Fred

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds detail on probing LLMs, scoring, per-model output structure, and cost implications for Anthropic—all beyond what annotations provide.

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

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

Three sentences front-load the action and result, then add optional details. Every sentence earns its place—no redundancy or filler.

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 specifies return structure per model (score, confidence, signals, raw_response) and combined view. Covers all use cases, parameters, cost note. 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 covers all 4 parameters (100% coverage). Description adds operational context: default model, why _apiKey is needed, and how context helps disambiguate. Adds meaning beyond schema.

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

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

The description clearly states the tool probes LLMs for brand/topic visibility and scores it 0-100. It distinguishes from siblings like ask_pipeworx or bet_research by focusing on AI visibility audits for marketing, which is unique.

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?

Describes use cases (AI-marketing audits, pre-launch checks, competitive monitoring) and explains default model and optional Anthropic probe with BYO key. Does not explicitly exclude alternatives but context is clear.

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

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

Annotations already indicate readOnly, openWorld, and idempotent hints. The description adds valuable context: it routes to 3,745 tools across 884 sources, fills arguments, and returns pipeworx:// citation URIs. This goes beyond annotations, though it omits potential caveats like rate limits or failure modes.

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

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

The description is well-structured, starting with a strong preference statement, then listing query types, and ending with examples. It is relatively concise given the amount of information, though a few sentences could be tightened without losing clarity.

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

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

Given the tool's complexity (multiple aliases, no output schema, but strong annotations), the description is comprehensive. It explains the tool's role, scope, internal routing, and expected output format (structured answer with citation URIs). This fully equips an agent to invoke the tool correctly.

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

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

The input schema has 6 parameters all aliased to the same 'question' field with 100% description coverage. The description adds meaning by suggesting natural language input and providing example questions, which helps an agent understand how to use the parameter effectively.

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

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

The description clearly states the tool routes questions to a large set of authoritative sources and returns structured answers with citations. It explicitly distinguishes itself from web search and provides concrete examples, making the purpose highly specific and actionable.

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

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

The description explicitly states 'PREFER OVER WEB SEARCH' and lists numerous query types, giving clear guidance on when to use. However, it does not mention when not to use or alternatives among sibling tools like deep_research or compare_entities, which slightly reduces the 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?

Adds significant behavioral context beyond annotations: describes the internal routing, extraction method, return format including refusal reasons, and the extra LLM call cost. No contradiction with readOnlyHint, openWorldHint, idempotentHint, destructiveHint.

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

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

Description is a single paragraph that front-loads purpose and covers key details efficiently. Could benefit from bullet points for the return format, but overall concise and well-organized.

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

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

Complete for the tool's complexity: explains return structure, refusal reasons, when to use, cost trade-off, and contrasts with sibling. No output schema, but description compensates fully.

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 aliases explained. Description lists aliases and states the question is in natural language, but adds no additional semantic meaning beyond what the schema already provides. Baseline 3 is appropriate.

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

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

Clearly defines the tool as a hallucination-resistant answer mode for high-stakes reads, explains that it extracts answers only from tool results and returns explicit refusals, and distinguishes it from the sibling ask_pipeworx.

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

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

Explicitly states when to use: when answers will be quoted, cited, or acted on and facts must not be invented. Also specifies when not to use: prefer ask_pipeworx for casual lookups due to extra cost.

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 is extremely transparent about behavioral traits beyond annotations: fan-out logic, low-confidence short-circuit, closed market detection, wide spread handling, cancellation rule parsing, and resolver contract. Annotations declare readOnlyHint, openWorldHint, idempotentHint; the description does not contradict them and adds significant 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 very verbose, spanning multiple paragraphs with extensive examples and edge cases. While well-structured with headers and lists, it could be more concise. The length may overwhelm an agent and makes it harder to quickly grasp the essential function.

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 with 3 parameters, no output schema, and intricate behavior, the description is remarkably complete. It covers resolver contract, parent event extraction, news fallback fields, safety checks, and cancellation rules. An agent has all necessary context to use the tool correctly.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds some context for the 'market' parameter (accepts slug, URL, question text) and explains 'depth' and 'include_raw', but the schema already provides adequate descriptions. The value added is marginal.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data. It specifies three input types (slug, URL, question text) and explicitly lists use cases: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. This differentiates it from siblings like ask_pipeworx or polymarket_edges.

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

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

The description explicitly states when to use the tool and provides examples. It does not explicitly state when not to use it, but the context makes it clear. It also discusses edge cases like low-confidence matches and closed markets, giving the agent guidance on handling them.

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 does not contradict annotations, which already indicate read-only and idempotent behavior. It adds valuable context about off-calendar fiscal year handling, sorting by primary metric, and the return format including citation URIs. This goes beyond what annotations provide.

