Huggingface

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

Annotations already indicate read-only and idempotent behavior. The description adds that the default model is free, that Anthropic probes require an API key (user pays), and the return format. No contradictions with annotations.

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

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

The description is a single paragraph of five sentences, front-loaded with the main action. Every sentence adds value; no wasted words.

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

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

Despite no output schema, the description explains the per-model return structure. Parameters, use cases, and behavioral details are covered comprehensively 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 good descriptions. The tool description adds context: default model is free, Anthropic requires key, and explains the return format. This adds value beyond the schema.

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

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

The description uses specific verbs ('probe', 'score') and identifies the resource (LLMs) and output (visibility score per model). It clearly distinguishes the tool from siblings like 'deep_research' by focusing on AI visibility audits.

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 clear use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It implies when to use but does not explicitly exclude alternatives like 'scan_competitor_ai_presence'.

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

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

Annotations already establish readOnly, idempotent, openWorld, and non-destructive behavior. The description adds valuable context: routing mechanics (fills arguments), return format (structured answer with citation URIs), performance ('one fast call'), and tier compatibility. This goes beyond the annotations without contradicting them.

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

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

The description is dense but well-structured, front-loading the key message ('PREFER OVER WEB SEARCH') and then expanding into categories, examples, and escalation paths. While slightly verbose, every sentence provides actionable guidance for a complex routing tool.

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

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

With no output schema, the description adequately explains the return type (structured answer with citations) and scope. It also positions the tool within the sibling set and covers various query types. It lacks edge-case details (e.g., ambiguous questions), but the core behavior is fully covered for a question-answering tool.

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

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

The schema description covers 100% of parameters, with all six documented as aliases for the 'question' field. The description adds several real-world examples of valid parameter values, which helps the agent formulate queries. This is slightly above the baseline because the examples illustrate expected question formats.

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 with a specific verb ('Routes the question') and resource (the 5,562 tools across 1,462 sources). It explicitly distinguishes itself from siblings like ask_pipeworx_grounded and deep_research, and provides concrete examples of questions it handles.

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 gives explicit when-to-use guidance ('PREFER OVER WEB SEARCH', 'START HERE for most questions') and when-not-to-use ('Step up only when needed') with named alternatives (ask_pipeworx_grounded, deep_research). It also covers breaking-news contexts and provides examples of appropriate queries.

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

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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses the beta status, that candidate routing improvements are enabled live when under test, that no candidate is currently active, and that it currently matches ask_pipeworx exactly. This contextualizes the experimental nature and current behavior without contradicting annotations.

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

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

The description is four sentences long and front-loaded with the purpose. It includes useful detail about the beta status, current state, and comparison behavior, though the date and specific tool count are slightly verbose; overall compact and well-structured.

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

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

Despite no output schema, the description covers the essential context: what the tool is, its relationship to the stable router, how to use it, its current behavior, and that it is a fully functional router rather than a stub. This is sufficient for an agent to select and invoke it correctly.

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

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

Schema description coverage is 100% and all six parameters (question and its aliases) are well-documented in the input schema. The description does not add parameter-level meaning, but the schema fully covers semantics, so a baseline score of 3 is appropriate.

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

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

The description clearly identifies the tool as a beta version of ask_pipeworx, an identical universal router with the same 5,562 tools, arguments, and response shape. This distinguishes it from the stable ask_pipeworx and the sibling ask_pipeworx_grounded by emphasizing its experimental routing 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?

Explicit guidance is given: 'Use it exactly like ask_pipeworx when you want the newest routing' and notes that results are compared against the stable router. It does not explicitly list when NOT to use it or compare with ask_pipeworx_grounded, but the intended 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 the annotations (readOnlyHint=true, destructiveHint=false), the description details the refusal mechanism with specific refusal reasons, the exact return structure including evidence and confidence, and the constraint that answers are extracted only from tool results. This fully discloses behavioral traits and failure modes.

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?

Five sentences, each serving a clear role: purpose, mechanism, success return, failure return, usage guidance. No redundant phrasing; every sentence adds critical information, making the description dense yet easy to parse.

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

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

For a tool with no output schema, the description fully covers success and failure return values, enumerates refusal reasons, explains the internal routing, and gives usage context with cost implications. It is sufficiently complete for an agent to decide when and how to invoke it.

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 'question' and five aliases well described. The description does not add parameter-specific detail beyond what the schema provides (e.g., it doesn't elaborate on alias usage), so the baseline 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 a specific verb+resource: 'Hallucination-resistant answer mode for high-stakes reads'. It explicitly distinguishes from sibling 'ask_pipeworx' by highlighting the grounded extraction mechanism, making the tool's unique purpose unmistakable.

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

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

The description provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on' and explicit exclusions: 'prefer ask_pipeworx for casual lookups'. It also mentions the cost tradeoff (one extra LLM call), giving clear decision criteria.

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

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

The description extensively discloses behavioral traits beyond annotations: resolver contract (confidence, matches, alternatives), parent event extraction, news fallback handling, safety short-circuits for low confidence and closed markets, illiquid spread warnings, and cancellation rule parsing. No contradiction with annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint).

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

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

The description is long but well-structured with labeled sections (RESPONSE SHAPES, RESOLVER CONTRACT, etc.). Every sentence provides value, though some finer details (e.g., resolution-rule risk) could be condensed. Slightly verbose for the purpose but not wasteful.

