Data Nantes

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds critical behavioral context: Anthropic calls require user's own API key and direct payment, and the return structure includes per-model fields plus a combined view.

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

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

Three sentences front-loaded with purpose, then model details and use cases. No redundant information; each sentence adds value.

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

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

Despite no output schema, the description details return structure (per-model {score, confidence, signals, raw_response} + combined view). It covers all parameters and provides clear use cases, making it complete for a probe-and-score 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. The description adds meaning by explaining the default model, how to enable Anthropic, and providing an example for 'entity'. It goes beyond the schema by contextualizing parameter usage.

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

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

The description clearly states the tool probes LLMs for knowledge about a business/brand/product/topic and scores visibility (0-100) per model. It distinguishes from sibling tools like 'scan_competitor_ai_presence' by focusing on scoring per model with a combined view.

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

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

Explicit use cases are given (AI-marketing audits, pre-launch brand checks, competitive monitoring). It also explains model selection (default workers-ai, optional Anthropic with _apiKey). No explicit when-not-to-use, but context is clear.

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

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

With annotations already declaring readOnlyHint=true and idempotentHint=true, the description goes beyond them by explaining that the tool automatically routes to the correct underlying tool, fills arguments, returns structured answers, and provides stable pipeworx:// citation URIs. It also mentions news routing and the existence of a grounded variant for hallucination-resistant evidence, which adds useful 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 front-loaded with the core directive ('PREFER OVER WEB SEARCH'), then uses domain lists and concrete examples efficiently. It is slightly long but each sentence adds routing or boundary information, and the ending alternatives are actionable.

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 broad routing role, the description gives enough context for an agent to select and invoke it correctly, including unambiguous input expectations, what it returns, and when to choose another sibling. Some minor gaps exist around failure behavior or latency, but these are less critical for a read-only 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?

Schema coverage is 100% and the schema documents 'question' and all aliases clearly. The description does not need to add parameter-level detail since the schema already covers it, and the description reinforces that input is a natural-language question.

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 names a specific action ('ask', 'routes the question') and a concrete resource domain: 5,596 tools across 1,465 verified sources covering SEC filings, FDA data, FRED/BLS, patents, real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, and more. It also distinguishes itself from siblings by explicitly saying 'PREFER OVER WEB SEARCH' and by referencing ask_pipeworx_grounded and deep_research as alternative 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 is explicit about when to use this tool: any factual question with trigger phrases like 'what is', 'look up', 'find', 'get the latest', 'how much', 'current', or 'current'. It even provides representative example queries and clarifies that it should be used 'even if web search could also answer 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 already declare readOnly, idempotent, and non-destructive behavior, so the description does not need to repeat those. It adds valuable context: current router behavior matches ask_pipeworx exactly, no candidate is active right now, it is a full working router rather than a fallback stub, and output is compared against the stable router.

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

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

The description is relatively long but every sentence serves a purpose: defining the beta relationship, explaining current behavior, giving usage guidance, and cautioning about the experimental edge. It is front-loaded with the key purpose and does not waste words on re-stating schema or annotations.

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 parameter schema is fully documented and annotations cover safety, the description supplies the missing behavioral context: why this beta exists, what is currently active, and what fallback behavior is not. It does not describe the response shape in detail, but explicitly says it matches ask_pipeworx, which is sufficient for an agent familiar with that sibling.

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

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

Schema description coverage is 100%, so the parameter docs already carry the burden. The description adds no parameter-level detail, but it does say the tool takes the same arguments as ask_pipeworx, which is a useful summary. This aligns with the baseline 3 for 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?

The description states a specific role: a beta version of ask_pipeworx that behaves as an identical universal router with the same tools, arguments, and response shape. It clearly distinguishes itself from the stable ask_pipeworx and ask_pipeworx_grounded siblings by emphasizing its experimental candidate-routing status.

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 it exactly like ask_pipeworx when you want the newest routing, which gives a concrete selection criterion. It does not state explicit when-not-to-use conditions, but the beta label and the comparison role make the intended usage clear enough.

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

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

Beyond annotations of readOnly/idempotent/openWorld, the description reveals refusal behavior with exact refusal_reason values, evidence extraction semantics, the extra LLM call cost, and the guarantee to only use tool results. This gives the agent an accurate model of how the tool behaves at runtime.

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

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

Every sentence earns its place: purpose, behavior, return/refusal shape, use cases, and cost tradeoff. The most important differentiator of groundedness is front-loaded, and there is no redundancy or filler tied to existing schema 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?

This tool has no output schema, and the description compensates by fully specifying both success and refusal return structures. Combined with the annotations and parameter schema, the agent has everything needed to decide when to call it and what to expect.

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

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

The schema already documents the single conceptual parameter, question, at 100% coverage and lists all aliases. The description adds little about parameter meaning or format, 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 states a specific verb and resource: it is the grounded/hallucination-resistant answer mode that extracts answers only from tool results. It explicitly contrasts itself with ask_pipeworx, which prevents any confusion between the 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?