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

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

The description is lengthy but contains no redundancy. It front-loads with example queries and each sentence adds value, covering usage, data sources, and output format. While dense, it could be slightly more concise, but it is well-structured for an AI agent.

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

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

For a complex tool with two entity types, the description covers all key aspects: what data is returned (financial metrics for companies, counts for drugs), sorting behavior, off-calendar handling, and citation URIs. Without an output schema, this is sufficient for an agent to understand the output.

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 has 100% coverage for both parameters, so baseline is 3. The description adds extra meaning: it explains the type enum values with the specific data each pulls, and details the values array with examples (tickers/CIKs for company, names for drug) and constraints (2-5 items).

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

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

The description uses specific verbs like 'compare' and 'rank' and clearly identifies the resource: 2-5 companies or drugs. It distinguishes from siblings by explicitly stating it should be preferred over sequential single-pack lookups, which contrasts with tools like entity_profile.

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

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

The description provides explicit when-to-use guidance: when comparing entities, with example queries. It also states 'ALWAYS PREFER over sequential single-pack lookups' and explains what data each type pulls, giving clear context for choosing this tool over alternatives.

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

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

Beyond annotations (readOnly, idempotent, etc.), description adds parallel processing, structured findings with gaps, citation format, and time estimate. 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 fairly long but every sentence adds value. It is front-loaded with the main purpose and structured logically. Slightly verbose but not wasteful.

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

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

For a complex tool with no output schema, the description explains process, output format, gaps, account requirements, timing, and provides an alternative. Very thorough.

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

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

Schema already covers parameters well (100% coverage). Description adds context: depth corresponds to facet counts, and question can be broad. This is helpful but not critical.

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

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

The description clearly states it performs 'grounded multi-source research in ONE call' by decomposing questions and using 3,745 tools. It distinguishes from sibling 'ask_pipeworx' for single lookups.

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

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

Explicitly says 'Use for broad or multi-part questions' and advises using 'ask_pipeworx for single lookups'. Also mentions account requirements and depth parameter implications.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description's addition about returning tool details and schemas is helpful but not critical. 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?

Two sentences, no wasted words. Front-loaded with purpose and usage guidance, then succinctly describes output. Well-structured and efficient.

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

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

No output schema, but description explains what will be returned (names, descriptions, schemas with examples). Covers key aspects for a discovery tool without over-explaining.

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

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

Schema covers all parameters with descriptions (100% coverage). The description adds value by explaining the query alias behavior and that it accepts natural language, but baseline is 3 due to full schema coverage.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It uses specific verbs like browse, search, look up, discover, and distinguishes from sibling tools by being a meta-tool for 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?

Explicitly recommends calling it first when many tools are available and the agent needs to see the option set. It doesn't state explicit when-not scenarios, but the context of discovery vs. direct answer-seeking is implied.

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 the tool is read-only, idempotent, and non-destructive. The description adds valuable behavioral context: it fans out across multiple sources (SEC, XBRL, USPTO, news, GLEIF), notes that USPTO patents may soft-fail after May 2025, and describes the return fields. No contradiction with annotations.

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

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

The description is fairly long but every sentence adds value, listing example queries, supported sources, and return data. It front-loads example inputs, making it easy for an agent to understand usage quickly. Could be slightly more concise, but information density is high.

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

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

Given the tool's complexity (multi-source, multiple data points) and the simple schema (2 params, no output schema), the description adequately explains what outputs to expect (CIK, recent filings with URIs, fundamentals, patents, news, LEI). It also covers limitations (names not supported, USPTO sunset). This is sufficient for an agent to judge the tool's capabilities.

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

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

The input schema covers both parameters with clear descriptions: 'type' is limited to 'company', and 'value' expects a ticker or zero-padded CIK. The description reiterates this and adds that names are not supported, but this adds minimal value beyond what the schema already provides. Baseline 3 due to 100% schema coverage.

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

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

The description clearly states the tool's purpose: to provide a full cross-source profile of a US public company. It includes example user queries and details the sources and outputs. While it doesn't explicitly differentiate from siblings like 'compare_entities' or 'resolve_entity', it strongly indicates preference over chaining multiple lookups, which distinguishes its holistic approach.

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

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

The description explicitly states when to use the tool: when a user asks for a holistic view (e.g., 'tell me about X', 'research Acme'). It also provides guidance on cases where the user only has a name, advising to use 'resolve_entity' first. This ensures correct invocation and avoids misuse.

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

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

Annotations already declare destructiveHint=true and idempotentHint=true, so the description's mention of 'delete' aligns but adds little beyond the annotation. It adds context about sensitive data clearing but no new 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 two sentences: the first states the action, the second provides usage guidelines. It is front-loaded with purpose and uses no unnecessary words. Every sentence earns its place.