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

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

For a tool with 3 parameters and no output schema, the description is remarkably complete. It covers input formats, processing steps, response structure, edge cases (low confidence, closed markets, wide spreads), error handling (news fallback), and behavioral nuances (cancellation rules). No gaps remain for agent understanding.

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

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

Schema coverage is 100%, so baseline is 3, but the description adds substantial meaning: examples for 'market', explanation of 'depth' enum (quick/thorough with source count), and use cases for 'include_raw' (keeps responses small vs full payloads for recomputation). This far exceeds schema documentation.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data with a single call. It specifies accepted inputs (slug, URL, question text) and the overall process (resolve, classify, fan out). It distinguishes from siblings by focusing on research of a single market, unlike polymarket_edges or polymarket_arbitrage which cover edge tracking or arbitrage.

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

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

The description explicitly states use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z'). However, it does not mention when not to use the tool or suggest alternative sibling tools for different scenarios, missing a layer of guidance.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context: clarifies data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handles off-calendar fiscal years, explains sorting by primary metric, and mentions citation URIs. 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 paragraph that front-loads example queries for quick recognition. It is concise for the amount of information conveyed, but could be slightly more structured (e.g., bullet points for the two types). Every sentence is informative and earns its place.

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

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

Given the tool's moderate complexity (two entity types, up to 5 entities, different data sources), the description is complete. It covers data sources, fiscal year handling, sorting, and citation URIs. No output schema exists, but the expected return format is implied. No gaps for an agent to be misled.

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% (both parameters described in schema). The description adds meaning by specifying that for 'company', values should be tickers/CIKs, and for 'drug', drug names. It also provides examples (e.g., ['AAPL','MSFT'] for companies, ['ozempic','mounjaro'] for drugs) and mentions min/max constraints.

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

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

The description clearly states the tool performs side-by-side comparison of 2-5 companies or drugs. It provides example queries ('compare X and Y', 'X vs Y', etc.) and distinguishes from sibling tools by stating 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.'

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

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

The description explicitly tells when to use the tool (comparing entities) and when not (sequential single-pack lookups), with strong guidance to prefer this tool over alternatives. It also specifies the recommended use case for comparison queries.

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 richly supplements annotations by detailing account/plan requirements, latency (15-60s, ~90s for thorough), never-invented gaps[], fetchable citations, semantic excerpting, hop behavior, and contradictions[]. This goes far beyond the readOnlyHint/openWorldHint annotations and adds essential behavioral context.

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

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

The description is long but every sentence carries meaningful information—auth, comparisons, mechanism, output format, edge cases, and performance. It is front-loaded with the most critical constraint (account required). Some density is justified given the tool's complexity, but it could be broken into shorter sentences for readability.

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

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

Without an output schema, the description fully explains the findings packet, gaps[], contradictions[], hop field, and citation_uri. It also covers enough operational context (latency, plans, alternatives) for an agent to decide when to invoke this tool. Very complete for a complex multi-step research tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by elaborating on depth semantics (paid plan requirement, gap recovery, lead-chasing) and explicitly endorsing broad/multi-part questions for the question parameter. Slight extra beyond schema, but not overwhelming.

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

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

The description clearly states it performs 'Grounded multi-source research' across 1462 structured data sources in one call, decomposes questions into facets, and routes to 5,562 tools in parallel. It explicitly distinguishes itself from siblings like ask_pipeworx by targeted use cases.

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

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

Explicit guidance is provided: 'For a single lookup use ask_pipeworx' and for breaking/colloquial news 'prefer ask_pipeworx'. It also notes account requirements and that ask_pipeworx works on every tier if not signed in. This gives clear when-to-use 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?

Beyond annotations (readOnly, idempotent, non-destructive), the description reveals behavioral details: returns top-N relevant tools with full schemas and examples, ready to call without second lookup. 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 clear and front-loaded with the purpose. It includes a list of example domains and usage guidance, which is valuable but slightly verbose. Every sentence serves a purpose; minor tightening possible.

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 explains what the tool returns (tools with names, descriptions, and schemas). It covers the key use case adequately. Missing details like pagination or error handling, but acceptable for a discovery tool.

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

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

Schema coverage is 100% with good descriptions for each parameter (e.g., query accepts multiple aliases). The description does not add significant meaning beyond the schema, aligning with the baseline score of 3.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It provides concrete examples of domains (SEC filings, FDA drugs, etc.) and distinguishes it from sibling search tools by emphasizing tool discovery rather than data search.

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

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

The description explicitly guides when to use: 'Use when you need to browse, search, look up, or discover what tools exist for...' and advises 'Call this FIRST when you have many tools available and want to see the option set.' This is actionable and 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?

Discloses behavior beyond annotations: fans out across multiple sources (SEC, XBRL, USPTO, news, GLEIF), returns specific fields (cik, filings, fundamentals, patents, news, LEI), notes that patents soft-fails due to USPTO sunset, and mentions URI format for filings. Annotations already indicate readOnly/openWorld/idempotent, which is consistent.

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

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

The description is a single paragraph but is well-structured with example queries upfront, followed by the core purpose, then details on returns and limitations. Every sentence adds value, though it could be broken into bullet points for even clearer reading.

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

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

Given no output schema, the description covers all return fields and their sources. It also notes limitations (only company type supported, patents sunset). It is complete for a complex tool that aggregates multiple data sources, though it could mention if there are any timeouts or rate limits (but annotations cover safety).

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

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

Input schema has 100% coverage with descriptions for both parameters (type and value). The description adds example values and reiterates that names are not supported, but does not add new semantic information beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description states exactly what the tool does: creates a full cross-source profile of a US public company in one parallel call. It uses specific verbs like 'profile', 'research', and 'brief me', and distinguishes from chaining multiple lookups by explicitly stating it fans out across multiple sources.