Usage guidance is explicit: use when answers will be quoted, cited, or acted on, and inventing facts is unacceptable. It even names the alternative, ask_pipeworx, and says to prefer it for casual lookups, giving clear selection 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 adds significant behavioral context beyond annotations: it explains that low-confidence resolutions short-circuit with status:'low_confidence_match', closed markets return 'market_closed_or_inactive', and wide spreads carry 'illiquid_wide_spread'. It details resolver contract and parent event extraction. No contradiction with annotations (readOnlyHint, openWorldHint, etc.).

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

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

The description is lengthy but well-structured, with clear sections for resolver contract, parent extractor, news fields, safety, etc. Every sentence adds value, though some details could be condensed. The length is justified by the tool's complexity.

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

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

The description covers all aspects: input types, output shapes (market, analysis, evidence), resolver contract, parent event extraction, news fields, safety mechanisms, resolution rule risk, and parameter explanations. No output schema exists, so the description fully compensates by describing return values in detail.

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

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

Schema coverage is 100%, but the description adds substantial meaning: it explains that market can be a slug, URL, or question text; depth options (quick vs thorough); include_raw (false recommended for summary, true for raw payloads). Examples further clarify usage.

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

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

The description explicitly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It details inputs (slug, URL, question text) and outputs (evidence packet + market-vs-model comparison). This distinguishes it from siblings like polymarket_arbitrage and polymarket_edges, which focus on different aspects.

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

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

The description provides clear usage scenarios: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also advises on when to inspect market_match_confidence before trusting analysis, and warns about resolution rule risk. No explicit when-not-to-use, but the scenarios imply alternatives.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds rich behavioral details: data sources (SEC EDGAR/XBRL, FAERS), handling of fiscal years, sorting by primary metric, and output format with citation URIs.

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

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

The description is somewhat lengthy but every sentence adds value. It is front-loaded with trigger phrases and core function. Could be slightly tighter, 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?

Given the tool's complexity (handling two entity types with different data sources) and lack of output schema, the description covers the main return values and data sources. The mention of 'paired data' is a bit vague, but it suffices for basic 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 description coverage is 100%, but each parameter description is brief. The main description adds useful context: what 'type' triggers for companies vs drugs, and examples for 'values' (tickers, drug names). This enhances meaning beyond the schema, earning a 4.

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

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

The description clearly states it compares 2-5 companies or drugs side-by-side, using verbs like 'compare', 'rank', and 'head to head'. It distinguishes from siblings by explicitly recommending preference over sequential single-pack lookups.

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

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

The description provides explicit trigger phrases (e.g., 'Compare X and Y', 'X vs Y', 'which is bigger') and explains when to use (for comparisons) and when not to (prefer over sequential lookups). It also gives examples of 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, idempotentHint, and non-destructive behavior. The description adds value by specifying what metadata is returned (fields/schema, themes, record count), which is useful since there is no output 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, well-structured sentence that front-loads the core purpose. Every word contributes value without redundancy.

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

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

The description succinctly covers what the tool returns (fields/schema, themes, record count), which is sufficient for a metadata tool with no output schema. It could mention that it does not return actual data, but the mention of 'metadata' implies this.

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

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

The only parameter dataset_id is described in the schema as 'from search_datasets,' and the tool description reinforces that context. However, the description does not add new meaning beyond what the schema already provides, so it's adequate but not exceptional.

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 gets metadata for a Nantes Métropole Open Data dataset, specifying fields/schema, themes, and record count. It distinguishes from sibling tools like search_datasets and query by indicating this is a preliminary step to learn column names before querying.

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 'call before query to learn the column names,' providing clear guidance on when to use this tool. While it does not list alternatives, the context makes the usage intent obvious.

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 carry readOnly/openWorld/idempotent traits, and the description adds value beyond that: timing expectations (15-60s, up to ~90s for thorough), a paid-tier constraint on depth='thorough', the guarantee that gaps[] are never invented, the citation_uri is only present when it can actually be served by resources/read, and the semantic-excerpting behavior for long records. No contradiction with 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 long (~400 words) but near-zero fluff: each clause earns its place by informing a call decision (auth, tier, alternative, scope, output packet, gaps, depth semantics, timing). The most critical constraint (ACCOUNT REQUIRED + signed-out fallback) is front-loaded; however, the third-party run-on makes it hard to parse as prose and the content could be restructured into short labeled sections 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?

For a tool with 2 parameters and no output schema, the description is complete: it conveys prerequisites, input-shape expectations, the full output packet schema (verbatim evidence, confidence, source, fetched_at, citation_uri, hop, gaps[], contradictions[]), degree semantics, edge-case behavior when data sources miss the topic, and the expected latency. The agent is equipped to call or reject the tool correctly without opening any further 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% and the schema already documents both params well, including the depth enum semantics, so the baseline is 3. The description adds actionable behavior beyond the schema: latency expectations per depth, the paid-plan gate on 'thorough', and the relationship between depth and the gap-recovery/contradiction passes. This is a moderate, real increment over the schema.