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

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

For a simple tool with one parameter, full schema coverage, and clear annotations (destructive, idempotent), the description covers all necessary context. It explains when to use and pairs with related tools. No output schema needed for this operation.

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 input schema fully documents the 'key' parameter. The description only says 'by key,' which repeats the schema. No additional meaning is added beyond the schema description.

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

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

The description clearly states 'Delete a previously stored memory by key,' which specifies the verb (delete), resource (memory), and the key-based access. It also distinguishes from siblings 'remember' and 'recall' by implication, as those store and retrieve respectively.

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 provides usage scenarios: 'Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' It also advises pairing with 'remember and recall,' guiding the agent on when to use this tool versus alternatives.

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

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

Annotations already mark the tool as readOnly, openWorld, idempotent, and non-destructive. The description adds context about return content (subcategories and series IDs) beyond the generic annotation hints. 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, front-loaded with the main action and examples, and contains no redundant information. Every word 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 simplicity, annotations, and output schema, the description is nearly complete. It could mention that category_id defaults to 0 (root) from the schema, but the overall context is sufficient for an agent to use the tool correctly.

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

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

The input schema has 100% description coverage for both parameters (_apiKey and category_id). The description does not add additional parameter semantics beyond what the schema provides, so 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: 'Browse economic data by category' and lists examples (housing, employment, money/banking). It specifies the return value: 'subcategories and related series IDs.' This distinguishes it from sibling tools like fred_get_series or fred_search.

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

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

The description implies usage (when exploring categories) but does not explicitly state when to use this tool versus alternatives like fred_search or fred_series_info. No 'when not to use' guidance or sibling comparisons are provided.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds context about the data source (FRED, 800k+ series) and return values (dates, values, units). No contradictions, and it adds useful behavioral context beyond the annotations.

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

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

The description is efficient, using four sentences to convey purpose, examples, usage guidance, and return type. It front-loads the key information without redundancy.

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

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

Given the tool's complexity (6 parameters, 2 required, output schema), the description adequately explains the tool's function, return values, and when to use it. It references the sibling tool for ID lookup, making the context 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 adds value by providing examples of series IDs and usage patterns, but does not explain all 6 parameters in depth. The schema already describes them adequately.

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

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

The description clearly states it retrieves authoritative historical time-series for economic indicators, with specific examples of series IDs like 'MORTGAGE30US' and 'UNRATE'. It distinguishes from sibling tool fred_search by noting that fred_search is for finding series IDs.

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

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

The description explicitly states when to use the tool ('for any macro/Fed data question') and when not to ('If you don't know the series ID, call fred_search first'), providing clear usage boundaries and an alternative.

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

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

Annotations already cover readOnlyHint, idempotentHint, and destructiveHint, indicating safe, non-destructive behavior. The description adds value by specifying the return content (release dates, names, series). No contradictions with annotations.

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

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

The description is a single sentence that front-loads the purpose and then specifies return information. No unnecessary words, and every part is meaningful. Appropriate length for the tool's simplicity.

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

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

Given the low complexity, presence of annotations, and existence of an output schema, the description is complete. It explains what the tool does and what it returns, which is sufficient for an agent to understand its use.

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

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

Input schema coverage is 100% with descriptions for all three parameters (limit, offset, _apiKey). The description does not add any additional parameter semantics beyond what the schema already provides, so a baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'Check' and the resource 'upcoming and recent economic data releases', and specifies what is returned ('release dates, names, and which series they update'). This distinguishes it from sibling tools like fred_category, fred_get_series, fred_search, and fred_series_info, which focus on categories or series details.

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

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

The description provides clear context that the tool is for checking releases, but does not explicitly state when to use it versus alternatives or when not to use it. Usage is implied by the tool name and description, but no exclusions or alternatives are mentioned.

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

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

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds that it returns series IDs, titles, and descriptions, which is helpful. However, it does not disclose pagination behavior (limit parameter), API key requirement (already in schema), or rate limits. The description adds moderate value beyond annotations.

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

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

The description is a single concise sentence that front-loads the action and purpose. No unnecessary words or redundant information.

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

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

Given the full schema coverage and an output schema (mentioned in context), the description is nearly complete. It covers the core functionality and return values. Missing mention of API key is mitigated by the required field in schema.

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

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

Schema coverage is 100%, so each parameter already has a clear description. The tool description does not add new meaning beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states the action ('Search for economic data series by keyword') and the resource ('economic data series'), and mentions return values ('series IDs, titles, and descriptions'). It distinguishes from sibling FRED tools like fred_category or fred_get_series by focusing on searching and identifying indicators.

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 does not explicitly state when to use this tool over alternatives like fred_category or fred_series_info. The phrase 'to identify the right indicator' hints at a discovery use case, but no when-not-to or alternative guidance is provided.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by specifying the metadata fields returned and the contextual hint to use it as a preliminary step, which complements 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 extremely concise—two sentences that pack the tool's purpose and usage guidance without any wasted words.