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

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

Provides explicit when-to-use guidance: 'ALWAYS PREFER over chaining single-pack...lookups when the user asks for a holistic view.' Also specifies input requirements (ticker or zero-padded CIK) and warns that names are not supported, directing to resolve_entity as an alternative.

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

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

Annotations already provide destructiveHint and idempotentHint. Description adds value by explaining why to use (clear sensitive data) and when, without contradicting annotations.

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

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

Two sentences, each earning its place. First defines purpose, second provides usage guidance. 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?

Simplicity of tool (1 param, no output schema) handled well. Covers purpose, usage, and pairing. Missing potential error handling but not critical.

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?

Only one parameter with 100% schema coverage. Description says 'by key' but schema already documents it. No additional semantic value beyond the schema.

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

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

Description clearly states 'Delete a previously stored memory by key.' with specific verb and resource. Explicitly pairs with 'remember' and 'recall', distinguishing 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?

Provides explicit use cases: stale context, task done, clearing sensitive data. Mentions alternative tools (remember, recall) for pairing.

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

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

Annotations already declare safe operations; description adds process details (fetch, extract, emit) and output format. 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 compact sentences plus bullet use cases. Front-loaded with main action, no excess words.

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

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

With good annotations and full schema, description explains output format and process adequately for a simple tool. Nothing missing.

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

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

Schema covers both parameters fully (url, max_links). Description adds no extra details beyond naming max_links, 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?

Clearly states the tool generates a production-ready llms.txt file for any URL. Distinguishes from siblings like ai_visibility_check by focusing on file generation.

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

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

Lists specific use cases (client site indexing, personal project drafting, competitor auditing). Does not explicitly exclude alternatives but context is clear.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint; description adds specific return fields (card data, tags, downloads, task categories, file list). No contradiction, and description enhances understanding beyond annotations.

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

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

Single sentence of 20 words, front-loaded with purpose. No superfluous information, every word serves the explanation.

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 rich annotations and existence of output schema (though not shown), description covers key output fields. Missing default revision behavior and error cases, but sufficient for a simple 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 has 0% description coverage. Description mentions repo_id with example 'squad' and optional revision but does not provide format details, constraints, or default values. Minimal added meaning beyond schema.

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

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

Description clearly states verb 'Get', resource 'dataset metadata', and specific outputs (card data, tags, downloads, etc.). Distinguishes from sibling tools like search_datasets and list_dataset_files by focusing on single dataset metadata 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?

Description implies usage when detailed metadata for a specific dataset is needed. Does not explicitly state when not to use or provide direct comparisons to siblings, but context from sibling list aids inference.

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, destructiveHint=false, and idempotentHint=true. The description adds little behavioral context beyond stating what info is returned. With 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?

A single sentence that is front-loaded and contains no wasted words. It efficiently conveys the tool's 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?

Given the presence of an output schema and strong annotations, the description adequately covers the returned data. It mentions config, tags, downloads, and files at root, which is sufficient for a simple retrieval tool. Minor omission of revision default is offset by schema documentation.

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 both parameters are documented. The description does not add any additional meaning or context for the parameters, thus no added value beyond schema.

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

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

The description clearly states it returns 'Detailed model info — config, tags, downloads, files at root.' This specifies the resource (model info) and the type of data returned. It differentiates from siblings like list_model_files and search_models, though it could be more explicit about the verb (e.g., 'Retrieve').

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

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

No guidance on when to use this tool versus alternatives like list_model_files or search_models. The description does not provide context on appropriate scenarios or conditions for use.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint; description adds value by naming specific return fields, but no additional behavioral caveats (e.g., rate limits, auth requirements).

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 with efficient, front-loaded structure: purpose first, then optional parameter, then return fields. No wasted words.

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

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

Given simple schema (2 params, no nested objects) and presence of output schema, description covers key aspects. Lacks error handling or validation details, but adequate for basic usage.

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

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

Schema has 0% description coverage; description only gives an example for repo_id and notes optional revision, adding minimal meaning beyond field names. Does not compensate for missing schema descriptions.

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

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

Description explicitly states it retrieves 'Detailed Hugging Face Space metadata' by repo_id, lists return fields (sdk, runtime status, likes, linked models), and distinguishes from sibling tools like get_dataset or 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?

Clearly specifies inputs (repo_id, optional revision) and purpose, making usage context obvious. However, no explicit 'when not to use' or alternatives, though sibling names (get_model, search_spaces) imply distinct roles.

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, non-destructive. Description adds return value details (filename, size, blob SHA) and optional parameters (revision, path), providing useful behavioral context beyond annotations. No contradictions.

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

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

Single sentence, 22 words, directly states purpose and key parameters. No filler or redundancy, perfectly 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?

With output schema present, description need not detail return format but does so helpfully. Covers repo_id, revision, path. Lacks mention of pagination or limits, but for a list tool this is acceptable.

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 0% so description must compensate. It names repo_id as required and mentions revision and path as optional, but does not explain their formats or constraints. Examples partially clarify path as subdirectory, but detail lacking.

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

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

Clearly states the tool lists files in a dataset repository, with specific parameters (repo_id, revision, path) and return fields (filename, size, blob SHA). Distinguishes from sibling 'list_model_files' by scope (dataset vs model).

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

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

Description indicates usage scenario (list files by repo_id and optional revision/path) but does not explicitly state when not to use or mention alternatives like 'list_model_files'. Guidance is implied but not explicit.