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

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

The description uses a specific verb+resource: 'Grounded multi-source research across Pipeworx's 1465 STRUCTURED data sources' in one call. It explicitly distinguishes itself from siblings by stating 'this is NOT open-web search' and 'this is not a single lookup,' so an agent can tell it apart from ask_pipeworx and query before opening any schema.

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?

Usage guidance is fully explicit: best for broad/multi-part structured-data questions with concrete examples ('compare X and Y's regulatory + financial exposure'). It gives named alternatives with conditions: not signed in → use ask_pipeworx, single lookup → ask_pipeworx, breaking/colloquial current-news → prefer ask_pipeworx because deep_research returns mostly empty gaps for topics outside the structured catalog.

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. Description adds valuable behavioral details: returns top-N tools with full schemas ready to call, no second lookup needed. It also lists specific data domains, providing context on what the tool can search.

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 clear and front-loaded with purpose. However, it includes a long list of domains which, while useful, could be slightly more concise. Still well-structured and every sentence adds value.

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

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

Given no output schema, the description adequately explains what is returned: top-N tools with names, descriptions, and full input schemas (with curated examples). It also specifies the default and max limits. No gaps for functional 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%. Description enhances parameter meaning: explains query accepts natural language, lists aliases (task, q, description, search), provides examples, and describes limit behavior. Adds that results are 'ready to call' without additional lookups.

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 'Find tools by describing the data or task.' The verb 'find' and resource 'tools' are explicit. The description distinguishes this tool from siblings (e.g., 'query', 'search_datasets') by specifying it is for discovering tools, not data. Includes a list of domains to further clarify scope.

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

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

Explicitly tells when to use: 'Use when you need to browse, search, look up, or discover what tools exist for...' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear context and exclusion guidance.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. Description adds details about parallel execution, sources (SEC, XBRL, USPTO, GDELT, GLEIF), soft-fail for patents API sunset, and fallback behavior. No contradictions.

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

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

Description is somewhat long but well-structured with example queries upfront followed by detailed output spec. Every sentence adds value; could be slightly more concise but remains clear.

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

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

For a complex multi-source tool without output schema, the description comprehensively covers return fields, data sources, limitations (patents sunset), and fallback behavior. Annotations provide safety profile. Adequate for agent decision-making.

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

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

Schema has 100% description coverage. Description adds context: value must be ticker or zero-padded CIK, names not supported, type only 'company' for now. Adds value beyond schema by clarifying format and restrictions.

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

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

The description clearly states the tool provides a full cross-source profile of US public companies, with example queries and a detailed list of return fields. It distinguishes itself from siblings like compare_entities or deep_research by focusing on a holistic view.

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

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

Explicitly says 'ALWAYS PREFER over chaining single-pack lookups when the user asks for a holistic view' and advises using resolve_entity first if only a name is available. Provides clear when-to-use and when-not-to-use guidance.

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

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

Annotations already indicate destructive and idempotent behavior. The description adds context about clearing sensitive data, but does not mention what happens if the key does not exist or confirm persistence of deletion. Still, it aligns with annotations and adds moderate value.

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

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

Two sentences: first states purpose, second provides usage and pairing advice. No extraneous information, front-loaded structure.

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 low complexity (1 param, no output schema), the description covers purpose, when to use, and related tools. Minor gaps like return value or error handling, but overall complete for a simple deletion 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' whose description is 'Memory key to delete.' The description does not add new semantics beyond the schema, so baseline 3 applies.

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

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

The description clearly states 'Delete a previously stored memory by key,' specifying a concrete action on a defined resource, and it distinguishes itself from siblings 'remember' and 'recall' by implying deletion versus storage or 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?

Explicitly provides when-to-use scenarios: 'when context is stale, the task is done, or you want to clear sensitive data.' Also advises pairing with related tools, giving clear usage context.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable detail about the process (fetching, extracting, emitting markdown) and the output format (single text blob for site-root/llms.txt), going beyond the annotations without contradiction.

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

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

The description is two sentences with no wasted words. It front-loads the main action and purpose, then provides context and uses cases. Excellent balance of detail and brevity.

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

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

Despite lacking an output schema, the description fully explains the output format and process. The tool is simple (2 params, no nesting), and the description covers all necessary information for correct invocation, making it complete.

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

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

Schema description coverage is 100%, so the parameters are already well-documented. The description does not add new semantic information beyond restating defaults (max_links default 25, max 50). 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 with a specific verb and resource ('Generate a production-ready llms.txt file for any URL'), and lists concrete use cases that distinguish it from sibling tools like 'scan_competitor_ai_presence' or 'ai_visibility_check'.

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

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

The description provides explicit use cases (e.g., indexing client sites, drafting for own projects, auditing competitors), which helps the agent decide when to use it. However, it doesn't mention when NOT to use or list alternative tools, preventing a perfect score.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false. The description adds the field list but no additional behavioral traits beyond that.