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

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

Given the tool has two parameters, full schema coverage, comprehensive annotations, and an output schema, the description is complete. It covers purpose, return fields, and a usage hint, without needing further elaboration.

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 parameters are well-documented in the schema. The description does not add additional parameter meaning beyond what the schema provides, so 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 'Get metadata for a series' and lists specific fields (title, units, frequency, etc.). It distinguishes from siblings by explicitly advising to 'check this before fetching historical data,' contrasting with fred_get_series.

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 advises checking this tool before fetching historical data, implying a recommended workflow. However, it does not explicitly state when not to use it or name alternative tools directly.

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

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

Annotations already convey read-only, idempotent, and non-destructive nature. Description adds that it fetches the page and extracts content, but does not reveal significant behavioral traits beyond what annotations provide. 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 one paragraph with a clear front-loaded purpose, followed by details and examples. It is concise but includes useful specificity; could be slightly tighter but is effective.

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

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

Given no output schema, description explains what the tool produces (standard llms.txt markdown). With rich annotations and good parameter coverage, it is sufficiently complete for an agent to understand and invoke correctly.

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

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

Schema coverage is 100% with both parameters well-described. Description adds context about output content (title, description, key links) and links parameter, but does not add new semantic depth beyond schema.

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

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

The description clearly states the tool generates a production-ready llms.txt file for a URL, specifying the output format and key use cases. It effectively distinguishes itself from sibling tools like scan_competitor_ai_presence by focusing on a specific file generation task.

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

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

Provides explicit use cases (getting client site indexed, drafting for own project, auditing competitor). Does not explicitly state when not to use or compare with siblings, but the positive guidance is clear and helpful.

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

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

Annotations already indicate readOnlyHint, destructiveHint, etc. Description adds value by detailing return fields and implying default filtering (active subscriptions) via the 'include_inactive' parameter 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?

Two sentences: first for purpose and return fields, second for usage. No waste.

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, description fully covers return fields. One optional parameter is well-documented in schema. Complete for a simple list tool.

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

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

Schema covers 100% of parameter description. Description adds minimal extra meaning, only implicitly reinforcing the default active-only behavior.

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

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

Description clearly states the tool lists the caller's active subscriptions and specifies the returned fields (id, type, params, etc.), distinguishing it from sibling tools like subscribe and unsubscribe.

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

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

Explicitly advises when to use: to review monitored subscriptions before adding more or to find an id for cancellation. While it doesn't list exclusions, it provides actionable 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 indicate non-read-only, non-destructive. Description adds behavioral context: rate-limited to 5 per identifier per day, free, doesn't count against tool-call quota. This goes beyond annotations.

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

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

The description is concise and front-loaded with purpose. Each sentence adds value: purpose, use cases, content guidelines, rate limit, cost. No wasted words.

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

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

Given the tool's simplicity (3 params, optional nested object, no output schema), the description covers all necessary information: purpose, usage, parameter semantics, limitations. Complete for an agent to use correctly.

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

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

Schema has 100% description coverage. The description adds value by explaining the enum options and how to write the message (specific, 1-2 sentences, 2000 chars max). Context object is optional and explained.

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: sending feedback to Pipeworx team. It explicitly lists types of feedback (bug, feature, data_gap, praise) and distinguishes itself from sibling tools by being the sole feedback channel.

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 detailed usage guidance: when to use each feedback type, what to include (describe issue in terms of tools/packs, avoid end-user prompt), rate limit (5 per day), and cost (free, no quota impact). No alternative needed as it's unique.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. The description adds valuable context: data source (CF analytics-engine), no PII, caching behavior. No contradictions.

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

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

The description is concise (~100 words), well-structured with bullet points, and every sentence adds 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?

The description explains what the tool returns (top tools, packs, total call volume) and mentions caching. While there is no output schema, the description is sufficient for an AI to understand the response. Missing explicit output format, but not critical.

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

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

Schema already documents the window parameter with enum and description at 100% coverage. The description adds extra semantic guidance on how to interpret short vs long windows, which helps in choosing the right parameter.

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

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

The description clearly states the verb (returns), resource (top tools, packs, total call volume), and scope (recent window). It distinguishes itself from sibling tools like discover_tools or ask_pipeworx by focusing on aggregated trending data.

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

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

Three explicit use cases are provided, giving strong guidance on when to use. However, it does not explicitly state when not to use or what the alternatives are, though the context is clear.

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

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

Adds significant behavioral context beyond annotations: explains fill checking against live CLOB depth, semantic anchor filtering, partition filter behavior, and warns when a trade should not be executed. Annotations already indicate read-only, idempotent, and non-destructive nature, so the description enriches the agent's understanding of runtime behavior.