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

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

Annotations already cover read-only, idempotent, non-destructive behavior, so the bar is lower. The description adds transparency about return content: path, type, byte size, blob SHA, and LFS details for weight shards. It also clarifies revision defaults (main) and subdirectory behavior, adding 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?

Two sentences, front-loaded with the core action and API context. The first sentence states the operation and parameters; the second details return fields and the use case. No filler or redundant explanation; every sentence contributes.

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 moderate-complexity listing tool with an output schema and strong annotations, the description covers what is done, how parameters behave, what is returned, and the typical use case. No significant gaps for an agent to select and invoke it 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 67% (revision and path have descriptions, repo_id does not). The description adds semantic value by explaining revision as 'branch, tag or commit; main by default' and path as 'optional subdirectory path.' Repo_id is not explicitly described but implied by 'model repository' and examples, compensating reasonably.

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 ('list') and resource ('files in a model repository') with clear scope: at a given revision and optional subdirectory. It distinguishes from siblings like list_dataset_files by focusing on model repos and even mentions LFS details for weight shards, making it unmistakably specific.

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

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

The description states the use case explicitly: 'Answers which weight, config and tokenizer files ship in a Hugging Face model repo and how large they are.' This tells the agent when to use it, and the sibling list_dataset_files implies the alternative for datasets, though no direct exclusion is stated.

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, destructiveHint, etc. Description adds return fields and clarifies active vs. inactive subscriptions, which is useful beyond annotations.

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

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

Two sentences, front-loaded with essential info. No wasted words.

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

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

Covers return fields and usage, but lacks details on pagination or limits. Adequate for a simple list tool.

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

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

Schema covers parameter with description. Description doesn't add new info about the boolean parameter, but the baseline is 3 due to high schema coverage.

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

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

Clearly states verb 'list', resource 'caller's subscriptions', and specifies returned fields. Differentiates from sibling tools 'subscribe' and 'unsubscribe' by implication.

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: 'to review what you're monitoring before adding more or to find an id to cancel.' Provides actionable 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?

With all annotations false, the description carries the full burden. It discloses the claim_token return flow, rate limit of 5 per identifier per day, free/no-quota nature, and that the team reads digests daily — all useful behavioral expectations. 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 long but every sentence earns its place: ownership boundary, category mapping, claim_token lifecycle, rate limits, and quota impact. It is front-loaded with the core purpose and uses compact notation for the claim_token example.

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

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

With no output schema, the description explains both return flows: filing returns a claim_token, and later passing it returns status and changes. It covers scope boundaries, prerequisites, rate limits, and category mappings, making it self-sufficient for a tool with 4 parameters and nested objects.

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 valuable context beyond the schema by explaining the claim_token round-trip usage (pass it back later as pipeworx_feedback({claim_token:"pwfb_…"})) and guiding message content ('describe in terms of Pipeworx tools/packs — don't paste the end-user's prompt').

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 'Tell the Pipeworx team something is broken, missing, or needs to exist,' naming a specific verb, resource, and scope. It clearly differentiates this feedback channel from siblings like ask_pipeworx and discover_tools by framing it as reporting issues about Pipeworx tools themselves.

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

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

It provides explicit triggers: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' It also gives an explicit exclusion: tools from other MCP servers should be filed there, not here.

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 operation; description adds valuable context: data source (CF analytics-engine), no PII, caching behavior (5min-1h), and that it's self-aggregating.

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

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

Four sentences with front-loaded main function, followed by use cases and behavioral notes. No redundant 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?

Despite no output schema, the description clearly states what is returned (top tools, top packs, total call volume) and covers caching, data source, and privacy. Complete for a simple read-only tool.

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

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

The lone parameter 'window' is fully covered by schema (enum and description). The description adds nuance: shorter windows for hotness, longer for steady-state demand.

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

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

The description clearly states that the tool returns top tools, top packs, and total call volume over a window, distinguishing it from other trending tools like trending_datasets and trending_models which focus on different resources.

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 three specific use cases (discovering hot data sources, confirming canonical tool, seeing alignment) but does not explicitly state when not to use or mention alternatives among siblings.

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

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

The description details behavioral traits beyond annotations: explains scanning scope, filters (semantic anchor, partition filter), and fill check logic. It does not contradict annotations (readOnly, idempotent, not destructive).

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 and well-structured, front-loading the main purpose. It is slightly long but each sentence adds value. Could be marginally more concise, but overall effective.

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

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

Despite no output schema, the description fully explains the response structure (opportunities array, partition_check object, fill_check) and behavioral details. It compensates for missing schema.

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

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

Schema covers both parameters (100% coverage). The description adds meaning by explaining mode behavior, example values, and acceptance of full URLs. This goes beyond the schema's basic descriptions.

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

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

The description clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks on Polymarket. It distinguishes itself from siblings like polymarket_edges and polymarket_fill_risk by specifying its unique approach and modes.

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

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

The description provides explicit guidance on when to use each parameter: no args for trending_scan, event for specific markets, topic for cross-event scans. It recommends event mode and mentions alternatives like polymarket_fill_risk for custom sizing.

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 extensive behavioral details: model families, edge calculation, tradeable-edge knobs, caching policy, and diagnostic output. No contradiction with annotations.

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

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

The description is well-structured with clear segmentation and front-loaded purpose. However, it is verbose, containing detailed model explanations that could be slightly condensed without losing essential information.