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

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

The description is two sentences, front-loading the core purpose and immediately providing usage guidance with no redundant information.

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

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

Although no output schema exists, the description lists return fields. The tool is simple and the description covers its essential aspects, though pagination details are omitted.

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 no extra meaning to the include_inactive parameter beyond what the schema already provides.

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

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

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

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

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

The description provides explicit usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel,' though it does not specify when not to use.

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

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

Annotations are all false and provide little safety context, so the description carries the burden. It discloses side effects (filing feedback, returning claim_token), rate limits (5/day), cost (free), and downstream impact (digests read daily). This goes well beyond the annotations.

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

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

The description is long but every sentence carries useful information: purpose, use cases, exclusions, token flow, rate limits, and cost. It is front-loaded with purpose and logically organized, though slightly dense.

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 the key return value (claim_token) and how to use it later. It covers rate limits and scope, but does not fully describe the complete response format or error cases. Still, it is sufficient for a feedback submission tool.

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

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

Schema coverage is 100% and the schema already describes each parameter thoroughly. The description adds a concrete usage example for claim_token and clarifies how to reference Pipeworx tools, which adds value beyond the schema.

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

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

The description opens with a specific verb and resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It clearly distinguishes from siblings like ask_pipeworx by focusing on feedback rather than queries, and explicitly excludes tools from other MCP servers.

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 categories (bug, feature/data_gap, praise) and explicit when-not-to-use (tools from other MCP servers). Also explains the claim_token workflow for checking status, which is a key usage pattern.

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, not destructive), the description discloses data source (CF analytics-engine), privacy implications (no PII), and caching behavior (5min-1h). This adds value 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 concise: one sentence for purpose, then bullet-style use cases, then technical details. Every sentence serves a purpose without redundancy.

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

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

For a simple read-only tool with one optional parameter and no output schema, the description fully covers purpose, output shape, caching, and privacy. No gaps remain.

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

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

Schema coverage is 100% (only parameter 'window' with enum). Description adds meaning: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This helps the agent choose between options.

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

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

The description specifies exactly what the tool does: 'Returns the top tools, top packs, and total call volume over a recent window'. It uses a specific verb+resource and distinguishes from siblings by focusing on trending analytics, which no other sibling tool does.

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 lists three explicit use cases: discovering hot data sources, confirming canonical tool, and aligning use case with agent needs. While it doesn't state when not to use or name alternatives, the guidance is clear and practical.

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 the annotations (readOnlyHint, idempotentHint, openWorldHint, non-destructive). It explains semantic anchor (Jaccard similarity), partition filter, fill check with realizable edge, and conditions for not trading. 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 detailed and front-loaded with the core purpose, but it is somewhat verbose, containing many technical specifics. Every sentence adds value, but a slightly more condensed version could improve conciseness without losing clarity.

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

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

Despite lacking an output schema, the description comprehensively covers the response structure, including fields like opportunities[], partition_check, and fill check details. All modes and parameters are fully explained, making the tool's behavior and output 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 basic descriptions, but the description adds substantial context: for event it explains walking child markets and partition checks; for topic it explains cross-event scanning and similarity checks. This greatly enriches parameter understanding.

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

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

The description clearly states the tool's purpose: finding arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes three modes (no args, event, topic) with specific use cases, making it highly specific and differentiated from siblings.

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

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

The description provides explicit guidance on when to use each mode: 'Call with NO args for a trending_scan', 'pass event for the strongest per-event partition_check', 'topic for a themed cross-event scan'. It also references a sibling tool for custom sizing. However, it does not explicitly state when not to use this tool versus all sibling tools like polymarket_edges 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?

All annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) are consistent and the description adds significant context beyond them: detailed breakdown of model families and their calculations, edge_pp_net (net of slippage), kelly fractions with caps, tradeable-edge knobs, diagnostics for empty segments, and caching behavior. The description discloses the tool's behavior thoroughly.

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: it opens with a clear headline purpose, then breaks into segments and fields. Each segment is explained with sufficient context. However, it is long and could benefit from bullet points or clearer separation; currently it is one block of text. Still, it earns its length for the complexity.

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

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

Given 9 parameters, no output schema, and annotations, the description is remarkably complete. It details the return structure (by_segment, fed_candidates, _diagnostics), explains edge fields and their meanings, describes the tradeable-edge filters, and mentions caching. There are no apparent gaps; an agent can fully understand the tool's behavior from this description.

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

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

Schema description coverage is 100% and the schema comments are already comprehensive (e.g., min_kelly explains half-Kelly fraction and default). The tool description adds no additional parameter-level detail beyond grouping and high-level context. As per guidelines, with high coverage, a baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It specifies the use case ('what should I bet on today') and directly addresses the agent's need without ambiguity. Distinguishes from sibling tools by its focus on Pipeworx data and opportunity discovery.