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

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

The description is relatively long but well-structured with clear sections and front-loaded requirements. Every sentence adds value, though some parts could be slightly more concise without losing clarity.

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

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

Given no output schema, the description explains the response structure, including opportunities array, partition_check details, and fill check. It covers edge cases and provides complete context for a complex tool with multiple modes and checks.

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

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

Both parameters have descriptions in the schema, and the description adds detailed explanations, acceptable values, and examples for each. It clarifies the two modes and their purposes, fully covering parameter semantics.

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

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

The description clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks, distinguishes between event and topic modes, and provides specific examples. It differentiates from sibling tools by detailing unique features like fill checks and partition filters.

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 that exactly one of event or topic is required, and describes the use case for each mode with examples. While it doesn't list when not to use the tool, the guidance is clear and sufficient for correct invocation.

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

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

Annotations indicate readOnly, openWorld, idempotent, and non-destructive. The description adds extensive behavioral context: caching (1h), response structure with diagnostics for empty segments, and detailed model families with gates. 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 long but front-loaded with purpose and efficiently organized: model families, response format, knobs. Every sentence provides essential detail for a complex tool. While not brief, it earns its length.

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

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

Given the tool's complexity (9 parameters, no output schema), the description is remarkably complete. It explains response structure, diagnostics for empty segments, caching behavior, and the reasoning behind tradeable-edge knobs. No gaps remain for an agent to misunderstand.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining parameter context beyond schema, e.g., tradeable-edge knobs, slippage assumptions, and how min_partition_leg_kelly applies to partition opportuntiies. This elevates the score above baseline.

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

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

The description clearly states the tool's purpose: scan Polymarket markets and return opportunities where Pipeworx data disagrees with market price. It distinguishes from siblings by specifying it's built for 'what should I bet on today' and avoids paging hundreds of markets, differentiating from related tools like polymarket_arbitrage.

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

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

The description explicitly states the tool is for discovering betting opportunities without paging, which implicitly guides when to use it. It lacks direct comparison to siblings or exclusions, but the context is clear enough for an agent to decide appropriately.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant beyond: history depth bounded by 60-day TTL and snapshot start date, decay computed from daily closes (not intraday), and snapshot gaps meaning no scan that day. 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 relatively long but well-structured: purpose first, then parameter explanation, then response format, then limits. Each part adds value, but some sentences could be shorter without losing meaning.

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 the response structure (tracked[], expired[], snapshot_dates[]) with field explanations, making it complete for an agent to understand 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?

Schema provides 100% coverage with descriptions for both parameters. The description adds context: days are lookback with default 14 and max 30 (consistent with schema's clamp 2-30), window is snapshot family with default '1wk'. This slightly enriches but schema already describes them.

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

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

The description clearly states the tool provides 'edge persistence and decay telemetry' from daily snapshots, answering a specific question about edge duration and trend. It distinguishes from sibling tool 'polymarket_edges' which likely provides current edges, making the purpose unambiguous.

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

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

The description implies usage context by contrasting 'fresh wide edge' vs '3-week-old wide edge' and the question 'how long has this edge existed and is it shrinking?'. However, it does not explicitly state when not to use or name alternatives, though the sibling list includes polymarket_edges as a likely alternative.

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

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

Annotations already indicate readOnlyHint, idempotentHint, openWorldHint, and not destructive. The description adds rich behavioral context: it requires one of 'market' or 'event', explains two modes, lists specific return fields (e.g., top_of_book, vwap_fill_price, slippage_pp, verdict), and warns about forced directional risk and thin books. 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 relatively long (~250 words) but well-structured with capitalized sections (REQUIRES, SINGLE-MARKET, BASKET) and a final warning. Every sentence adds value, though some could be more concise. It is front-loaded with the core purpose.

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

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

Given the complexity (two modes, no output schema, 4 parameters), the description is remarkably complete. It explains return fields for both modes, includes warnings about thin books and partial fills, and provides usage context. Without an output schema, the description compensates by detailing all return values.

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

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

Schema coverage is 100%, but the description adds significant meaning: explains that 'side' defaults to 'auto' in basket mode, 'size_usd' has different interpretations per mode (spend vs target proceeds vs settlement notional), and provides a clamp range. This goes well beyond schema descriptions.

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

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

The description clearly states it checks 'realizable-vs-theoretical edge against live CLOB order-book depth', distinguishing between single-market and basket modes. It differentiates itself from siblings like polymarket_arbitrage and polymarket_edges by explicitly recommending usage before acting on those tools' signals.

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

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

The description provides explicit guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It also warns about partial basket fills converting arb into unhedged directional positions, giving clear when-to-use and when-not-to-use context.

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

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

Annotations already declare readOnlyHint true, idempotentHint true, destructiveHint false. Description adds rich behavioral details: two modes, compatibility warnings, temporal alignment, cross-type/subtype skipping, and leg-level pricing. No contradiction.