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

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

Given the tool's complexity (9 parameters, no output schema), the description is highly complete. It details output structure, model families, edge calculations, caching, and diagnostics, providing a comprehensive understanding of capabilities and limitations.

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

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

Schema description coverage is 100%, and the description adds significant context about parameter interactions (e.g., tradeable-edge knobs like min_liquidity and max_spread_pp, and how min_partition_leg_kelly applies to per-leg Kelly). This enhances understanding beyond the schema alone.

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

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

The description clearly states the tool scans Polymarket markets to return opportunities where Pipeworx data disagrees with market price, with a specific use case for 'what should I bet on today'. It details three model families and distinguishes itself from siblings by its comprehensive opportunity discovery mechanism.

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

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

The description explains when to use the tool (opportunity discovery) and provides guidance on tradeable-edge knobs and empty segment diagnostics. However, it does not explicitly state when not to use it or contrast with specific siblings like polymarket_arbitrage or polymarket_edge_tracker.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds substantial behavioral detail: response structure (tracked vs. expired), trend categories, decay computation, snapshot gaps, and limitations.

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, front-loading the core question and detailing response fields and limits. It could be slightly more concise, but all sentences are informative.

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

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

Despite having no output schema, the description thoroughly explains the return fields and their meanings, as well as data gaps and history depth. It fully compensates for the missing output schema.

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

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

Schema coverage is 100%, and the description adds value by clarifying default values, clamping behavior (days 2-30), window options (24hr, 1wk, 1mo), and explaining snapshot write conditions. This goes beyond the schema.

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

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

The description clearly states the tool's purpose: providing edge persistence and decay telemetry from daily snapshots, answering how long an edge has existed and if it's shrinking. It distinguishes itself from sibling tools like polymarket_edges by focusing on time-series analysis.

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

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

The description explains when to use the tool (to differentiate fresh vs. old edges) and includes limits like the 60-day TTL and daily closes. However, it does not explicitly name alternative tools or direct when not to use it, though the context implies 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 declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds behavioral context beyond annotations, detailing the risk of partial basket fills converting an arb into an unhedged directional position and the dominant loss mode in real arb-bot P&L. No contradictions.

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

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

Description is dense but well-structured with clear sections for single-market and basket modes. Every sentence serves a purpose, though it's slightly verbose. Could be more concise, but given complexity, it's appropriate.

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, yet description fully explains return values for both modes, including edge cases like thin_legs and forced_directional_risk. Covers usage guidance and behavioral warnings comprehensively.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by explaining default values, interpretation of size_usd for singles vs baskets, and side semantics for both modes. Provides clarity 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 it's a 'Realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes between single-market and basket modes with specific output fields like top_of_book, vwap_fill_price, etc. It differentiates from sibling tools like polymarket_arbitrage and polymarket_edges.

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

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

Explicitly says 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500' and explains why (theoretical overround on thin books is not capturable, partial fills create directional risk). Provides clear context for when to use and when not to.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds rich context: compatibility_warning conditions, temporal_alignment, skipped_cross_type/subtype counters, and what the response format contains. 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 verbose but well-structured with bolded terms and clear sections. It front-loads the main purpose and then details modes, response fields, and safety considerations. Could be slightly more concise without losing information.

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

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

Given the complexity (cross-venue comparison, safety fields, temporal alignment) and lack of an output schema, the description covers most important aspects. It explains when spreads are meaningful and what the response contains, though a brief example output 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 coverage is 100% with descriptions for all three parameters. The description enhances understanding by explaining the two modes, listing the 10 macro shortcuts, and clarifying how explicit tickers override topic mapping. A minor point: the parameter descriptions in the schema are sufficient, so the added value is moderate.

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

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

The description clearly states it computes cross-venue spreads between Kalshi and Polymarket for the same question, with two modes (topic shortcuts and explicit tickers). It distinguishes itself from sibling tools like polymarket_arbitrage by focusing on Kalshi-Polymarket pairings.

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 each mode, warns that pre-mapped topics often return compatibility_warnings, and explains when spreads are invalid (temporal misalignment, non-equivalent bet shapes). This helps the agent avoid incorrect usage.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds behavioral context beyond annotations: it notes scoping by identifier, the ability to list all keys, and the non-destructive nature. 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 three sentences, all highly informative. It front-loads the primary action ('Retrieve a value...'), then scoping, then pairing. No redundant or extraneous 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 no output schema, the description adequately explains what to expect (value or list of keys). It covers all aspects: usage, parameter behavior, scoping, and relationship with siblings. Highly complete for a simple 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 a single parameter 'key' described as 'Memory key to retrieve (omit to list all keys)'. The description adds meaning by explaining the omission behavior, which is not fully captured in 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 uses specific verbs ('retrieve', 'list') and resources ('value saved via remember', 'all saved keys'), which clearly states what the tool does. It distinguishes from siblings by mentioning 'remember' and 'forget'.

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

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

The description explicitly states when to use ('to look up context the agent stored earlier') and when not ('omit the key argument' to list). It also provides context for pairing with other tools (remember, forget).

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

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

Annotations already declare readOnly, idempotent, non-destructive. Description adds behavioral detail: mark_read flag affects future calls, returns specific fields, and indicates persistence. 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 with no redundancy. Front-loaded with purpose, followed by key details. Every sentence adds unique 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?

With 5 parameters and no output schema, the description covers return format, filtering options, and mark_read behavior. Could mention ordering (most recent) explicitly, but schema hints at time-based filtering. Adequate for a read-only 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 covers all 5 parameters. Description adds value by providing example filter values ('sec_8k'), explaining the effect of mark_read, and clarifying the relationship between parameters. Doesn't fully describe all constraints but improves usability.

