoring.

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

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

The description explicitly mentions use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring'), but does not explicitly exclude alternatives or state when not to use. The context is clear enough for an agent to infer appropriateness.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable context: returns structured answers with pipeworx:// citations and routes to many tools internally. No contradictions. The bar is lowered by annotations, but the description still enhances understanding of behavior.

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

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

Description is well-structured with front-loaded primary instruction, subsequent elaboration, and examples. At 6 sentences, it is concise for the complexity covered, though could trim minor redundancy. Every sentence adds value.

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

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

Despite no output schema, the description explains return format (structured answer with stable URIs). Covers scope, routing, and usage examples. For a complex knowledge tool, it provides sufficient context for agent decision-making.

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

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

Schema provides 100% coverage of parameter descriptions (all aliases for question). The description adds rich guidance on parameter content via examples and usage scenarios, going beyond schema basics. This helps the agent formulate effective queries.

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 answers factual questions using authoritative data, lists specific domains (SEC, FDA, FRED, etc.), and distinguishes from siblings by emphasizing structured data and citations. It includes examples that solidify the purpose.

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

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

Explicitly states 'PREFER OVER WEB SEARCH' and provides clear when-to-use guidance with trigger phrases ('what is', 'look up', etc.) and examples. Effectively differentiates from web search and clarifies its role as the primary factual question 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 annotations (readOnly, idempotent), description adds key traits: costs one extra LLM call, returns refusal reasons like not_in_source, tool_error, etc., fully aligning with read-only and idempotent behavior.

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

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

Description is efficient, front-loading purpose and usage, though slightly dense with multiple sentences. No redundant phrases, but could be slightly more terse.

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

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

Despite no output schema, description fully explains return format (answer, evidence, refusal_reason, etc.) and covers all necessary context for an agent to use the tool correctly in high-stakes scenarios.

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

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

Schema coverage is 100% with clear descriptions of aliases; description adds no new parameter meaning beyond what schema already provides. Baseline score appropriate.

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

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

Description clearly states it is a hallucination-resistant answer mode for high-stakes reads, distinctly different from sibling ask_pipeworx by emphasizing extraction only from tool result and explicit refusals.

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 (answers to be quoted/cited/acted on, must not invent facts) and when to prefer ask_pipeworx for casual lookups, providing clear 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 goes far beyond annotations, detailing fan-out logic, response shapes, resolver contract (match confidence, alternatives, suggestions), parent event extraction, news fallback handling, safety short-circuits (low confidence, closed markets), wide-spread market handling, and resolution-rule risk. This provides comprehensive behavioral transparency.

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

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

The description is very long and detailed, but it is well-structured with sections (RESPONSE SHAPES, RESOLVER CONTRACT, etc.) and front-loaded with the purpose. However, it could be more concise; some details are redundant or could be summarized. It earns its length due to complexity, but still feels verbose.

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

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

Given the tool's complexity (multi-source fan-out, classification, multiple edge cases), the description is remarkably complete. It covers all states and responses: market matching, analysis, evidence, parent events, news errors, blocking conditions, and resolution rules. No output schema exists, but the description fully explains return values.

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

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

Schema descriptions cover all three parameters, but the description adds significant value by explaining the market_input flexibility (slug, URL, question text), the depth parameter's effect (quick vs thorough), and the include_raw parameter's purpose and implications. This goes beyond the basic schema, justifying a score above the baseline of 3.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question text) and explains what it returns (evidence packet + comparison). It distinguishes from sibling tools like polymarket_edges and polymarket_arbitrage by focusing on data aggregation and classification.

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

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

The description explicitly says 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It provides many examples of when to use. However, it does not explicitly state when not to use or mention alternative tools, though the sibling list provides context. It is clear enough for an agent to decide.

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

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

Annotations indicate read-only, open-world, idempotent, non-destructive. Description adds significant context: sources (SEC EDGAR/XBRL, FAERS), fiscal year handling, sorted results, output format (paired data + citation URIs). Goes well beyond annotations.

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

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

Single paragraph with front-loaded trigger phrases. Every sentence contributes value. Slightly long but efficient for the complexity. Could use bullet points for readability but still concise.

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

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

Comprehensive for a tool with two entity types, multiple data sources, no output schema. Covers what data is pulled, sorting, and return format (paired data + citations). No gaps given complexity.

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

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

Schema coverage is 100% but description adds meaning: explains 'type' enum with data sources, 'values' with examples and constraints. Adds context beyond schema (e.g., 'latest 10-K' vs static schema).

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

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

The description clearly states it performs side-by-side comparisons of 2–5 entities in one call. It uses specific trigger phrases ('compare X and Y', 'X vs Y') and explicitly contrasts with sequential single-entity lookups, distinguishing it from sibling tools like 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?

Provides explicit guidance on when to use (comparison queries, ALWAYS PREFER over sequential lookups) and explains behavior per entity type. Lacks explicit when-not-to-use scenarios (e.g., more than 5 entities) 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?

Beyond annotations (readOnly, openWorld, idempotent, non-destructive), description discloses decomposition, parallel routing, grounded answers with evidence, gaps for unanswered facets, and 15-60s latency.