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

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

Description is comprehensive but somewhat lengthy; however, every sentence carries useful information. Slightly less concise than ideal but acceptable given 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?

Fully explains return structure (leg prices, spread array, top spreads), safety fields, and edge cases (compatibility warning scenarios, temporal alignment). No output schema needed due to thorough textual 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?

100% schema coverage, description adds value: lists exact topic values, explains override behavior, and contextualizes each parameter with usage examples.

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 computes cross-venue spreads between Kalshi and Polymarket for the same resolving question, distinguishing from siblings like 'polymarket_arbitrage' by specifying it operates across two venues.

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 (finding spreads) and when-not-to-use (compatibility warnings for non-equivalent or misaligned events). Describes two modes and safety fields.

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 scoping detail 'to your identifier' and pairing with remember/forget, but does not contradict annotations. No additional behavioral traits disclosed.

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, each earning its place: first describes action, second provides usage motivation, third explains scope. Front-loaded with main functionality. No redundancy.

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

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

Given one optional parameter and no output schema, description covers all needed information: what it does, when to use, scoping, and relationship to siblings. Complete for this straightforward 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?

Single optional parameter 'key' fully described in schema. Description adds value by explaining behavior without the key: 'omit to list all keys'. This goes beyond schema's description of the parameter.

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 'Retrieve a value previously saved via remember, or list all saved keys', with specific verb 'retrieve' and resource 'value via remember'. Distinguishes from siblings remember and forget in description.

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 context: 'Use to look up context the agent stored earlier... without re-deriving it from scratch'. Does not explicitly state when not to use, but context implies proper usage. Sibling tools like remember and forget are implicitly contrasted.

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 the tool is read-only, idempotent, and non-destructive. The description adds significant behavioral context: it explains the side effect of mark_read (flags events read so next call shows only newer ones), the return fields, and that the feed persists. This complements annotations effectively 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 a single, efficient paragraph of 4 sentences, front-loaded with the main purpose. It includes all necessary details without redundancy. A slightly more structured format could improve readability, but it is concise and 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?

With no output schema, the description adequately explains the return fields (source, citation_uri, raw payload) and covers filtering, side effects, and polling suitability. It also provides a fallback access method, making the tool's context complete for a retrieval task.

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

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

Schema coverage is 100% with descriptions. The description adds value by explaining usage of parameters filter by type with an example, since as ISO timestamp, and mark_read effect. It does not repeat schema but enhances 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 pulls fired events from the user's subscription feed, specifying the verb 'Pull' and resource 'alerts from subscription feed'. It distinguishes itself by detailing the return fields (source, citation_uri, raw payload) and filtering options, setting it apart from sibling tools that serve different purposes.

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

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

The description explains the tool is suitable for polling ('Polls work fine') and provides an alternative access method (the same feed at a URL for scripts/dashboards). However, it does not explicitly compare to sibling tools or state when not to use this tool, leaving a minor gap in 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?

Beyond annotations (readOnlyHint, etc.), the description elaborates on the tool's behavior: it fans out to multiple sources in parallel, specifies fallback logic for news, notes the USPTO PatentsView API sunset, and describes the return structure (changes grouped by source, total_changes, citation URIs). No contradictions with annotations.

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

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

The description is a single paragraph but packs extensive information efficiently. It front-loads with user-facing example queries, and every sentence adds meaningful context without redundancy. Ideal length for clarity and completeness.

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

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

For a complex tool with multiple data sources, fallback, and no output schema, the description adequately covers return structure (changes grouped by source, total_changes, citation URIs), limitations (USPTO soft-fail), and parameter usage. It leaves no critical gap 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?

Although schema coverage is 100%, the description adds valuable context: 'since' accepts ISO dates or relative shorthand with examples ('7d', '3m'), 'value' can be ticker or CIK, and 'type' is restricted to 'company'. This enriches 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 returns a change feed for a company, covering SEC EDGAR, news (GDELT/GNews), and USPTO patents. It provides example queries like 'What's new with X' and distinguishes itself from the sibling tool 'entity_profile' by specifying when to use the latter.

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

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

The description explicitly advises when to use entity_profile instead (for static profile), explains the fallback mechanism from GDELT to GNews, and gives examples for the 'since' parameter with recommendations like '30d' or '1m' for typical monitoring.

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

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

Annotations indicate idempotentHint=true and destructiveHint=false. The description adds context about scoping and retention (24h for anonymous, persistent for authenticated). It does not contradict annotations but doesn't elaborate on overwrite behavior (implicitly idempotent).

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 (three sentences) and front-loaded with the primary purpose. Every sentence adds value without redundancy.

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

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

For a simple memory store with two parameters and no output schema, the description covers usage context, key-value pair structure, scoping, persistence, and companion tools. No gaps for intended use.