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

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

The description clearly states it pulls fired events from subscription feed and specifies key returned fields. However, it does not explicitly differentiate from sibling tools like 'list_subscriptions' or others, which slightly reduces clarity.

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

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

Provides context that polls work fine and mentions an alternative URL for scripts/dashboards. Lacks explicit when-not-to-use or comparison with sibling tools, but sufficient guidance for typical use.

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

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

Annotations already indicate read-only, idempotent, non-destructive. The description adds: fans out to multiple sources, GDELT→GNews fallback, USPTO soft-fail, and return structure 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?

Well-structured with example queries front-loaded. Slightly long but every sentence adds value; could trim redundant phrasing but overall efficient.

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

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

No output schema, but description details return structure (changes grouped by source, total_changes, citation URIs). Covers fallback, soft-fail, and parameter specifics. Fully adequate for 3-param 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 has 100% coverage with descriptions. The description adds examples for `since` (ISO date or relative shorthand like '7d'), clarifies `value` accepts ticker or CIK, and `type` enum is only '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 explicitly states it returns a change feed for a company, listing sources (SEC EDGAR, GDELT→GNews, USPTO) and including example queries. It clearly distinguishes from sibling `entity_profile` by contrast.

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

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

Provides explicit when-to-use: 'What's new' queries. Includes when-not-to-use: 'Use entity_profile instead when you want the static profile'. Also explains fallback behavior and soft-fail 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 indicate idempotent and non-destructive. The description adds useful context: scoped by identifier, persistence duration, and pairing with recall/forget. 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?

Four sentences, each adding distinct value: purpose, when to use, persistence details, and pairing. No wasted words; front-loaded with core action.

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

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

For a simple tool with 2 string params and no output schema, the description covers usage, parameters, persistence, and relationship to related tools. Fully adequate.

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

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

Schema covers both parameters with descriptions. The description adds concrete examples of values (resolved ticker, target address), helping the agent understand usage beyond the generic schema.

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

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

The description clearly states the verb 'save' and resource 'data', specifying it's for reuse across conversations or sessions. It distinguishes itself from sibling tools like recall and forget by mentioning pairing.

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

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

Explicitly tells when to use (discover worth carrying forward) and what to store (resolved ticker, etc.). Also gives context on persistence differences between authenticated and anonymous sessions.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, lowering the bar. The description adds substantial behavioral context beyond that: it reveals that each call cascades through several lookup endpoints, that LEI/FIGI enrichment degrades gracefully (EDGAR identifiers still return if GLEIF/OpenFIGI are unavailable), and that unresolved identifiers are explicitly listed under 'unresolved' rather than omitted. This is exactly the kind of behavioral detail an agent needs.

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 user queries and the core purpose, which is effective. It is verbose, but most sentences add unique value (e.g., cascade behavior, degradation, unresolved handling). There is some redundancy—'accepts ticker, CIK, ISIN, or company name as input' repeats the schema description—and the length could be trimmed slightly, but it remains 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?

For a tool with only two parameters and no output schema, the description is highly complete. It specifies what identifiers are returned for each type, how input formats are interpreted, how unresolved IDs are reported, and how the tool behaves under partial service failure. An agent has everything needed to select and invoke the tool correctly without additional lookups.

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% (both properties have descriptions), so the baseline is 3. However, the description greatly enriches parameter meaning: it explains the 'type' parameter in depth (company returns CIK/ticker/LEI/FIGI; drug returns RxCUI/ingredient/brand), and the 'value' parameter by providing examples and special cases (e.g., an ISIN resolves to the legal entity via GLEIF mapping). This goes well beyond the schema's concise 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's purpose: to 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It uses a specific verb ('resolve') and resource ('name to identifiers'), and distinguishes itself from siblings by noting it provides the IDs other tools need. It also enumerates supported entity types (company, drug), removing ambiguity.

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

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

The description gives explicit guidance with 'Use FIRST whenever you have a name but need an ID,' clearly establishing when to use the tool. It also notes that it replaces 2-3 manual lookups, but it does not explicitly name alternative tools or exclusions. The context is clear, though a direct contrast with siblings would have earned a 5.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds that the tool probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. No contradictions.

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

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

Description is around 80 words, front-loaded with the main action, then details, use case, and output. Every sentence serves a purpose with no 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?

Despite no output schema, description explains the output format (ranked list with score, confidence, signal density). It provides enough context for an agent to understand the tool's functionality and expected results.

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 for all 4 parameters. Description adds value by noting that the first entity is treated as the 'subject' for narrative, which is not in 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?

Description clearly states the tool compares AI visibility across multiple entities side-by-side, specifying the verb 'Compare', resource 'AI visibility', and differentiation from single-entity probe sibling ai_visibility_check. It also details output as a ranked list with score, confidence, and signal density.

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

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

Description explicitly states the tool is useful for competitive AI-marketing audits and gives an example question. It implies usage when comparing multiple entities, but does not provide explicit when-not-to-use guidance or differentiate from compare_entities sibling.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. The description adds behavioral details: fans out to two services, partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts. This goes beyond annotations.

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

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

Single paragraph of about 100 words, front-loaded with purpose. Information is dense but well-organized. Could benefit from slightly more structure (e.g., bullet points for return fields) but remains concise and effective.

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

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

Despite no output schema, the description lists all return fields (summary block with 8 fields, per-advisory detail, links, alternative versions). It covers ecosystem limitation, version defaults, and graceful degradation. Complete for the tool's complexity.