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

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

The description provides a usage context ('for what should I bet on today') and explains how to use knobs (e.g., min_liquidity, max_spread_pp). However, it does not explicitly compare to alternatives like polymarket_arbitrage or polymarket_edge_tracker, nor does it state when not to use this tool. The guidance is useful but incomplete for an agent deciding 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 adds behavioral context beyond annotations, including snapshot TTL, data gaps, and decay computation from daily closes. No contradictions with annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint false).

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 purpose and contains valuable details, but is somewhat lengthy. Every sentence contributes, though some restructuring could improve readability.

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

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

Given simple parameters and no output schema, the description fully details the response structure (tracked, expired, snapshot_dates) and limitations (TTL, data gaps), making it complete.

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

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

Schema coverage is 100% with parameter descriptions. The description adds minor clarifications (e.g., clamp 2-30 vs max 30, default 14) but does not significantly enhance understanding beyond the schema.

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

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

The description clearly states it provides edge persistence and decay telemetry, answering 'how long has this edge existed and is it shrinking?' It distinguishes itself from siblings like polymarket_edges by focusing on historical persistence rather than current edges.

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

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

The description implies usage through the example contrasting fresh vs. 3-week-old edges, but does not explicitly state when to avoid this tool or mention alternatives. Given the context of sibling tools, the usage is clear but lacks explicit exclusions.

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

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

While annotations already indicate readOnly, idempotent, and non-destructive behavior, the description adds substantial context: it walks the order book ladder, returns a verdict (clean|degraded|cannot_fill), and warns about thin books and forced directional risk. This fully informs the agent of behavioral traits beyond annotations.

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

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

The description is dense and front-loaded with the core purpose, but it packs a lot of information into a single paragraph. While effective, it could be slightly more structured (e.g., bullet lists for return values) to improve scannability. Still, every sentence adds value.

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

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

Given the tool's complexity (two modes, multiple return fields, no output schema), the description provides a remarkably comprehensive overview. It enumerates all key return fields for both modes (e.g., top_of_book, vwap_fill_price, capture_ratio, forced_directional_risk) and explains the dominant failure mode, leaving no critical gaps.

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

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

Schema coverage is 100%, but the description adds significant semantics: it clarifies that side defaults to 'auto' for basket mode, size_usd is clamped to 10–1,000,000 (not in schema), and it explains the REQUIRES constraint (market or event). This goes well beyond the schema definitions.

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: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes between single-market and basket modes and explicitly ties its usage to sibling tools like polymarket_arbitrage and polymarket_edges, making its role in the tool ecosystem 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 usage guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also warns against partial basket fills converting an arb into an unhedged directional position, giving clear 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 indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds substantial behavioral context: two modes, safety fields explaining compatibility_warning and temporal_alignment, and details on skipped comparisons. No contradictions.

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

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

The description is front-loaded with the core purpose, uses clear section labels (TWO MODES, RESPONSE, SAFETY FIELDS), and every sentence adds meaningful detail without redundancy. It is well-structured for an AI agent 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?

Given no output schema, the description fully explains the response structure (leg-by-leg prices, top_spreads_pp, compatibility fields, temporal alignment, skipped counters). It covers all scenarios including when spreads are meaningless, making it complete for agent decision-making.

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

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

Schema coverage is 100% with all three parameters described. The description adds value by listing the topic values explicitly and explaining how explicit parameters override topic-mapped ones. This is useful beyond the schema.

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

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

The description clearly states it computes cross-venue spreads between Kalshi and Polymarket for the same resolving question, with two modes (topic shortcuts vs explicit parameters). It distinguishes itself by focusing on spread across venues, which no sibling tool appears to do.

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

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

It explains the two modes and when to use each: topic shortcuts for common macros like 'fed', explicit for custom pairs. It also warns that most pre-mapped topics return compatibility warnings, setting realistic expectations. However, it does not explicitly contrast with sibling tools like polymarket_arbitrage or polymarket_edges.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds context about using ODSQL and the specific dataset, confirming the read-only nature. 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 with no wasted words. It front-loads the core action and then succinctly lists the operations in parentheses. Highly 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?

The description covers the key operations but omits information about return format or any limitations. Given there is no output schema, the agent might need more context about what the response contains (e.g., structure of records). Adequate but not fully complete.

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

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

Schema coverage is 100%, so baseline is 3. The description enhances understanding by grouping parameters into categories (filter, aggregate, sort, paginate), which adds semantic structure beyond the schema's individual descriptions.

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

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

The description clearly states the tool queries records from a specific dataset (Nantes Métropole Open Data) using ODSQL, and lists filtering, aggregation, sorting, and pagination. This verb+resource combination distinguishes it from siblings like search_datasets and dataset_info.

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

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

Usage context is implied by the purpose, but there is no explicit when-to-use, when-not-to-use, or mention of alternatives. The description does not guide the agent on when to prefer this over similar tools like search_within.

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. The description adds scope details (IP, key hash, account ID) and pairing behavior, providing useful context beyond annotations.