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

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

Schema coverage is 100% with examples. The description adds semantic value by explaining the key naming pattern (e.g., 'subject_property', 'target_ticker') and value content ('findings, addresses, preferences, notes'), aiding correct parameter usage.

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

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

The description clearly states the tool's purpose: 'Save data the agent will need to reuse later'. It specifies the resource (key-value memory) and action (save), and distinguishes it from siblings like recall and forget by mentioning pairing.

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

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

Explicitly states when to use: 'when you discover something worth carrying forward'. It also describes scope (by identifier) and persistence differences (authenticated vs anonymous), and mentions companion tools recall and forget.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds that each call cascades through several lookup endpoints internally, which provides useful insight beyond annotations without contradiction.

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

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

The description is well-structured with examples, purpose, and details. It is slightly long but every sentence adds value. No wasted words.

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

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

Despite having no output schema, the description fully explains return values for each type (ticker, CIK, company_name, citation URI for company; RxCUI, ingredient, brand, citation for drug). It also mentions auto-disambiguation, making it complete for the tool's complexity.

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

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

Schema coverage is 100%, and the description adds significant detail: for 'company' it explains accepted inputs (ticker, CIK, name) and for 'drug' it gives examples like 'ozempic'. This goes well beyond the schema's brief 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 explicitly states the tool resolves names to canonical identifiers, provides concrete examples of user queries ('What's the ticker for...'), and lists supported entity types. It is distinct from all sibling tools, which perform other functions.

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

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

The description says 'Use FIRST whenever you have a name but need an ID,' which gives clear when-to-use guidance. It also notes the tool replaces multiple manual lookups, but does not explicitly list alternatives or 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 already declare the tool as read-only, idempotent, and non-destructive. The description adds value by explaining the probing process with ai_visibility_check, ranking, and output details (score, confidence, signal density). 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 concise (3 sentences), front-loads the purpose, and each sentence adds value. No wasted words.

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

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

The description explains the output format (ranked list with score, confidence, signal density) and mentions the use of ai_visibility_check, compensating for the lack of an output schema. Some constraints are in the schema; overall adequate.

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

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

Schema coverage is 100%, so baseline is 3. The description does not provide additional meaning beyond what is already in the parameter descriptions, which are adequate.

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 compares AI visibility across multiple entities side-by-side, using a specific verb and resource. It distinguishes itself from sibling tools like ai_visibility_check by describing the comparative and ranking functionality.

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

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

The description provides a clear use case (competitive AI-marketing audits) and implies the tool is for comparing brands. It mentions the underlying probe tool, but does not explicitly contrast with all sibling tools like compare_entities.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds valuable behavioral details: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30 seconds, sources_failed lists timeouts, and the rest still returns. No contradictions with annotations.

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

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

The description is a single dense paragraph but front-loads the key purpose and provides comprehensive details. It could be slightly more structured (e.g., bullet points), but it is concise without unnecessary filler, earning its place.

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

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

Despite no output schema, the description enumerates the exact fields returned in the summary block and mentions per-advisory detail, links, and alternative version list. It covers limitations (NPM only, partial failure behavior), making the tool's behavior fully understandable.

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

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

Schema coverage is 100%, so baseline is 3. The description adds context by stating that 'package' is an npm package name (with scoped packages accepted) and that 'version' defaults to latest when omitted. This frames the parameters within the composite check, adding slight 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 clearly states the tool performs a composite check for adding an npm package, combining deps.dev and bundlephobia data. It distinguishes itself by specifying the exact data sources and the scope (npm ecosystem in v1), setting it apart from any sibling tools.

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

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

The description explicitly states when to use: when an agent asks about safety, popularity, size, or cost of adding a package. It also clarifies when not to use: for non-npm ecosystems (PyPI, Maven, etc.), which should be handled by deps.dev:version directly. This provides clear guidance on alternatives.

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

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

Annotations declare readOnly, openWorld, idempotent, non-destructive. Description adds significant context: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag, and that every passage carries offsets for verification. 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?

Single paragraph, front-loaded with purpose, then usage, then technical details. Every sentence is informative and necessary. No redundancy or fluff.

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

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

No output schema, but description explains return type: top-N passages with character offsets and similarity scores. Also covers cap/truncation. Lacks exact JSON structure, but sufficient for agent to understand the result. Pairs with sibling for grounding completes the context.

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

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

Schema coverage is 100% with clear param descriptions. Description adds minimal value beyond schema: provides query examples but doesn't add new constraints. Baseline 3 is appropriate as the schema already does the heavy lifting.

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

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

The description clearly states it performs semantic search inside a fetched record, using specific examples like SEC 10-K body or article. It distinguishes from siblings by emphasizing it operates on already-pulled text, not external search. The verb+resource pattern is explicit.