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 front-loaded value proposition; all sentences contribute meaningful information. Slightly long but 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?

Covers purpose, usage, prerequisites, behavior, and output (structure findings packet with gaps). No output schema, but description provides sufficient context for an agent to invoke correctly.

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

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

Schema covers 100% of parameters; description adds meaning: explains depth options in terms of facet count and paid plan requirement, and emphasizes question can be broad/multi-part.

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

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

Description clearly states it performs 'grounded multi-source research in ONE call', decomposes questions, and returns structured findings. Distinguishes from sibling ask_pipeworx for single lookups.

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

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

Explicitly says 'Use for broad or multi-part questions' with examples, and contrasts with ask_pipeworx for single lookups. Also mentions account and plan requirements.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds valuable behavioral details: returns top-N most relevant tools with names, descriptions, full input schemas with curated examples, and each result is ready to call directly. 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 the core purpose and includes essential details. It is slightly verbose but every sentence contributes value (examples, return format, when to use). Could be tightened, 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?

With 6 parameters, 100% schema coverage, no output schema, the description explains the return format (names, descriptions, schemas, examples) and provides usage context. It is fully sufficient for an agent to understand and invoke 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 description coverage is 100%, providing baseline 3. The description adds meaning by explaining the 'query' parameter with examples ('analyze housing market trends') and noting aliases, which adds value beyond the schema.

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

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

The description uses a specific verb ('Find') and resource ('tools'), and explicitly lists numerous domains (SEC filings, FDA drugs, etc.), clearly differentiating from sibling tools like search_datasets or search_models. It states the purpose unambiguously.

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

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

The description explicitly advises 'Call this FIRST when you have many tools available and want to see the option set.' It provides clear context for when to use it, though it does not explicitly state when not to use it or mention 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?

Detailed disclosure of what the tool fans out across (SEC, XBRL, USPTO, news, GLEIF) and specifics of returned data (e.g., up to 5 filings with URIs, fundamentals sorted by period_end DESC). Mentions the USPTO sunset in May 2025 and fallback behavior for news. Annotations (readOnlyHint, idempotentHint) are consistent with the description.

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

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

The description is somewhat long but efficiently packed with essential information, front-loaded with example queries and a clear summary. Every sentence adds value without redundancy.

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

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

Despite no output schema, the description comprehensively explains return values (cik, company_name, filings with URIs, fundamentals, patents with sunset note, news fallback, LEI). It covers parameters fully and notes limitations, making it complete for an agent to understand and use the tool correctly.

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

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

Schema coverage is 100%, with descriptions for both parameters. The description adds extra context: type is only 'company' (person/place coming soon) and value must be ticker or zero-padded CIK (names not supported). This enhances 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 full cross-source profile of a US public company in one parallel call, with many example queries that illustrate the purpose. It distinguishes itself from sibling tools like resolve_entity by specifying when to use this tool instead.

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

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

Explicitly recommends using this tool over chaining single-source lookups for holistic views. Also provides clear instructions on input: ticker or zero-padded CIK, not names, and directs users to resolve_entity if they only have a name.

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 destructive and idempotent behavior. The description adds context about clearing sensitive data and implies mutation, which complements the annotations without contradiction.

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

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

The description is extremely concise: two sentences that cover purpose and usage without any 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?

For a simple delete operation with good annotations and schema coverage, the description provides all necessary context for the agent to use the tool correctly.

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

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

Schema coverage for the single parameter is 100%, and the description's mention of 'key' does not add significant detail beyond the schema. Baseline score 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 action ('Delete') and the resource ('a previously stored memory by key'), and explicitly distinguishes from siblings 'remember' and 'recall', making it 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 on when to use this tool ('when context is stale, the task is done, or you want to clear sensitive data') and pairs it with related tools, aiding decision-making.

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

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

Annotations already indicate safe read-only, idempotent behavior. The description adds process details: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' This provides meaningful transparency beyond annotations without contradiction.

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

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

The description is highly concise with two sentences and a bulleted use-case list. Every sentence adds value, and the structure is front-loaded with the core action and output details.

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 (2 parameters, no output schema), the description adequately covers the process and output format. It lacks details on edge cases (e.g., invalid URLs) but is otherwise complete for standard use.

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

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

Schema coverage is 100% with clear descriptions for both parameters. The description adds minimal new information beyond repeating what the schema already provides (e.g., URL and max links). The baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Generate a production-ready llms.txt file for any URL' and specifies the target AI crawlers (ChatGPT, Claude, Perplexity) and output format. It effectively distinguishes itself from the diverse sibling tools by focusing on a unique, well-defined task.

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

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

The description provides explicit use cases: 'getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.' It does not mention when not to use the tool or alternatives among siblings, but the context is clear enough for most agents.

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, covering safety and idempotency. The description adds no behavioral context beyond 'info,' but it does not contradict the annotations. Given the strong annotation coverage, a score of 3 is appropriate.

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?

Extremely concise but at the cost of informativeness. A one-sentence description that does not earn its place because it adds minimal value beyond the title.