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

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

Two sentences, front-loaded with purpose, no superfluous words. Every sentence contributes 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?

For a simple retrieval tool with one optional parameter and comprehensive annotations, the description fully covers scope, pairing, and usage. No output schema needed as return values are implied.

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

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

The single parameter 'key' has schema description 'Memory key to retrieve (omit to list all keys)'. The description reinforces the omitting behavior and adds usage context, adding value beyond the schema's baseline 3 for high coverage.

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

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

The description states 'Retrieve a value previously saved via remember, or list all saved keys', clearly indicating the verb (retrieve/list) and resource (saved memory). It distinguishes from siblings '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 explains when to use (look up context stored earlier, avoid re-deriving) and mentions pairing with remember/forget. It does not explicitly state when not to use, but provides clear context.

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

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

The description states that setting mark_read:true flags events as read, which implies a state change. However, annotations include readOnlyHint:true, indicating no writes. This contradiction undermines transparency, resulting in a score of 1.

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

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

The description is concise (4 sentences) with the main verb and resource front-loaded. Every sentence adds detail without redundancies, making it efficient for the agent 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?

Despite lacking an output schema, the description explains the return structure (source, citation_uri, payload), covers filter behavior, and mentions side effects of mark_read. It adequately covers all necessary information for a tool with five optional parameters.

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

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

Schema coverage is 100% and parameters have descriptions. The description adds value by providing examples (e.g., 'sec_8k' filter, ISO timestamp) and clarifying the effect of mark_read, going beyond the schema's baseline.

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

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

The description clearly states the tool pulls fired events from the subscription feed and specifies the returned data (source, citation_uri, payload). It distinguishes itself from siblings by focusing on alerts retrieval, which is unique among the listed tools.

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

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

The description provides context on filtering by type and since, mentions polling suitability, and notes an alternative URL for scripts/dashboards. It does not explicitly exclude scenarios but offers clear usage guidance for the intended purpose.

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

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

Beyond annotations (readOnlyHint, idempotentHint), description details parallel calls, news fallback logic (GDELT→GNews), USPTO soft-fail due to API sunset, and output structure with grouped changes and citation URIs.

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

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

Description is packed with examples and details, but front-loaded with query patterns. Slightly verbose but every sentence serves a purpose. Could be tightened slightly.

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

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

No output schema, but description fully explains return structure (changes grouped by source, total_changes, citation URIs) and covers edge cases like fallback behavior and API sunset. 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?

Despite 100% schema coverage, description adds practical usage guidance: explains relative date formats, suggests '30d' for monitoring, clarifies value accepts ticker or CIK, and notes type is limited to '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?

Description clearly states it provides a change feed for a company, listing recent updates across SEC, news, and patents. It distinguishes from sibling entity_profile which serves static profiles.

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

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

Explicitly lists query examples and when to use (e.g., 'What's new with X'), and directs to entity_profile for static profiles. Provides clear context for choosing between tools.

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

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

Beyond annotations, describes scoping by identifier, persistence difference between authenticated (permanent) and anonymous (24 hours). 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?

5 sentences, front-loaded purpose, every sentence provides value. No redundancy.

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

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

Fully explains storage behavior, scoping, and expiration for a simple write tool with no output schema.

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

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

100% schema coverage provides full parameter descriptions. Description adds naming convention examples but doesn't significantly augment semantics 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 states 'Save data the agent will need to reuse later' – a specific verb+resource. It distinguishes from siblings recall and forget, making the purpose clear.

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

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

Explicitly says 'Use when you discover something worth carrying forward' and gives examples. Mentions complementary tools (recall, forget). Lacks explicit when-not-to-use, but context is sufficient.

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

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

While annotations already declare read-only, idempotent, and non-destructive behavior, the description adds rich behavioral context: it explains that identifiers are source-labelled, that unresolved identifiers are explicitly listed under `unresolved` rather than omitted, that enrichment degrades gracefully if GLEIF/OpenFIGI are unavailable, and that each call internally cascades through several lookup endpoints. This goes well beyond the safety profile provided by annotations.

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

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

The description is heavily detailed but somewhat verbose. The opening quote block lists seven example queries, many of which are redundant, and the company type description is a single long, complex sentence. Still, the main purpose ('resolve a user-spoken NAME...') is front-loaded, and the structured sections (purpose, supported types, fallback) are logically ordered. It could be trimmed without losing semantic value.

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

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

For a complex tool with no output schema, the description covers the return semantics well: it lists the identifiers returned for companies (CIK, ticker, LEI, FIGI, ownership) and drugs (RxCUI, ingredient, brand, citation), and explains behavior for unresolved identifiers and service degradation. It also clarifies the input normalization (ISIN mapping, ticker/CIK/name) and the fact that it replaces multiple manual lookups, making it sufficiently complete for an agent to invoke correctly.