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

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

Schema coverage is 100%, both parameters are described clearly in the schema. The description repeats that version defaults to latest but adds no new semantic value. 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: a composite check for adding an npm package, combining deps.dev and bundlephobia data. It specifies the verb 'scan' and resource 'npm package dependency', and distinguishes from sibling tools (none similar, only scan_competitor_ai_presence which is unrelated).

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

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

Explicitly says when to use ('is X safe / popular / small' or 'what does adding lodash cost me') and when not ('PyPI / Maven / Cargo / Go fall under deps.dev:version directly'). Also mentions potential latency and partial failures, providing clear guidance.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, making the safety profile clear. The description adds value by specifying the return fields (dataset id, downloads, likes, tags) and the supported sort options, which are not covered by the schema.

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 sentence that efficiently conveys the core functionality. It is well-structured and front-loads the key information, though it could be slightly more concise by omitting redundant phrasing.

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 several important aspects: it does not mention default values (e.g., limit), the 'direction' parameter, or the 'full' boolean flag. For a tool with 8 parameters and no required ones, more details are needed to help the agent form a precise request.

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 25%, with only 'language' and 'task_categories' having descriptions. The description clarifies that 'author', 'language', and 'task_categories' are filters, and 'sort' can be 'downloads', 'likes', or 'lastModified', but it omits parameters like 'full', 'direction', 'limit', and 'search', leaving their semantics unclear.

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

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

The description clearly states the tool searches Hugging Face Hub datasets with specific filters (author, language, task_categories) and sorting options, and explicitly lists the returned fields (id, downloads, likes, tags). This differentiates it from sibling tools that search models or 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?

While the description mentions filters and sorting, it provides no guidance on when to use this tool versus alternative search tools (e.g., search_models, search_spaces). There is no explicit 'when-to-use' or 'when-not-to-use' 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 provide readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds that it searches and returns results with filters, but does not disclose additional behavioral traits beyond what annotations already cover.

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 packs many details. It is efficient, though slightly dense. Could be restructured for readability, but overall well-sized.

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 medium complexity (10 parameters) and the presence of an output schema and annotations, the description covers the main purpose and key filters. It doesn't detail pagination or the 'direction' parameter's relationship to sorting, but is reasonably complete.

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

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

Schema coverage is 100% with all 10 parameters described in the schema. The description lists some filters and sorting but adds little meaning beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states it searches Hugging Face Hub models with specific filters (author, library, language, pipeline_tag, tags) and sorting options. It distinguishes this tool from siblings like search_datasets 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?

The description implies when to use this tool (searching models), but does not explicitly state when not to use or compare to alternatives. Among siblings, it's clear it's for models, but lacks explicit exclusion guidance.

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

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

Annotations already declare the tool as read-only, open-world, idempotent, and non-destructive. The description adds that it returns specific fields (space id, sdk, runtime status, likes), which is useful but not critical. It does not disclose pagination, rate limits, or other behavioral nuances.

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, front-loading the purpose. However, it could benefit from slight restructuring or bullet points for 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 7 parameters (0 required) and an output schema (unseen), the description provides a basic overview but misses detailed parameter explanations. It is adequate but not thorough for a complex 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 coverage is low (14%), with only the 'sdk' parameter having a description. The description mentions filters by 'author' and 'sdk' but does not clarify other parameters like 'full', 'sort', 'direction', or 'limit'. This leaves significant ambiguity.

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 'Search Hugging Face Spaces (demo apps)' with specific filters (author, SDK) and expected return fields (space id, sdk, runtime status, likes). This distinguishes it from sibling tools like search_datasets 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?

The description implies usage for searching spaces with filters but does not explicitly state when to use this tool versus alternatives like get_space or search_datasets. No when-not or alternative guidance is 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 show readOnly and idempotent. The description adds technical details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation, and character offsets for verification. 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?

Highly efficient single paragraph: purpose first, usage guidance, technical details. Every sentence adds value with no redundancy.

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

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

Despite lacking an output schema, the description clearly states what is returned (top-N passages with offsets and scores) and explains internal behavior. Complete for a search tool with 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 already describes all parameters (100% coverage) with max chars and example queries. The description repeats this information without adding new semantic meaning beyond the schema.

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

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

The description clearly states the tool does semantic search inside a fetched record, with concrete examples (SEC 10-K, article). It distinguishes itself from siblings by mentioning pairing with ask_pipeworx_grounded and highlighting that it reduces context usage.

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

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

Explicitly states when to use: 'when the record is too big to cram into the prompt' and provides a specific alternative workflow ('Pairs with ask_pipeworx_grounded'). This gives clear context and avoids misuse.

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

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

Adds detail beyond annotations: OAuth requirement, persistence constraints, webhook auto-disable after 10 failures, and phone verification requirement. Annotations already mark idempotent and non-destructive; description aligns well.

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

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

Single paragraph dense with information, well-organized from purpose to requirements to types to delivery. Could be slightly more structured with bullets, but no wasted sentences.

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

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

Comprehensive for a creation tool: covers auth, type-specific params, delivery channels, limits, and return value (subscription id). No output schema needed; description suffices.

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

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

With 100% schema coverage, baseline is 3, but description significantly enriches it: explains each type parameter with real examples, delivery options with explicit requirements (phone verification, email pattern, webhook HMAC signing). Goes well beyond schema.