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 having an output schema, the description omits key contextual details such as the scope of 'info' (e.g., metadata, config, statistics) or how revision affects the result. The tool's complexity is moderate, and the description is insufficient.

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 0%, so the description must compensate. It provides no explanation of the two parameters (repo_id and revision). The user cannot infer what values to provide or their purpose.

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 'Detailed dataset info.' is vague; it tells what the tool returns but not what it does (e.g., 'retrieve metadata'). It fails to distinguish from sibling tools like list_dataset_files or search_datasets, which also provide dataset information.

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?

No guidance on when to use this tool versus alternatives. For example, when should a user choose this over list_dataset_files or search_datasets? The description does not mention any preconditions or context.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds that it returns config, tags, downloads, and files at root, providing useful behavioral context beyond annotations.

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

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

The description is a single concise sentence that front-loads the key purpose. No unnecessary words or repetition.

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

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

Given the simple tool with rich schema, annotations, and output schema, the description succinctly covers what the tool returns. Could mention that revision is optional, but schema already handles that.

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

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

Schema coverage is 100% with clear descriptions and examples for repo_id and revision. The description adds no additional parameter meaning beyond stating what info is returned, so baseline score 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 retrieves detailed model info including config, tags, downloads, and root-level files. It is specific with verb+resource and distinguishes from siblings like list_model_files and search_models.

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?

No explicit guidance on when to use vs. alternatives. The description implies use when detailed info is needed, but does not mention when not to use or suggest siblings for specific cases like file listing.

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 no behavioral context beyond these (e.g., what fields are returned, pagination, permissions).

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?

Extremely short but under-specified. 'Detailed Space info' is not informative; conciseness sacrifices clarity.

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

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

Given the tool has an output schema, return values are covered, but the description lacks context about what constitutes a space, how to identify it, and the role of revision. Insufficient for a new user.

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 0%; the description does not explain the meaning or usage of repo_id or revision. No compensation for missing parameter docs.

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 'Detailed Space info' is vague; it does not specify what a 'Space' is or how it differs from sibling tools like get_dataset or get_model. No verb or clear resource scope.

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

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

No guidance on when to use this tool versus alternatives (e.g., search_spaces, get_dataset). No exclusion criteria or context provided.

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

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

Annotations provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering safety. The description adds no behavioral context beyond annotations, so baseline score applied.

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

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

The description is a single concise sentence with no unnecessary words.

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

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

Even with an output schema, the description lacks details on pagination, sorting, or file types. It is too minimal for a 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 description coverage is 0% and the description does not explain any parameter (repo_id, path, revision). At low coverage, description should compensate, but it does not.

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

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

The description clearly states the action (List) and the resource (files in a dataset repo). It distinguishes from sibling tools like list_model_files by specifying dataset repo.

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?

No guidance on when to use this tool vs alternatives. No mention of prerequisites, context, or appropriate use cases.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds no behavioral context beyond stating the action, so it is adequate but not additive.

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

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

The description is concise with no extra words, but it omits critical context about the optional path parameter, making it less useful than it could be.

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 existence of an output schema and annotations, the description is incomplete: it fails to mention that the 'path' parameter is optional and defaults to root, and it does not differentiate from the sibling 'list_dataset_files'.

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 description does not clarify parameter meaning beyond the schema. Notably, it says 'at the root' which contradicts the existence of a 'path' parameter for subdirectories, potentially misleading the agent.

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

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

The description clearly states the action (list files) and the resource (a model repo), distinguishing it from the sibling 'list_dataset_files' which is for datasets. However, it inaccurately says 'at the root' while the schema includes a 'path' parameter for subdirectories.

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?

No guidance on when to use this tool versus alternatives like 'list_dataset_files' or other listing tools. No mention of prerequisites or exclusions.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds context: returns specific fields, scoped to caller, defaults to active. No contradictions. Adds moderate value beyond annotations.

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

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

Three sentences, no fluff. First sentence states purpose and return fields. Second provides usage guidance. Efficient and front-loaded.

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

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

Simple tool with one optional parameter. Description lists return fields even without output schema. Usage guidance and annotations complete the picture. Sufficient for an agent to understand and use correctly.

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

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

Schema coverage is 100% (one parameter fully described in schema). Description does not add extra detail about the parameter, but baseline of 3 is appropriate since schema already handles it.

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

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

Clearly states verb 'List', resource 'subscriptions', and scope 'caller's active subscriptions'. Lists returned fields. Distinguishes from siblings like subscribe and unsubscribe by focusing on review and retrieval.

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

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

Provides explicit use cases: 'review what you're monitoring before adding more' and 'find an id to cancel'. Implicitly tells when to use it before write operations, but does not explicitly state when not to use it.

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

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

Annotations all false, so description carries burden. It discloses rate limiting (5 per identifier per day), that it's free, and that the team reads digests daily affecting roadmap. No contradictions. Does not detail post-submission behavior but sufficient for a feedback tool.

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

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

The description is concise, front-loaded with purpose, and every sentence provides essential information without fluff. Perfectly structured for an agent to quickly grasp usage.