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

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

The schema covers the two parameters at 100% coverage, but the description adds meaning beyond it: it introduces ISIN as an accepted input for company queries (not mentioned in the schema), describes how an ISIN resolves to the legal entity via GLEIF, and details the exact outputs (e.g., RxCUI + ingredient + brand for drugs). This extra context enhances the agent's ability to populate parameters correctly.

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

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

The description explicitly states its purpose: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It enumerates supported entity types (company, drug) and specific identifier outputs (CIK, LEI, FIGI, RxCUI), distinguishing it from sibling tools like entity_profile or compare_entities.

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

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

The description gives direct usage guidance: 'Use FIRST whenever you have a name but need an ID.' It provides input format examples and notes that it replaces 2-3 manual lookups. However, it does not explicitly list when-not-to-use scenarios or name alternative tools, so it stops short of a full inclusion/exclusion guideline.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true, indicating a safe, non-destructive, and potentially non-deterministic operation. The description adds behavioral details: it outputs a ranked list with score, confidence, and signal density per entity, and internally uses 'ai_visibility_check'. No contradictions.

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

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

The description is two sentences: first states the core action (compare and rank), second gives a practical use case and output summary. 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?

With no output schema, the description explains the return format (ranked list with score, confidence, signal density). Parameter descriptions are fully covered in schema. The description differentiates from siblings, mentions internal tool usage, and provides a realistic usage example. All aspects are addressed.

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

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

Schema description coverage is 100% with good parameter descriptions. The tool description adds value by explaining that the first entry in 'entities' is treated as the 'subject' for narrative purposes, and that 'context' is shared across probes. This nuance goes beyond the schema, justifying above baseline 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 compares AI visibility across multiple entities, ranks them by score, and surfaces which is most/least recognized. It distinguishes itself from siblings like 'ai_visibility_check' (single entity) and 'compare_entities' (generic comparison) with a specific verb 'scan' and concrete use case example.

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 case ('competitive AI-marketing audits') and an example question. It implicitly suggests using 'ai_visibility_check' for single entity checks by stating it probes each entity with that tool, but does not explicitly list alternatives or state when not to use. This is slightly lacking for a top score.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds value by detailing partial failure modes (bundlephobia first measurement can take 5-30s, sources_failed listed), which supplements the 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?

Description is relatively long but front-loaded with the main purpose. Each sentence adds value (behavior, limitations, return content). Could be slightly more concise, but 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?

Without an output schema, the description thoroughly lists return fields (summary block, per-advisory detail, links, alternative versions). It also explains partial failure behavior and ecosystem limitations, making it complete for the agent.

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

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

Schema has 100% description coverage for both parameters. Description adds practical context: scoped packages accepted (e.g., @types/node), version defaults to latest. This goes beyond schema, providing useful usage guidance.

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

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

The description clearly states it is a composite check for npm packages, combining deps.dev and bundlephobia. It uses specific verbs ('scan', 'check') and identifies the resource (npm package). Sibling tools include various unrelated tools; this tool's purpose is distinct.

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

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

Explicitly advises when to use: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also notes scope limitations (NPM ecosystem only in v1) and handles partial failures gracefully, providing guidance on expected behavior.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds return value details (dataset_ids, titles, themes, record counts) but no additional behavioral context (e.g., data source freshness, permissions).

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

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

Two efficient sentences with no wasted words. Front-loaded with action and scope. Compact yet 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?

Covers purpose, return type, and relationship to sibling tools. Lacks discussion of pagination behavior or error cases, but schema covers offset/limit limits. Sufficient for a simple search tool with no output schema.

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

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

Schema description coverage is 100% for all 3 parameters. The description does not add new parameter insights beyond the schema (e.g., date filtering, format requirements). 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 verb 'Search', resource 'Nantes Métropole Open Data datasets', and what is returned (dataset_ids, titles, themes, record counts). It also distinguishes from sibling tools by mentioning passing dataset_ids to query/dataset_info.

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

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

Implies when to use: for searching datasets by keyword. References related tools (dataset_info, query) for further steps. Does not explicitly state when not to use, but context is clear.

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

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

Adds significant detail beyond annotations: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flagging. All annotations (readOnly, idempotent, openWorld, non-destructive) are consistent and reinforced.

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 front-loaded purpose, clear examples, and technical details. Every sentence serves a purpose without redundancy. Concise while covering all essential aspects.

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

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

Despite no output schema, description fully explains return values (passages with character offsets and similarity scores). It also covers edge cases (200K char cap, truncation flagging) and hints at embedding details. 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%, but description adds value with concrete examples (SEC 10-K, natural-language query examples). For text and query parameters, it provides usage context beyond schema descriptions. Limit parameter is implied ('top-N') but not explicitly restated.

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 'Semantic search INSIDE a fetched record' with specific examples (SEC 10-K, article, long tool result). It clearly distinguishes the tool's scope as searching within previously fetched content, differentiating it from tools that fetch or search externally.