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

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

Explicitly states when to use: when the record is too big for the prompt. Also mentions pairing with ask_pipeworx_grounded for grounding. Missing explicit when-not-to-use guidance, but the positive use case is very clear and actionable.

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 details beyond annotations, such as delivery channel limits (10 SMS/day), phone verification requirement, and webhook auto-disabling after 10 failures. However, it contradicts the idempotentHint=true annotation by stating 'Returns the new subscription id,' implying each call creates a new subscription rather than being idempotent. This reduces trust in behavioral clarity.

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 return, then systematically covers types, delivery channels, and constraints. While somewhat long, every sentence adds value and no content is redundant. A slight structural improvement could be to separate type and delivery into distinct paragraphs, but overall it is 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 (3 parameters, nested objects, no output schema), the description covers all essential aspects: return value, auth requirement, type-specific params, delivery options with limits and verification, webhook signing, and failure handling. It is sufficiently complete for an agent to invoke the tool correctly without needing external references.

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

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

The description provides extensive context beyond the schema: for each 'type' enum it gives real-world examples (e.g., sec_8k items codes); for 'params' it details type-specific filters; for 'delivery' it explains email pattern, phone verification, webhook signing and auto-disable. With 100% schema coverage, the description adds substantial meaning, making parameters self-explanatory.

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

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

The description clearly states the tool creates a proactive monitoring subscription to a live-data event stream and returns a subscription ID. It distinguishes itself from siblings like 'unsubscribe' and 'list_subscriptions' by focusing on creation vs reading/removing. Supported types are enumerated with examples, making the purpose unambiguous.

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

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

The description specifies when to use it (for live-data subscriptions) and prerequisites (Pipeworx OAuth account, not anonymous). However, it does not explicitly advise against using it for other purposes or point to alternatives like 'recent_alerts' for reading events once subscribed. The guidance is adequate but lacks explicit exclusion of alternatives.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds context: the tool returns example questions with tool+argument shapes, no side effects, and is a safe learning tool. No contradiction.

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

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

The description is verbose but front-loads the purpose and includes necessary detail. Every sentence adds value, though it could be slightly more concise without losing meaning.

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

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

No output schema, but the description explains the return format (category-bucketed example questions with tool+argument shapes). Covers usage, parameters, and the intended context of first-time use. 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% with a description for the `topic` parameter. The description reinforces the parameter's purpose and lists valid focus areas, adding some context beyond the schema.

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

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

The description clearly states it's the onboarding entry point, returns category-bucketed example questions, and explains the behavior with specific verbs like 'returns' and 'drawn from the live catalog'. It distinguishes from sibling tools by focusing on onboarding and learning what Pipeworx can do.

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

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

Explicitly advises using this tool first when unfamiliar with Pipeworx or to learn meta-tools. Provides guidance on when to call with no arguments vs. with a topic, and implies it's a precursor to other tools like ask_pipeworx.

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

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

The description explains that the row is deactivated, not deleted, and that historical events remain available via recent_alerts. This adds value beyond annotations, which include idempotentHint and 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?

Two sentences, each informative and without fluff. Front-loaded with action, followed by behavioral details.

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

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

For a simple tool with one parameter and no output schema, the description covers action, ownership constraint, and the deactivation behavior adequately.

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

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

Schema coverage is 100% with a clear description for 'id'. The description reinforces 'by id' but does not add new meaning beyond the schema.

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

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

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

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

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

It explicitly mentions ownership enforcement ('you can only cancel your own subscriptions'), indicating when to use and when not. It does not explicitly name alternatives but the context with siblings is clear.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds behavioral context by listing the return values (verdict, extracted form, actual value, citation, percent delta) and noting the tool's coverage (v1 supports company-financial claims). This goes beyond annotations without contradiction.

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

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

The description is fairly long but well-structured with examples, scope, and output details. It packs useful information without redundancy. It could be slightly more concise, but every sentence adds value.

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

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

For a tool with one parameter and no output schema, the description fully explains the return types, underlying sources (SEC EDGAR + XBRL), and efficiency benefit (replaces 4-6 sequential calls). It provides sufficient context for correct invocation and 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?

The single parameter 'claim' is fully described in the input schema (100% coverage). The tool description does not add any additional meaning beyond what the schema already provides, so baseline 3 is appropriate.

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

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

The description clearly defines the tool as natural-language claim verification against authoritative sources, with specific examples ('Is it true that…', 'fact check'). It distinguishes from siblings by stating it replaces 4-6 sequential calls and focuses on company-financial claims. This provides a specific verb and resource with clear differentiation.

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

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

The description states 'Use whenever the agent needs to check whether something a user said is factually correct' and narrows the scope to company-financial claims via SEC EDGAR + XBRL. While it doesn't explicitly exclude other uses or mention alternatives, the context is clear enough for an agent to decide when to invoke it.

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