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

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

While the description covers purpose, usage, and limitations, it lacks explanation of the return value or success confirmation. Given no output schema, this is a minor gap but partially acceptable for a feedback 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 detailed descriptions. The description adds value by explaining how to frame feedback (describe in terms of tools/packs, avoid pasting prompts), supplementing the schema's parameter details.

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

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

The description clearly states the tool collects feedback for the Pipeworx team, listing specific types (bug, feature, data_gap, praise) and instructing on what to describe. It effectively differentiates from sibling tools by focusing on team feedback rather than queries or searches.

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

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

The description explicitly states when to use the tool for bugs, missing features, data gaps, or praise. It also mentions rate limits and that it's free. However, it does not explicitly state when not to use it or provide alternatives, but the context is clear.

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

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

Annotations already declare readOnly, idempotent, and non-destructive. The description adds behavioral details: data is derived from CF analytics-engine, no PII, cached 5min-1h. 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, front-loaded with purpose, and uses bulleted use cases. Every sentence adds value; no filler or redundancy.

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

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

For a read-only tool with one optional parameter and no output schema, the description adequately explains what is returned (top tools, packs, call volume). It covers data source, privacy, and caching. Minor omission: exact structure of return value not specified, but not necessary for this tool.

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

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

Schema coverage is 100% and the schema already provides a description for the 'window' parameter. The description repeats the enum values and adds a brief explanation of short vs long windows, but adds no new semantic information. 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, packs, and call volume over specified time windows, and lists three explicit use cases. This distinguishes it from sibling tools like 'trending_datasets' and 'trending_models' by being specific to Pipeworx agent activity.

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 use cases are provided (discovering hot data, confirming canonical tools, alignment checking). The description mentions caching and no PII, but does not explicitly exclude alternatives or state when not to use. Still, the context is clear enough.

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, etc. The description goes far beyond by detailing orchestration steps (monotonicity checks, partition summation), filtering logic (Jaccard similarity, placeholder filters), and the fill check with live book depth. No contradictions with annotations.

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

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

The description is comprehensive but somewhat lengthy. It uses capitalized headings for structure and front-loads the key requirement (event or topic). Every sentence adds value, but the density could be reduced by splitting into more digestible sections. Still, it's 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?

Despite no output schema, the description explicitly outlines the response structure (opportunities array with fields, partition_check object, fill check results). It covers edge cases (missing parameters, low similarity pair count, placeholders) and references a sibling tool for sizing. 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?

Both parameters have 100% schema description coverage. The description adds substantial value: explaining what event and topic are, giving concrete examples (slugs, seed questions), clarifying that full Polymarket URLs work, and specifying exactly when to use each. This is exemplary.

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

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

The description clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It specifies the verb (find), resource (Polymarket arbitrage opportunities), and differentiates between event and topic modes. However, it does not explicitly contrast against sibling tools like polymarket_edges or polymarket_fill_risk, so a slight deduction.

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 requirement for exactly one of event or topic, warns against calling with no args, recommends when to use each mode, and even references when to use polymarket_fill_risk. This provides clear when-to-use and contextual 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, but the description adds substantial behavioral context: caching at KV level keyed on knobs, exclusion of Fed bets from ranking, diagnostic sections for empty segments, and the 24h-move warning. 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 quite long but well-structured with parentheses and bullet-like formatting. It is front-loaded with purpose and every sentence adds value. Could be slightly more concise but remains effective.

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

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

Given no output schema, the description compensates by detailing the response structure (by_segment, fed_candidates, _diagnostics) and caching behavior. The high complexity (9 parameters, three model families) is fully covered.

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

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

All 9 parameters are described in both schema and description. The description adds meaningful context beyond the schema, such as why min_partition_leg_kelly exists, how slippage affects edge calculation, and practical usage tips for min_liquidity and max_spread_pp.

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 and returns edges where Pipeworx data disagrees with price, with a specific use case ('what should I bet on today'). It distinguishes from sibling tools by focusing on Pipeworx-driven edge detection and mentions three distinct segments.

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

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

The description provides a clear context for when to use the tool (initial discovery without paging hundreds of markets) and explains tradeable-edge knobs. However, it does not explicitly state when not to use it or compare it to alternatives like polymarket_arbitrage or polymarket_kalshi_spread.

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 explains behavior beyond annotations: response structure, limits (60-day TTL, daily closes), decay computation, and snapshot gaps. It adds significant value and does not contradict the readOnlyHint, openWorldHint, or idempotentHint 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 and front-loaded with purpose. It is somewhat lengthy due to thorough return field explanations but remains clear and each part 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?

Despite lacking an output schema, the description fully explains the return structure (tracked, expired, snapshot_dates) and limitations. It covers all necessary information for an agent to use the tool 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%, so the baseline is 3. The description adds minimal extra meaning beyond the schema, such as clarifying that days is clamped 2-30, but this is already partly in the schema description. The additional details are not substantial.

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: providing edge persistence and decay telemetry from daily snapshots. It distinguishes itself from related tools like polymarket_edges by focusing on the time dimension and the distinction between fresh and old edges.