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

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

The description clearly states the tool creates a proactive monitoring subscription to a live-data event stream. It distinguishes itself from sibling tools like list_subscriptions, unsubscribe, and recent_alerts by explicitly focusing on creation.

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 prerequisites (Pipeworx OAuth account), supported types with examples, and delivery channel specifics including verification and rate limits. Does not explicitly contrast with siblings but context is clear.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds that it returns examples from a live catalog and can be focused by topic, providing useful context beyond the annotations.

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

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

The description is a single, well-structured paragraph that front-loads example queries and efficiently covers purpose, usage, and output. No redundant phrases.

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

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

Despite lacking an output schema, the description explains the return value (category-bucketed examples with tool+argument shapes) thoroughly. Combined with usage guidelines, it provides complete context for the tool's simple interface.

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

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

The schema covers the parameter fully with description and enum-like values. The description reinforces the parameter's usage with examples like 'finance', 'pharma', 'betting', adding value.

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

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

The description clearly states the tool is an onboarding entry point to suggest questions about Pipeworx. It provides example queries and explains it returns category-bucketed examples with tool+argument shapes, distinguishing it from sibling tools like ask_pipeworx and entity_profile.

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

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

The description explicitly says to use this tool first when learning about Pipeworx, and shows how to call with no arguments or a topic. It does not explicitly state when not to use it, but the context implies it's not for direct queries.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds negligible behavioral insight. Does not mention pagination, rate limits, or what happens when limit is exceeded. Description fails to add 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?

Extremely concise: a single phrase. Could be improved by adding a brief note on default behavior or ordering, but overall no wasted words.

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

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

With an output schema present, the description need not explain return values. However, for a tool that returns a list of datasets, it might benefit from noting typical size or ordering. Adequate but not rich.

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

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

Schema coverage is 100%, so baseline 3 applies. The description does not add any extra meaning to the 'limit' parameter beyond what the schema already provides. No additional constraints or examples are given.

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

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

Clearly states it returns trending datasets, which is a specific verb+resource. However, lacks detail on what defines 'trending' (e.g., based on recent views or downloads) and does not differentiate from sibling tools like trending_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?

Implied usage: use to get currently trending datasets. No explicit guidance on when to prefer this over search_datasets or get_dataset. Minimal but sufficient for a straightforward list retrieval.

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

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

The description adds no behavioral context beyond the annotations (readOnlyHint, openWorldHint, idempotentHint). It does not explain how trending is determined (e.g., based on downloads, likes) or any other behavioral traits.

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 wasted words. It is front-loaded and to the point.

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 (1 parameter, output schema exists, rich annotations), the description is minimally adequate. However, it lacks explanation of the 'trending' metric, which could help the agent decide if this tool is appropriate.

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 single parameter 'limit' is already described in the schema (1-100, default 20). The description adds no additional semantic meaning, so baseline 3 is appropriate.

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

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

The description 'Currently-trending models on the Hub.' clearly states the verb (returning/listing trending items) and the resource (models on the Hub). It distinguishes from sibling tools like 'trending_datasets' by specifying 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 versus alternatives such as 'search_models' or 'trending_datasets'. There is no mention of context, prerequisites, or 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?

Adds context beyond annotations: ownership enforcement, soft-delete behavior (deactivation), and impact on historical alerts via recent_alerts.

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

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

Two efficient sentences with no fluff, front-loaded with 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?

Complete for a simple one-param tool: explains purpose, ownership, effect, and relationship to recent_alerts.

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

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

Schema already describes 'id' fully (100% coverage). Description adds ownership semantics ('your own subscriptions'), providing extra 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 clearly states it cancels a subscription by ID, distinguishing it from 'subscribe' and 'list_subscriptions'.

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

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

Explicitly mentions ownership enforcement (only own subscriptions) and fact that row is deactivated but not deleted, providing clear when-to-use and when-not-to-use guidance.

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

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

Even with annotations declaring read-only, open-world, and idempotent behavior, the description adds substantial behavioral detail: the SEC EDGAR/XBRL fast path vs. grounded pipeline, the exact verdict list, the presence of pipeworx:// citations, the meaning of could_not_verify and unsupported, and the number of sequential calls it replaces. This gives the agent a strong mental model of what happens under the hood.

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 every sentence earns its place: trigger phrases, use case, routing logic, return format, critical error semantics, and efficiency benefits. It is front-loaded with user-facing phrasing and scales logically from purpose to pipeline to output, with no fluff or redundancy.

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

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

With only two parameters, good annotations, and no output schema, the description nonetheless covers what the agent needs: verdicts, actual value with citation, reasoning, and how to handle edge cases (could_not_verify vs. unsupported). It also explains the tool's internal fallback behavior, making it complete for a complex natural-language verification tool.

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

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

The input schema already provides 100% coverage for both parameters with detailed descriptions (e.g., tolerance_pct default, overrides, and hallucination-detection use case). The description adds high-level context such as 'exact percent-delta math' and 'tolerance implied by the claim wording', but it does not materially expand on what the schema already provides, so the baseline 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 opens with explicit user-intent triggers ('fact check', 'verify the claim that…', 'true or false') and clearly states it is 'natural-language claim verification against authoritative sources'. It also states when to use it: 'whenever the agent needs to check whether something a user said is factually correct', which distinguishes it from general-purpose tools.

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

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

The description provides a clear 'Use whenever' directive and explains the internal routing for company-financial vs. other factual claims. It also gives important caller instructions about could_not_verify and unsupported, but it does not explicitly name alternative tools or state when NOT to use this tool, leaving exclusions implicit.

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