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 clear when-to-use guidance: 'Use when the record is too big to cram into the prompt... saves context, returns only the passages that matter.' Also mentions pairing with ask_pipeworx_grounded. Lacks explicit when-not-to-use or alternatives, but context is strong.

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

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

The description contradicts the idempotentHint annotation: it states 'Returns the new subscription id,' implying non-idempotent creation, while idempotentHint=true suggests idempotency. This inconsistency warrants a score of 1. The description otherwise provides rich behavioral context, but the contradiction lowers the score.

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

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

The description is well-structured with clear sections and examples, but it is lengthy. It efficiently conveys necessary information without irrelevant details, earning a score of 4.

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

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

Given the tool's complexity (3 parameters, nested objects, enums, delivery channels), the description is remarkably complete. It covers all relevant aspects: prerequisites, type-specific params, delivery options with constraints, and return value. No output schema exists, but the description adequately explains the return value.

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%, providing a baseline of 3. The description adds substantial meaning by explaining each enum value for type with concrete examples and specific parameter structures, and details delivery options with constraints, significantly enhancing understanding beyond the schema.

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

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

The description clearly states the tool's purpose: 'Create a proactive monitoring subscription to a live-data event stream.' It uses a specific verb and resource, and distinguishes it from siblings like list_subscriptions and recent_alerts.

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

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

The description mentions prerequisites (Pipeworx OAuth account) and provides examples for each subscription type with parameters, implying when to use each. However, it lacks explicit 'when not to use' or direct comparisons to sibling tools beyond a brief mention of the feed channel.

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

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

Annotations indicate read-only, idempotent, open-world, non-destructive. The description adds behavioral context: returns live examples from catalog, uses no arguments for full spread, supports topic filtering. 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 somewhat long but every sentence adds value. It front-loads common user queries and structures content logically. Minor verbosity 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?

Given no output schema, the description fully explains the return structure (category-bucketed examples with tool shapes). Covers all scenarios and is complete for an onboarding tool.

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

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

Schema coverage is 100% and the description goes beyond by explaining the effect of omitting the topic parameter and listing specific allowed values. Provides practical usage meaning.

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

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

The description clearly states the tool returns category-bucketed example questions with tool+argument shapes, serving as the onboarding entry point. It distinguishes from siblings like ask_pipeworx by focusing on exploration.

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

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

Explicitly recommends using this tool first when unsure of Pipeworx capabilities, and mentions meta-tools. Provides guidance on when to call without arguments vs with a specific topic.

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 deactivation (not deletion) and that historical events remain available via recent_alerts. Annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=true) already indicate mutation is safe and idempotent; description adds valuable detail without contradiction.

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

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

Two efficient sentences: purpose first, then ownership and behavioral detail. No filler, every sentence adds value.

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

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

For a single-parameter tool with no output schema, the description covers purpose, ownership, and side effects completely, referencing relevant sibling tool (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?

Only parameter 'id' is fully described in schema (100% coverage). Description adds no additional meaning beyond stating 'by id' and referencing subscribe, which is already in 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?

Clear verb 'Cancel' and resource 'subscription by id'. Distinguishes from sibling tools like 'subscribe' and 'list_subscriptions' by specifying ownership and deactivation behavior.

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

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

Explicitly states ownership enforcement, guiding when to use: only for own subscriptions. No when-not-to-use or explicit alternatives mentioned, 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?

The description goes far beyond the readOnly/idempotent annotations by detailing behavioral traits: the two verification pipelines, the precise semantics of could_not_verify vs. unsupported (including that could_not_verify must not be treated as evidence and carries verification_error{stage,detail}), and the return contract of verdict, actual value with pipeworx:// citation, and reasoning. This adds rich context that annotations alone do not provide.

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

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

The description is a multi-sentence paragraph, but every sentence contributes value: usage trigger, routing explanation, output contract, error semantics, and performance advantage. It is front-loaded with example queries and could be slightly trimmed, but it is not verbose or padded, earning a solid 4.

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

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

With no output schema, the description compensates thoroughly by enumerating all verdict types, explaining error cases (could_not_verify vs. unsupported), describing return fields (verdict, actual value, citation, reasoning), and outlining the two routing branches. It covers both complexity and downstream interpretation, making it highly 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?

The input schema already has detailed descriptions for both parameters (tolerance_pct includes allowed range, override behavior, and default; claim includes examples). Schema description coverage is 100%, so the baseline is 3. The tool description adds no additional parameter semantics beyond the schema, so no higher score is warranted.

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

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

The description clearly states that the tool performs natural-language claim verification against authoritative sources, with specific example queries and a precise output (verdict, actual value with citation, reasoning). It distinguishes itself from generic search tools by focusing on fact-checking and highlighting that it replaces 4–6 sequential calls, making the purpose unambiguous.

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

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

It explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and explains the two routing paths (SEC EDGAR for company-financial claims, grounded pipeline for any other factual claim). However, it does not explicitly state when not to use the tool or name alternative tools for excluded scenarios, so it lacks explicit when-not or alternative guidance.

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