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

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

The description implies usage for assessing edge longevity and provides context for when it's valuable. However, it does not explicitly mention when not to use or compare with sibling tools like polymarket_arbitrage, leaving some ambiguity.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds significant behavioral context: it walks the order book ladder, returns verdicts (clean|degraded|cannot_fill), describes forced directional risk, and explains basket fill risks. All information is 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 somewhat long but well-structured into sections (REQUIRES, SINGLE-MARKET, BASKET) and front-loads the core purpose. Each sentence adds value, but some redundancy could be trimmed. Still effective and clear.

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

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

Given the tool's complexity (two modes, multiple output fields, risk warnings), the description covers all essential aspects: it explains both modes, lists all return values (top-of-book, vwap_fill_price, slippage_pp, etc.), and includes edge-case warnings about thin books and directional risk. No output schema exists, so the description carries the full burden and succeeds.

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

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

Schema description coverage is 100%, with each parameter having a description. The description further adds meaning by clarifying the dual interpretation of 'size_usd' (max spend on buys, target proceeds on sells) and explaining default side auto-logic for basket mode. This goes beyond what the schema provides.

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

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

The description clearly identifies the tool as a 'realizable-vs-theoretical edge check against live CLOB order-book depth'. It distinguishes between single-market and basket modes, and explicitly mentions it should be used before acting on polymarket_arbitrage or polymarket_edges, differentiating it 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 states when to use this tool: 'before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It explains why (theoretical overround on thin books is not capturable, partial basket fills convert an arb into an unhedged directional position), providing clear 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?

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds extensive behavioral context: compatibility warnings, temporal alignment, leg-by-leg prices, skipped cross types, and the note that pre-mapped shortcuts may not yield tradeable spreads. No contradiction with annotations.

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

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

The description is lengthy but well-structured with labeled sections (TWO MODES, RESPONSE, SAFETY FIELDS). Every sentence adds unique value for a complex tool. Slightly dense but appropriate for the tool's complexity.

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

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

Despite no output schema, the description thoroughly explains the response shape (leg-by-leg prices, matched spread field, safety fields with conditions) and covers edge cases (compatibility_warning scenarios, temporal alignment). It is complete for an agent to understand inputs and outputs.

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

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

Schema coverage is 100%, but the description enriches all parameters: it lists the 10 topic shortcuts with a realistic caveat, explains the explicit ticker parameters with examples, and adds meaning beyond the schema (e.g., 'pre-mapped ≠ tradeable').

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 identifies the tool as computing the cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes two modes (topic shortcuts vs. explicit tickers) and contrasts with related sibling tools 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 details when to use each mode and warns about conditions that make spreads unreliable (compatibility_warning, temporal alignment). However, it does not directly compare to sibling tools or explicitly state when not to use this tool vs. alternatives, though the warnings serve as usage constraints.

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 and destructiveHint. The description adds behavioral context: listing all keys when omitted, scoping to identifier, and that values come from 'remember'. 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 concise sentences front-loading the core action. No wasted words; every sentence adds value. Efficiently packs purpose, usage, and pairing 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 tool with one optional param and strong annotations, the description covers triggering, scoping, and pairing. Could mention return type but not essential. Overall 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%, baseline 3. The description adds semantic examples of key values (ticker, address, notes) and reiterates the omission behavior, providing context beyond the schema's description.

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

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

The description clearly states it retrieves a saved value or lists keys, uses specific verbs ('Retrieve', 'list'), and distinguishes from siblings 'remember' and 'forget' by pairing them. Examples of use cases (ticker, address, notes) add specificity.

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 (look up context without re-deriving) and pairs with remember and forget. Mentions scoping to identifier and includes example scenarios, providing clear guidance on when and how to use.

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

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

The description states that setting mark_read:true will mark returned events as read, causing subsequent calls to show only newer events. This implies state modification and non-idempotent behavior, contradicting the idempotentHint=true annotation. According to the rubric, a contradiction scores 1.

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 (4 sentences) and front-loaded with the core purpose. Every sentence adds value, covering return fields, filtering, mark_read behavior, and alternative access. No redundant or unnecessary text.

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 no output schema, the description adequately explains return structure (source, citation_uri, raw event payload). It covers all parameters via schema (100% coverage) and adds behavioral context. The mention of polling and an alternative HTTP endpoint provides completeness. The tool is a read-only polling tool, and the description covers necessary details.

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

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

With 100% schema description coverage, the baseline is 3. The tool description adds value by providing an example for 'type' (e.g., 'sec_8k') and explaining the effect of 'mark_read' on subsequent calls. It also hints at the 'limit' parameter implicitly. This extra context justifies a score 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?

The description clearly states the tool's purpose: 'Pull fired events from your subscription feed.' It specifies the resource (alerts from subscription feed) and action (pull), and distinguishes from siblings by focusing on fired events from persisted feed. The mention of fields like source, citation_uri, and raw event payload further clarifies the output.

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 guidance on usage: 'Filter by type and/or since' and 'Set mark_read:true to flag returned events read'. It also mentions polling suitability and an alternative HTTP endpoint (GET registry.pipeworx.io/alerts.json). However, it does not explicitly contrast with sibling tools like 'recent_changes' or 'recall', nor 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?

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. The description adds significant behavioral details: multiple source fan-out, fallback mechanism, USPTO soft-fail, and return format with citations. No contradiction.

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

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

The description is dense but well-structured with example queries upfront. Every sentence adds value, though it could be slightly more concise given the length.

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

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

Despite no output schema, the description explains the return structure (changes grouped by source, total count, URIs), covers fallbacks, date formats, and alternative tool. Fully adequate for a tool of this complexity.

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

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

Schema coverage is 100% with parameter descriptions. The description adds value: explains 'since' accepts ISO or relative shorthand with examples, clarifies 'value' accepts ticker or CIK, and notes 'type' only supports 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 the tool provides a change feed for a company combining SEC filings, news, and patents within a time window. It uses specific verbs like 'fans out' and 'returns structured changes' and distinguishes from sibling tool entity_profile which provides static profile.

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

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

Explicitly gives example queries, specifies when to use entity_profile instead, and describes fallback behavior (GDELT→GNews). Provides clear context for when to use this tool vs alternatives.

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

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

Annotations indicate idempotentHint=true and destructiveHint=false. Description adds persistence details: authenticated users get persistent memory, anonymous sessions retain for 24 hours. 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?

Concise but thorough; front-loaded with purpose and usage. Every sentence adds value. Could slightly tighten but still 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?

No output schema, but description mentions pairing with recall for retrieval. For a simple key-value store, this is complete enough. Context signals indicate low 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 covers 100% of parameters with descriptions. Description adds example key patterns and clarifies value is any text, adding value beyond schema.

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

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

Description starts with 'Save data the agent will need to reuse later' and provides specific examples (ticker, address, preference). Clearly distinguishes 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 'Use when you discover something worth carrying forward' and pairs with recall and forget. Provides context for when to store 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 indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable behavioral context by revealing that each call cascades through several internal endpoints, effectively replacing 2-3 manual lookups.

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

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

The description is concise yet comprehensive, starting with relatable query patterns, then stating the core purpose, and finally detailing supported types and outputs. Every sentence adds value without redundancy.

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

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

Given the complexity of resolving multiple entity types with cascading lookups and no output schema, the description fully explains what is returned for each type (ticker, CIK, RxCUI, etc.) and mentions citation URIs, making it complete for a 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%, but the description enriches parameter understanding by providing concrete examples (e.g., 'AAPL', '0000320193', 'ozempic') and explaining different input formats for each entity type, including auto-disambiguation for company names.

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

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

The description clearly states the tool resolves user-spoken names to canonical/official identifiers needed by other tools. It provides specific query examples and distinguishes its role as the first step when a name is known but an ID is required.

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 to 'Use FIRST whenever you have a name but need an ID,' providing clear usage context. It also mentions that the tool replaces multiple manual lookups, implying efficiency and purpose.

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, covering safety. The description adds context that the tool probes each entity with 'ai_visibility_check', which is useful behavioral detail without 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 sentences are front-loaded with the core purpose and immediately provide usage context. Every word earns its place, 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?

The description covers the main functionality, use case, and output summary ('ranked list with score, confidence, signal density per entity'). It could be more explicit about error handling or edge cases, but it is complete for this complexity level.

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

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

Schema descriptions cover 100% of parameters (baseline 3). The description adds semantic value by noting that the first entity is treated as the 'subject' for narrative and that omitting 'models' defaults to 'workers-ai'.

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 'Compare' and resource 'AI visibility across multiple entities'. It explicitly distinguishes from sibling 'ai_visibility_check' by emphasizing side-by-side comparison and ranking.

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

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

The description provides clear context for competitive AI-marketing audits and an example question. It implies using 'ai_visibility_check' for single entities but does not explicitly mention when-not-to-use or other alternatives like 'compare_entities'.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. Description adds key behavioral details: fans out across deps.dev and bundlephobia, partial failures degrade gracefully, bundlephobia first measurement may take 5-30 seconds and will be listed in sources_failed if it times out. 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 relatively long but efficiently structured: front-loaded with purpose, then output summary, usage guidance, ecosystem scoping, and failure behavior. Every sentence adds value, though some could be tightened.

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

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

No output schema exists, so description must detail return values. It lists the summary block fields (is_latest, license, etc.), per-advisory detail, links, and alternative versions. Also covers partial failure behavior. This is sufficiently complete for an agent to understand what the tool returns.

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 schema descriptions already cover package (scoped packages accepted) and version (default to latest). Description adds minimal extra meaning beyond confirming the composite check uses these parameters. 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: a composite check for evaluating whether to add an npm package, covering license, advisories, version history, and bundle size. It distinguishes itself from sibling tools as the only one performing this specific function.

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

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

Explicitly states when to use: for questions about safety, popularity, size, or cost of adding an npm package. Also specifies ecosystem limitations (NPM only in v1) and directs to alternative for other ecosystems (deps.dev:version directly).

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. Description adds no extra behavioral context such as pagination, result limits, or rate limits.

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

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

Single sentence is concise but overly minimal; lacks structure and substantive content.

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

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

Given 8 parameters, no required fields, low schema coverage, and no output schema details, the description is woefully incomplete 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?

With only 13% schema coverage, description must explain parameters but provides none. No meaning added beyond the schema for the 8 parameters.

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 'Browse / search datasets on the Hub' clearly states the action (browse/search) and resource (datasets), distinguishing it from sibling tools like search_models and search_spaces.

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?

No guidance on when to use this tool vs alternatives like search_models or search_spaces. No exclusions or context provided.

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, so the description does not need to reiterate safety. The description adds 'Browse / search' but offers no additional behavioral detail (e.g., pagination, rate limits, or result structure). It is adequate but not rich.

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 short sentence with no superfluous words. It is front-loaded with the key action and resource.

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 10 parameters and a simple search use case, the description is minimal. However, the schema fully documents parameters and an output schema exists, so the core functionality is covered. Still, a mention of the return format or advanced features would improve completeness.

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

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

Schema description coverage is 100%, with all 10 parameters described in the schema. The description does not add any parameter-level context beyond what is already in the schema, so baseline 3 applies.

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

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

The description uses specific verb-noun pair 'Browse / search models' and clearly identifies the resource type 'models'. It distinguishes from sibling tools like search_datasets and search_spaces by focusing on models.

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 this tool is for searching models but does not provide explicit guidance on when to use it vs alternatives (e.g., get_model for single model or search_datasets for datasets). No when-not or exclusion criteria are mentioned.

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

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

Annotations already declare readOnlyHint=true, but the description adds no additional behavioral context beyond stating the action. It does not discuss pagination, filtering behavior, or any side effects.

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

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

The description is extremely short (one line) but fails to provide necessary information. It is undersized rather than appropriately concise, as it sacrifices substance for brevity.

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 low schema coverage and lack of parameter details, the description is severely incomplete. It does not explain what a Space is, how to filter, or what the output contains, despite having an output schema.

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

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

Schema description coverage is only 14%, meaning most parameters lack descriptions. The tool description does not add any parameter details or usage guidance, leaving the 7 parameters largely undefined.

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 'Browse / search' and the resource 'Spaces (demo apps)', which is specific and distinguishes it from sibling tools like search_datasets or search_models.

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?

No guidance is provided on when to use this tool vs alternatives or when not to use it. The description lacks any context about usage scenarios.

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

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

Discloses embedding model (BGE-base-en), chunking strategy (500-char overlapping windows), character cap (200K chars with truncation flag), and output details (character offsets and similarity scores) far beyond what annotations provide. 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?

Every sentence serves a purpose: purpose first, then usage guidance, then technical details. No wasted words. Front-loaded with core functionality.

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

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

Despite no output schema, description fully explains return values (top-N passages with offsets and scores) and covers edge cases (truncation). Comprehensive for a semantic search 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 already provides 100% parameter descriptions; the description adds practical constraints (max 200K chars for text, 1-20 range for limit with default, example queries for query) that improve usability beyond schema alone.

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

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

Describes a specific verb-resource ('Semantic search INSIDE a fetched record') with concrete examples (SEC 10-K, article) and explicitly distinguishes from siblings by contrasting with whole-document approaches and naming ask_pipeworx_grounded as a complementary tool.

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

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

Explicitly states when to use ('too big to cram into the prompt') and explains benefits (saves context, returns relevant passages). Pairs with a sibling (ask_pipeworx_grounded) but does not explicitly list 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?

Even though annotations exist, the description adds substantial behavioral context: OAuth requirement, per-type parameter examples, delivery channel constraints (SMS 10/day cap, webhook HMAC signing, auto-disable on failure). No contradictions with annotations.

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

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

The description is front-loaded with the purpose and returns value, then covers types and delivery. Each sentence adds value, though slightly verbose; minor improvement could tighten some examples.

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 (multiple subscription types, delivery channels, auth, constraints), the description covers all essential aspects. No output schema, but the return value is stated.

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 rich meaning beyond schema property descriptions, including type-specific parameter structures, examples, and delivery channel details.

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

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

The description clearly states 'Create a proactive monitoring subscription to a live-data event stream,' using a specific verb (create) and resource (subscription to event stream). It distinguishes from sibling tools like 'unsubscribe' and 'recent_alerts'.

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 context for when to use (proactive monitoring) and notes that anonymous/BYO accounts cannot persist subscriptions. It indirectly contrasts with 'recent_alerts' for pulling the feed, but does not explicitly list all 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, idempotentHint, and non-destructive. The description adds valuable behavioral context: the return format (category buckets with exact tool+argument shapes) and the effect of the optional topic parameter. No contradictions.

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

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

The description is a single, well-structured paragraph. It front-loads common user queries and then explains functionality. Every sentence adds value, though it could be more concise without losing clarity.

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

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

Given one optional parameter, no output schema, and the tool's role as an entry point among many siblings, the description covers what the tool does, how to call it, what it returns, and when to use it. It references meta-tools for deeper guidance, making it complete for an AI agent.

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

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

Schema coverage is 100% with a description for the topic parameter. The description goes further by providing example values (finance, pharma, betting) and explaining that omitting it gives a full spread, adding practical semantic meaning.

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

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

The description clearly states the tool is the onboarding entry point, answering 'what can I ask Pipeworx?'. It specifies it returns category-bucketed example questions with tool/argument shapes, distinguishing it from sibling meta-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 advises to 'Use this FIRST when you do not yet know what Pipeworx can do for you' and contrasts with meta-tools like ask_pipeworx, entity_profile, etc., providing clear 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the tool's safety profile is clear. The description adds no additional behavioral context, which is acceptable but does not exceed the baseline.

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

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

The description is extremely concise (one phrase) and front-loaded. While efficient, it could optionally include structured details like 'Lists currently trending datasets on the platform.'

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 tool with one optional parameter, output schema, and comprehensive annotations, the description is minimally adequate. It lacks usage context but does not omit critical information.

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

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

Schema coverage is 100% with a clear parameter description. The tool description does not reiterate or add meaning beyond the schema, matching the baseline of 3 for high 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 'Currently-trending datasets' clearly indicates the tool returns trending datasets, distinguishing it from sibling tools like trending_models. However, it lacks an explicit verb (e.g., 'list' or 'retrieve'), so it's not a full 5.

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?

No guidance is provided on when to use this tool versus alternatives such as search_datasets or get_dataset. The description does not mention exclusions or contextual 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?

Annotations already provide safety profile; description adds minimal behavioral context beyond 'trending'. No elaboration on trend calculation.

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 sentence, no fluff, front-loaded with key 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?

Sufficient for a simple trending list with output schema present; could mention sort order or result type but not required.

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

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

Schema covers 100% parameter description; description adds no extra 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?

Description clearly states the verb 'trending' and resource 'models on the Hub', distinguishing it from sibling like 'trending_datasets'.

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?

Implied usage for discovering currently popular models, but no explicit guidance on when not to use or alternatives like 'search_models'.

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

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

Annotations indicate readOnlyHint=false, destructiveHint=false, idempotentHint=true. The description adds that the row is deactivated (not deleted) and historical events stay accessible, which clarifies the non-destructive nature beyond annotations. There is no contradiction.

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

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

The description is three sentences long, each serving a distinct purpose: action, constraint, and behavioral detail. There is no redundancy or unnecessary 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 tool with one parameter and no output schema, the description covers the essential aspects: purpose, ownership rule, and the deactivation behavior. It is sufficiently complete for an agent to understand and use the tool correctly.

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

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

The only parameter 'id' is well-described in the schema ('Subscription id (uuid) returned by subscribe'). The description does not add further semantic details beyond what the schema provides. With 100% schema coverage, baseline score 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 action ('Cancel a subscription by id') and the specific resource being acted upon (subscription by id). This distinguishes 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?

The description notes ownership enforcement, which guides when this tool can be used. It also explains that the row is deactivated (not deleted) and that historical events remain available via 'recent_alerts', indirectly suggesting its appropriateness for non-destructive cancellation. However, it does not explicitly state when to avoid using this tool (e.g., if complete deletion is needed).

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

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

Annotations provide readOnlyHint, idempotentHint, openWorldHint, and destructiveHint, which the description does not contradict. The description adds significant behavioral context: it specifies data sources (SEC EDGAR + XBRL), the exact return structure (verdict types, structured form, actual value with pipeworx:// citation, percent delta), and the scope limitation (company-financial claims for public US companies). This goes well beyond the annotations.

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

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

The description is a single, well-structured paragraph of about five sentences. It front-loads with natural-language triggers, then moves to scope, return values, and efficiency gains. Every sentence contributes uniquely, with no redundancy or fluff.

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

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

Given the tool's moderate complexity (single parameter, structured output, domain-specific), the description comprehensively covers the return format (verdict types, actual value, citation, delta), scope limitations, data sources, and the efficiency rationale. No output schema exists, so the description carries that burden and does so thoroughly. There are no apparent gaps for an agent to be confused about.

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 for the single 'claim' parameter is 100%, so the baseline is 3. The schema description provides a clear example. The tool description adds extra examples of claim phrasing, but this does not substantially increase semantic clarity beyond what the schema already offers.

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

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

The description opens with explicit trigger phrases (e.g., 'Is it true that…', 'fact check', 'verify the claim') and clearly states the core function: natural-language claim verification against authoritative sources. It specifies the domain (company-financial claims via SEC EDGAR + XBRL) and distinguishes itself by noting it replaces 4-6 sequential calls, giving a clear identity relative to siblings.

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

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

The description directly says 'Use whenever the agent needs to check whether something a user said is factually correct,' providing clear when-to-use guidance. It also notes the current scope (company-financial claims), implying when not to use (non-financial claims). However, it does not explicitly list alternatives or situations where sibling tools like 'ask_pipeworx_grounded' or 'deep_research' would be preferred, which would strengthen the guidance.

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